Tool Comparisons

Claude Opus 5 wins overall, but not because 61 beats 51. It wins because it also reaches 51 — at low effort, for $0.36 per task against Muse Spark 1.1's $0.26 — while keeping four higher rungs that Meta's model cannot reach at any price, and because it can be used outside the United States, which Muse Spark 1.1 currently cannot. The concession is real: at equal measured intelligence Muse Spark 1.1 is about 28 percent cheaper per task, starts responding roughly 48 times sooner and sustains about 2.4 times the throughput. A fixed, validated, US-based workload at index 51 should run on Muse Spark 1.1 and would be overpaying on Opus 5. A workload that might need more than 51, or that serves users outside the US, has exactly one option of the two.

No overall winner, because the measurements refuse to produce one — and the tie is not where the headline suggests. On Artificial Analysis Intelligence Index v4.1, Claude Opus 5 scores 61 at max effort and GLM-5.2 scores 51 at max effort, a ten-point gap. But the two models meet exactly once, at 51: Opus 5 reaches it at low effort, the bottom of a five-rung ladder, for $0.36 per task, while GLM-5.2 reaches it at max effort, which is both its default and its ceiling, for $0.32 per task. GLM-5.2 therefore wins the tie by about 11 percent and Anthropic's cursor never descends low enough to take that point back. What Opus 5 has instead is headroom: four further rungs ending at 61, at 5.6 times the tie price, against a wall on the GLM-5.2 side that no budget moves. GLM-5.2 also wins on token price ($1.40 and $4.40 per million against $5 and $25), on blended price ($0.90 against $3.85 per million), on throughput and latency, and on 753B parameters of weights published under an unmodified MIT license we verified by reading the file. The decision is a capability threshold, not a budget: run your hardest work against GLM-5.2 at max effort, and if it completes, the ten points are money with no job to do.

Claude Opus 5 wins on capability, headroom, long-prompt cost predictability and time to first token; GPT-5.6 Luna wins decisively on price and ties the one score they share. Both reach 51 on Artificial Analysis Intelligence Index v4.1, but Luna gets there for $0.28 per task against Opus 5's $0.36 — roughly 22 percent cheaper, both measured July 28, 2026, before OpenAI's July 30 price cut — and that 51 is Luna's absolute ceiling while it is Opus 5's floor, with 56, 59, 60 and 61 stacked above it. Since OpenAI cut the GPT-5.6 rates by a factor of five on July 30, 2026, Luna's list price sits 25 times below Opus 5's on input and about 20.8 times below on output, so the price gap is far wider than the July 28 cost-per-task figures suggest. Luna also reprices an entire request at double input and 1.5 times output above 272,000 prompt tokens, where Opus 5 bills its full 1M-token window flat. Pick Luna if the workload genuinely tops out at 51 and prompts stay short; pick Opus 5 if the ceiling is variable, prompts run long, or first-token latency is the felt experience.

Claude Opus 5 wins, and it wins in an unusual way: Gemini 3.5 Flash's best measured configuration scores 50 at $0.59 per task, one point below Opus 5's cheapest configuration at 51 for $0.36, which makes Google's model strictly dominated on intelligence per unit of task cost. Opus 5 also holds four measured rungs above Flash's ceiling and a knowledge cutoff sixteen months fresher. The concessions are real and matter: Gemini 3.5 Flash moves tokens about three and a half times faster, lists at roughly a third of Opus 5's token price, and accepts audio and video that Opus 5 cannot read at any price. For input-heavy, latency-sensitive or multimodal work, Flash remains the correct choice and the index gap is close to irrelevant.

Claude Opus 5 wins, on capability and on dependability. It scores 61 on Artificial Analysis Intelligence Index v4.1 at max effort against 46 for Gemini 3.1 Pro Preview, and its cheapest configuration still scores 51, five points above the only figure published for Google’s model. Gemini’s case is real and this page does not weaken it: it costs $0.29 per task against $0.36, streams about 129 output tokens per second against 55, beats Opus 5 on factual reliability by 33 to 31 on AA-Omniscience, and reads audio and video that Opus 5 cannot accept. What decides it is the label. Gemini 3.1 Pro Preview has been a "Public preview" offering for 159 days with no announced general availability date; Google’s Service Specific Terms exclude Pre-GA offerings from any SLA or indemnity and allow discontinuation without prior notice, and Google retired this model’s direct predecessor after 111 days. Google also charges a long-context surcharge that reprices every token in a request, output included, above 200,000 input tokens, while Anthropic bills its full one-million-token window flat. Teams that can absorb a short-notice migration and fit under index 46 should take Gemini and the savings; teams that need an SLA on the model serving production traffic have no Google option in this tier today.

Claude Opus 5 wins on capability and it is not close: 61 against 44 on version 4.1 of the Artificial Analysis Intelligence Index, among the widest gaps in this comparison series, plus a stated May 2026 cutoff and flat pricing across its full million-token window. MiniMax M3 wins everything the index does not measure — about seventeen times cheaper per task at USD 0.12 against USD 2.03, a first token in 1.64 seconds against 78.91, four times the single-call output ceiling, video input, and downloadable ungated weights. Unlike earlier comparisons in this series, no Claude Opus 5 effort level undercuts M3 on cost; even its cheapest rung costs three times more while scoring 51 against 44. One caveat that changes the open-weight case: M3's LICENSE file is the MINIMAX COMMUNITY LICENSE, not MIT — its grant covers non-commercial purposes, commercial use requires displaying “Built with MiniMax M3”, and products above USD 20 million in yearly revenue need prior written authorization.

Claude Opus 5 wins overall, but conditionally. It leads DeepSeek V4 Pro by 17 index points at matched max effort (61 to 44) and by 41 points on AA-Omniscience (31 to -10), where DeepSeek V4 Pro's negative score means it asserts more wrong answers than correct ones. DeepSeek V4 wins decisively on cost, charging about 29 times less per output token, and wins outright on licensing with plain MIT weights verified at file level. Reasoning effort never changes the per-token rate, so no Opus 5 setting reaches DeepSeek's cost band. Pick Opus 5 when a wrong answer is expensive to detect; pick DeepSeek V4 when outputs are cheaply verifiable, volume dominates, or weights must run on your own hardware.

Claude Opus 5 wins on measured capability, scoring 61 on Artificial Analysis Intelligence Index v4.1 at max effort against 40 for Qwen3.6 Plus, a 21-point gap that narrows to 19 points at Opus 5 default high effort. Qwen 3.6 wins on price and on openness: Qwen3.6 Plus costs 10 times less per input token below 256,000 tokens, and the family publishes Qwen3.6-27B and Qwen3.6-35B-A3B under an unmodified Apache 2.0 license. The decisive detail is that Alibaba tiers its pricing above 256,000 tokens while Anthropic does not, so the input price ratio collapses from 10 times to 2.5 times on long-context work.

Claude Opus 5 wins on measured capability per unit of cost, and the margin is wider than the price sheets suggest. At its default high effort it scores 59 on Artificial Analysis Intelligence Index v4.1 against Claude Sonnet 5’s 53 at maximum effort, and costs USD 1.06 per task against USD 1.53 — six points higher and 31 percent cheaper on the same benchmark suite, on figures measured July 28, 2026. The mechanism is verbosity: Sonnet 5 bills at 40 percent of Opus 5 per token, yet cost more to run the full index (USD 4,010.12 against USD 3,835.51) because it emitted 300 million output tokens where Opus 5 emitted 100 million. Sonnet 5 keeps a real and defensible win on raw per-token price for input-heavy work such as retrieval, classification and extraction, where output stays short and the token multiplier never fires. That win is permanent: Anthropic cancelled the September 1, 2026 increase to USD 3 and USD 15 on August 10, 2026, so the gap stays at 2.5 times.

No overall winner, and the split is clean rather than mushy. Claude Opus 5 wins raw capability (61 against 54 at each ceiling, 59 against 54 at each default), context (1M against 500k with no long-context surcharge), recency (May 2026 against February 1, 2026) and output ceiling. Grok 4.5 wins cost per task ($0.35 against $2.03 at max, a 5.8x gap), interactive latency (8.79s to first chunk against 67.72s) and native X access via its documented x_search tool. The decisive finding is that Grok 4.5 beats Opus 5 outright at the bottom of Anthropic range: 54 points for $0.35 against 51 points for $0.36. Grok 4.5 replaces Opus 5 low-effort tier rather than merely undercutting it, but it cannot reach the top, because high is its maximum setting. Route between them: default to Grok 4.5 on volume, escalate to Opus 5 at high or max when output quality is the product.

No overall winner, because on these two models the effort level decides the outcome more than the model does. Claude Opus 5 scores higher than GPT-5.6 Terra at every matched effort level, by 6 to 11 points on the Artificial Analysis Intelligence Index, and Terra costs less per task at every matched level. The decisive figure is the crossover: Claude Opus 5 at medium effort scores 56 for $0.62 per task, beating Terra at its maximum setting (55 for $0.82) on both score and cost. Opus 5 spans 51 to 61 points across its five effort levels and Terra spans 40 to 55, so the two ranges overlap and the dial matters more than the badge. Choose Opus 5 above roughly $0.60 per task, and Terra below roughly $0.35, where Opus 5 cannot follow at a comparable score.

Claude Opus 5 wins at identical pricing: 61 against 56 on the Artificial Analysis Intelligence Index v4.1 at matched max effort (measured July 27, 2026), a May 2026 knowledge cutoff against January 2026, a 512-token caching floor against 1,024, its own rate-limit bucket, and a clean sweep of Anthropic's own head-to-head benchmarks. Because the price is identical, migration is recommended for most workloads — but it is not a one-line model-ID swap. Audit four things first: thinking is on by default and now shares your max_tokens budget with the answer; disabling thinking at xhigh or max effort returns a 400 error where Opus 4.8 accepted it; the effort ladder was recalibrated so carried-over settings are no longer tuned; and inherited verification instructions cause over-verification. Two reasons to stay are legitimate rather than sentimental: a pipeline that disables thinking at xhigh or max effort, and any workload where a confident wrong answer costs more than a missing capability — Anthropic's own system card documents that Opus 5 hallucinates more than Opus 4.8 and states uncertain answers confidently more often. Note that migrating does not remove Opus 4.8 from your stack: it is the designated fallback model when Opus 5's cybersecurity classifiers decline a request.

There is no overall winner here, and that is a conclusion rather than a hedge. On version 4.1 of the independent Artificial Analysis Intelligence Index, Claude Opus 5 scores 61 at max effort against Kimi K3’s 57 — four points on the rounded figures, 3.58 on the raw ones — while Kimi K3 costs a measured USD 0.72 per index task against USD 2.03, about 2.8 times less. Read that as a threshold rather than an average: if the hardest task in your workload sits above what a model scoring 57 can finish, the four points are the entire reason Opus 5 exists; if it sits below, and for most workloads it does, you are paying 2.8 times for headroom you never touch. The multiplier is also a property of a dial rather than of the two models — at Opus 5’s default high effort the comparison is 59 against 57 at about 1.5 times the cost, and at medium effort Opus 5 is cheaper than Kimi K3 while scoring one point lower. Opus 5 takes the capability ceiling, the stated May 2026 cutoff and long-prompt speed; Kimi K3 takes the cost, the responsiveness at very long context, and openness that is now delivered rather than promised: Moonshot published the weights on July 27, 2026, ungated, though under a custom Kimi K3 License with revenue-triggered commercial conditions rather than the Modified MIT widely reported. On the one independent bench covering both, hallucination rates are effectively tied at 50.07 and 50.94 percent, so Opus 5’s advantage there is accuracy, not fewer fabrications.

Claude Opus 5 is the stronger model and GPT-5.6 Sol is the better buy for a large share of teams, and both halves of that sentence are needed. Opus 5 leads version 4.1 of the independent Artificial Analysis Intelligence Index by two points at max effort, 61 against 59, and holds a two-to-three point lead at every matched effort rung. It also carries a May 2026 knowledge cutoff against February 16, 2026, reaches first token in roughly half the time, prices its full million-token window with no long-context surcharge where Sol doubles input billing above 272,000 tokens, and leads every agentic evaluation Anthropic published. But the cheaper token does not produce the cheaper invoice: Opus 5 charges 17 percent less per output token and costs about 32 percent more per completed task, USD 2.03 against USD 1.54 on the Artificial Analysis index and USD 2.06 against USD 1.44 on ARC-AGI-2, because it generated roughly 100 million tokens on the index run against a 63 million peer median. Sol also wins ARC-AGI-2 outright at matched max effort, 92.5 percent against 90.4, on the one benchmark neither vendor built, ran or scored. We give the verdict to Opus 5 on the consistency of its capability lead and its long-context pricing, with the explicit caveat that buyers who budget by finished task rather than by token should choose Sol.

Claude Opus 5 is the better default, and the reason is the price rather than the point. It is narrowly ahead of Fable 5 on version 4.1 of the independent Artificial Analysis Intelligence Index — 61 against 60, with both models measured at max effort; Opus 5 scores 59 at the high setting the API uses by default, a level at which Artificial Analysis publishes no Fable 5 figure to compare against. Both figures are contaminated by the same Opus 4.8 fallback on classifier refusals, in proportions Anthropic disclosed for a different evaluation and never for this index, so a one-point gap published without a confidence interval cannot carry a procurement decision. The exact two-to-one price ratio can, and it comes with a fresher May 2026 cutoff, four times the rate-limit allowance, and zero-data-retention eligibility Fable 5 structurally cannot offer. Fable 5 keeps a genuine claim on two things: it wins the Cognition-scored FrontierCode set 53.5 to 53.4, and it is stronger on long-horizon work — a gap Anthropic's own spokesperson conceded its evaluation suite does not measure.

Split verdict by workload, with the verifiable capability lead going to Claude Opus 4.8 and the cost-and-speed lead going to Grok 4.3. On the two benchmarks scored the same way for both models, Opus 4.8 leads: on the Artificial Analysis Intelligence Index it sits in the top five as of August 4, 2026 (55.7, about 18 points above Grok 4.3), while Grok 4.3 ranks in the upper-mid tier, and Opus 4.8 also leads the GDPval-AA agentic benchmark. Opus 4.8 also posts an Anthropic-reported 88.6 percent on SWE-bench Verified, with no comparable Grok 4.3 figure. In our hands-on coding it was also the more reliable model on long, complex work. Grok 4.3 is roughly ten times cheaper on output tokens ($2.50 versus $25 per million, fetch-verified), runs much faster — well over twice the output speed on the same Artificial Analysis test — and adds real-time access to data from X plus an OpenAI-compatible API that Opus 4.8 does not match. Context window is a tie at one million tokens on both vendors' published specs. We did not crown a single overall winner: the models target different buyers. Best for agentic coding, reasoning, and computer use: Claude Opus 4.8. Best for cost, output speed, real-time X data, and drop-in integration: Grok 4.3. Pricing is fetch-verified from vendor docs; like-for-like benchmarks are from Artificial Analysis; the computer-use and SWE-bench figures are single-vendor Anthropic claims.

Split verdict by use case, tilted toward Claude Opus 4.8 on capability and toward DeepSeek V4 on cost and openness. On the one independent evaluator that scores both the same way, Artificial Analysis, Opus 4.8 leads the Intelligence Index at 56 versus 44 for DeepSeek V4-Pro as of June 2026 — second only to Anthropic's own Claude Fable 5 among ranked models — and it leads the comparable SWE-bench Verified figure 88.6% to 80.6%. It is the stronger, more reliable model in our hands-on use and the only one clearing Western data-residency requirements. DeepSeek V4 costs roughly an order of magnitude less per token on its hosted API (V4-Flash output $0.28 versus $25 per million), ships MIT-licensed open weights you can self-host, and lands within striking distance of the frontier. No single overall winner: the two models target different buyers. Best for raw capability, agentic coding, and US-hosted compliance: Claude Opus 4.8. Best for cost, open weights, and self-hosting: DeepSeek V4. All benchmark numbers are vendor-reported or from the Artificial Analysis index; only pricing is fetch-verified.

Split verdict by use case. Claude Opus 4.8 wins top-end reasoning, agentic reliability, and turnkey US-hosted compliance — it ranks in the top tier of the Artificial Analysis Intelligence Index, well ahead of every Qwen 3.6 tier on that composite. Qwen 3.6 wins cost and openness — its hosted Plus tier at 1.95 dollars output per million tokens is roughly 13 times cheaper than Opus 4.8, and its Apache 2.0 27B and 35B-A3B open weights are self-hostable with no revenue threshold while still posting 77.2 percent SWE-bench Verified. Pick Opus 4.8 for the strongest top-end model or Western compliance; pick Qwen 3.6 for cost, open weights, or self-hosting.

Split verdict by category, no single overall winner. On verified pricing, Mistral Large 3 wins outright — $0.50 input and $1.50 output per million tokens versus Claude Opus 4.8's $5 and $25, roughly eight to ten times cheaper — and it is the only model here with fully open weights (Apache 2.0, self-hostable) and EU data sovereignty. On the one composite both models are scored on by the same evaluator, the Artificial Analysis Intelligence Index, Opus 4.8 ranks far ahead of Mistral Large 3, though Opus runs that test in adaptive max-effort reasoning mode. Claude Opus 4.8 also wins the larger declared context window (1,000,000 versus 262,144 tokens, both verified). Latency flips the other way: Mistral Large 3's time to first token is 1.09 seconds against Opus 4.8's 25.91 seconds in reasoning mode. Best for top-end capability, reasoning, and the largest context: Claude Opus 4.8. Best for cost, open weights, low latency, and European sovereignty: Mistral Large 3. Pricing and context windows are fetch-verified; the Intelligence Index is same-evaluator; other benchmarks are vendor-reported.

Split decision, no single overall winner. GPT-5.5 wins agentic coding on the one like-for-like benchmark both vendors report (SWE-bench Verified, roughly 88.7% versus 80.6%), ships the deeper agentic tool stack, has a fresher December 2025 knowledge cutoff, and is generally available today — but that 88.7% is press-relayed, not independently verified. Gemini 3.1 Pro wins on the dimensions we could confirm with hard evidence: vendor-verified pricing roughly 2.5 times cheaper ($2 versus $5 per million input), a confirmed 1,000,000-token multimodal context window across text, image, video, audio, and PDF, the highest published GPQA Diamond reasoning score (94.3%), and the widest distribution. Reasoning is a draw — Gemini edges GPQA Diamond, GPT-5.5 edges ARC-AGI-2. Best for agentic coding on the OpenAI stack: GPT-5.5. Best for price, multimodal input, and Google Cloud teams: Gemini 3.1 Pro.

There is no overall winner: Claude Opus 4.8 and Claude Sonnet 4.6 are two tiers of the same family, built to be used together. Opus 4.8 is the stronger model — better agentic coding, deeper reasoning, cleaner self-verification, plus Fast Mode and Dynamic Workflows that Sonnet lacks — at $5 per million input tokens and $25 per million output tokens. Sonnet 4.6 is the better value at $3 input and $15 output, roughly 40 percent cheaper, with near-flagship quality on most work and its own 1M context window. Pick Opus 4.8 for the hardest agentic and reasoning work where reliability is worth the premium; pick Sonnet 4.6 for high-volume, latency-sensitive, and cost-sensitive production. If you build at scale, run both and route by task. We will not hand you a fake winner — the right Claude depends on the job, and the cost gap is exactly why the choice exists.

Split decision. Gemini 3.1 Pro wins frontier reasoning (94.3% GPQA Diamond vs 90.1%), native multimodal input across text, image, video, audio, and PDF, and a polished managed Google Cloud ecosystem. DeepSeek V4 wins on price (V4-Flash at $0.14 input and $0.28 output per million tokens, roughly an order of magnitude cheaper), MIT-licensed open weights you can download and self-host, a 384K output ceiling six times Gemini's 64K, and strong published competitive-coding scores (a 3206 Codeforces rating). Real-world coding is a tie at 80.6% SWE-bench Verified. Best for capability and convenience: Gemini 3.1 Pro. Best for cost and control: DeepSeek V4.

Split decision. Gemini 3.1 Pro is the better default for quality-first teams: it leads documented reasoning (94.3% GPQA Diamond, 77.1% ARC-AGI-2), sits at the frontier tier on the Artificial Analysis Intelligence Index a step above Grok 4.3, and accepts the broadest multimodal input (text, image, video, audio, PDF). Grok 4.3 is the better default for cost-first and output-first teams: vendor-confirmed pricing of $1.25 input and $2.50 output per million tokens is nearly 5 times cheaper on output, and it adds native PowerPoint, PDF, and spreadsheet generation, live X Corp data, and general availability today while Gemini is still in preview. Best for reasoning, multimodal breadth, and Google-native deployment: Gemini 3.1 Pro. Best for price, native file output, and real-time X data: Grok 4.3.

Split decision by use case. GPT Image 2 wins on text inside images (about 99 percent accuracy), native 4K resolution, official API access, and low-volume pay-per-image economics, and it ranks number one on the Artificial Analysis Image Arena. Midjourney wins on pure aesthetic quality, flat-rate billing that scales to thousands of images, the Niji anime model, and a 21 million plus creative community. Pick GPT Image 2 for design, text, and automation; pick Midjourney for art, exploration, and high-volume flat-fee work. winner_id is null because the two solve different jobs.

Split decision by use case. FLUX 2 wins on price (about $0.03 per image versus $0.134, more than four to one), open weights, self-hosting, and fine-tuning. Nano Banana Pro wins on text rendering (sub-10% error multilingual), multi-subject consistency (up to 14 objects and 5 people), and Gemini 3 real-world grounding. Pick FLUX 2 for cost and control at volume; pick Nano Banana Pro for accuracy-critical and multi-subject work. No single overall winner.

Split by job, no single winner. FLUX 2 wins photorealism (edged Midjourney V7 in our blind tests), multi-reference consistency on up to 10 images, open weights you can self-host, and production economics (about $0.03 per image versus $0.211 per image at high quality). GPT Image 2 wins text rendering (near-perfect character accuracy across Latin and CJK in our testing), native 4K, reasoning layout, and the cheapest draft tier at $0.006 per image. Pick FLUX 2 for photorealistic and reference-heavy production; pick GPT Image 2 for text-in-image design and multilingual layouts.

A split decision. Nano Banana Pro (Google Gemini 3 Pro Image) wins legible in-image text, native 4K, factual diagrams, multilingual layouts, and pay-per-image automation at $0.134 per 1K or 2K image. Midjourney V8.1 wins pure aesthetic mood, cinematic stills, and high-volume creative exploration on an unlimited subscription from $10 per month. Choose Nano Banana Pro for correctness and scale; choose Midjourney for beauty and exploration. Many teams should run both.

Split verdict, not a hedge. Luma Ray 3 wins on the native 16-bit HDR pipeline with ACES2065-1 OpenEXR export, best-in-class video-to-video stylization, a longer single-generation clip (10 seconds versus 8), reasoning-driven generation and predictable flat monthly billing. Veo 3.1 wins on native synced audio in one pass, far longer extended sequences (roughly 148 seconds versus roughly 30), native 4K, pay-per-second flexibility and a slightly higher Artificial Analysis Video Arena ranking (about 1,226 Elo versus about 1,211). Pick Luma Ray 3 for HDR-mastered finishing destined for an OpenEXR color-grade in DaVinci Resolve or Nuke. Pick Veo 3.1 for native audio, dialogue, sound design or sequences longer than 30 seconds. High-end 2026 pipelines increasingly capture audio-driven and longer shots on Veo 3.1, then finish HDR and video-to-video passes on Luma Ray 3.

Seedance 2 and Veo 3.1 win different races, so there is no single overall winner. Seedance 2 is the quality-and-price champion: it tops the Artificial Analysis Text-to-Video Arena (with audio) at an Elo of about 1,218 versus Veo 3.1 at 1,093, generates clips up to 15 seconds in one pass, accepts up to 12 reference files, and runs from roughly $0.067 to $0.22 per second across third-party hosts. Veo 3.1 is the production-and-audio champion: the most proven native lip-sync in the category, verified true 4K at $0.60 per second, Google-confirmed pricing of $0.40 per second for 720p and 1080p with audio, and a stable first-party API on Gemini and Vertex AI. Pick Seedance 2 for the best-looking output at the lowest cost if you can accept that resolution and pricing vary by host. Pick Veo 3.1 for synchronized dialogue, verified 4K delivery, and an enterprise API you can build on today; ByteDance announced a native-4K upgrade for Seedance 2.0 at its Volcano Engine FORCE conference on June 23, 2026, but it is not yet confirmed on the international BytePlus API, so Veo remains the verified-4K choice for now. Because those two buyers pull in opposite directions, winner_id is null: match the model to the job.

A split verdict between the cheapest frontier challenger and one of the most capable models of 2026, and we will not fake a single overall winner. Claude Fable 5 is the capability and verification leader of this pair: it scores 59.9 on the Artificial Analysis Intelligence Index as of August 4, 2026 — second overall, behind only Claude Opus 5's 60.7 — holds a top-ten LMArena Elo, and posts a leading 95 percent on the independently verified vals.ai SWE-bench Verified suite, carries the larger 1,000,000-token context, and is available in the European Union. Grok 4.5 is the value and throughput leader: it costs $2 per million input tokens and $6 per million output tokens against Fable 5's $10 and $50 — roughly five times cheaper on input and about eight times cheaper on output — is measured at about $2.49 per task against roughly $11.80 for Fable 5, posts 64.4 on the Artificial Analysis Coding Agent Index v1.3 against 65.8 for Fable 5 — narrowly behind, not ahead — and is marketed as much faster, consistent with Anthropic's own Slower latency label on Fable 5. Grok 4.5 carries real caveats: it is not yet on the independent SWE-bench Verified or LMArena leaderboards, its AA-Omniscience factuality score of 26 comes with a 54 percent hallucination rate, and it is blocked in the European Union under the AI Act's systemic-risk provisions. Elon Musk's 'Opus-class, much faster' framing is partly supported on coding value and speed but overstated on general intelligence and reliability. Best for capability, independently verified coding, the larger context, and EU availability: Claude Fable 5. Best for cost, throughput, and speed outside the EU: Grok 4.5. No single overall winner — route capability-critical and EU-based work to Claude Fable 5, and cost-sensitive high-volume work outside the EU to Grok 4.5.

A split verdict between the cheapest rate card and the most independently proven flagship, and we will not fake a single overall winner. Grok 4.5 reached public availability on July 9, 2026 as SpaceXAI's new flagship; GPT-5.5 has been OpenAI's established, still-active flagship since April 2026. On the rate card, Grok 4.5 is decisively cheaper: $2 per million input tokens and $6 per million output against GPT-5.5's $5 and $30 — less than half on both sides, verified on both vendors' own documentation — and Artificial Analysis measures its cost per task near the bottom of the frontier tier at about $2.49. SpaceXAI positions Grok 4.5 as 'Opus-class, much faster,' which we label as a vendor claim. GPT-5.5 answers with verified proof Grok 4.5 does not yet have: an independent SWE-bench Verified score of 82.6 percent on vals.ai, an LMArena Elo of 1481, a slightly higher Artificial Analysis Intelligence Index (55 to 54), more than double the context window (1,050,000 versus 500,000 tokens), a fuller native agentic tool stack, and EU availability that Grok 4.5 lacks under the AI Act. On independent agentic coding, both are charted and Grok 4.5 leads: Artificial Analysis lists it at 64.44 on its Coding Agent Index v1.3 through Grok Build at high effort, against GPT-5.5's best entry of 61.49 through Codex at xhigh — one effort step higher — a 2.95-point gap without a matched effort setting or a shared harness. Best for the lowest token price, low cost per task, and vendor-stated speed on high-volume, non-EU work: Grok 4.5. Best for independently verified capability, long context, EU deployment, and a mature ecosystem: GPT-5.5. No single overall winner — route cost-sensitive, high-volume, non-EU work to Grok 4.5, and verification-critical, long-context, or EU work to GPT-5.5.

A genuine split verdict between two value-tier frontier models that launched on the same day, July 9, 2026, and we will not fake a single overall winner. Grok 4.5 is the raw rate-card and speed play: at $2 per million input tokens and $6 per million output it matches Terra on input and comes in 50 percent below its $12 output, and SpaceXAI markets it as Opus-class and much faster, a vendor claim we do not treat as a benchmarked win. GPT-5.6 Terra is the measured-efficiency, longer-reach model: Artificial Analysis measures its cost per task at about $0.55 against Grok 4.5's $2.49 because it burns far fewer tokens per task, it edges the Intelligence Index 55 to 54 — though on the Coding Agent Index v1.3 Grok 4.5 leads it 64.44 to 55.79 at the same high effort setting, in different harnesses — carries more than double the context at 1,050,000 tokens, ships a fuller native tool stack with a documented February 16, 2026 cutoff, and is available in the EU where Grok 4.5 is blocked under the AI Act's systemic-risk provisions. Neither model has an independently verified SWE-bench Verified score — OpenAI did not submit Terra and Grok 4.5 is too new to appear — so verified coding is a tie by absence, and Grok 4.5 carries a standalone AA-Omniscience reliability caveat with a 54 percent hallucination rate. The pricing itself is split: the rate card favors Grok 4.5 while the independent cost-per-task figure favors Terra, and which governs your bill depends on how token-efficient your workload is. Best for the cheapest raw output rate and speed on non-EU work: Grok 4.5. Best for measured cost per task, long context, marginal capability edges, and EU deployment: GPT-5.6 Terra. No single overall winner — route raw high-volume output outside the EU to Grok 4.5, and reasoning-heavy, long-context, and EU-bound work to GPT-5.6 Terra.

Split decision, no overall winner. GPT-5.6 Terra wins on price (40% of Opus 4.8 on input and cached input, 48% on output), cost per task (about $0.55 on Artificial Analysis's workload mix), the larger 1,050,000-token context window, and knowledge freshness, which makes it the rational default for high-volume, budget-bound work. Claude Opus 4.8 wins on independently verified capability: 88.6% on SWE-bench Verified (vals.ai) where Terra has no submitted score, a top-tier Artificial Analysis Intelligence Index, an LMArena Thinking Elo near 1,482, and roughly 84% on agentic computer use. Pick Terra for volume and value; pick Opus 4.8 for the hardest tasks where measured capability must justify the spend.

A split verdict between OpenAI's balanced value tier and Google's multimodal flagship, with no single overall winner. Where GPT-5.6 Terra leads: a low $0.55 cost per task on the Artificial Analysis Intelligence Index; a Coding Agent Index v1.3 score of 55.79 through the Codex harness at high reasoning effort, against 30.34 for Gemini 3.1 Pro through the Gemini CLI harness at the same effort; a February 16, 2026 knowledge cutoff against Gemini's January 2025; double the output ceiling at 128,000 tokens against 64,000; a higher long-context threshold, its surcharge starting at 272,000 input tokens against Gemini's 200,000; and general availability since July 9, 2026. Where Gemini 3.1 Pro leads: native multimodal input across text, image, video, audio, and PDF, where Terra takes only text and image; a marginally higher Artificial Analysis Intelligence Index of 57 against 55; a charted LMArena Elo of 1485, where Terra is not charted; and native Google Search and Maps grounding with the deepest first-party distribution of any frontier vendor. On price the two now list identical rates — $2.00 input, $0.20 cached input, and $12.00 output per million tokens, each doubling on input and rising by half on output above its own long-context threshold. Terra is cheaper only between 200,000 and 272,000 tokens, where Gemini has stepped up and Terra has not. Neither model has an independently verified SWE-bench score: Terra was not submitted, and Gemini's 80.6 percent is self-reported on DeepMind's model card, not run by vals.ai. Best for value coding, long output, the freshest knowledge, and prompts between 200,000 and 272,000 tokens: GPT-5.6 Terra. Best for native multimodal input and the Google ecosystem: Gemini 3.1 Pro. Route agentic coding and long-output work to Terra, and multimodal, small-context, and Google-native work to Gemini 3.1 Pro.

There is no single winner: GPT-5.6 Terra and Luna are lower tiers of the same GPT-5.6 generation, so the choice is capability versus cost rather than better versus worse. Terra leads on capability — 55 to 51 on the Artificial Analysis Intelligence Index v4.1, and 62.28 to 58.66 on the AA Coding Agent Index v1.3, both measured through the Codex harness at max reasoning effort (read August 2, 2026) — and is the pick for harder reasoning and multi-step agentic coding. Luna wins on price by exactly ten times on every rate since OpenAI's July 30, 2026 price cut — 0.20 against 2.00 dollars per million input tokens, and 1.20 against 12.00 on output — making it the default for high-volume, latency-sensitive, cost-sensitive automation. Both share the 1,050,000-token context window, 128,000-token output ceiling, and February 16, 2026 cutoff, and neither appears on the independent SWE-bench Verified leaderboard. Pick Terra when a small capability edge is worth the premium; pick Luna when volume and budget dominate.

A split verdict between SpaceXAI's aggressively priced flagship and Google's multimodal flagship, with no single overall winner. Where Grok 4.5 leads: a flat $6 per million output tokens that is half of Gemini's $12 up to 200,000 tokens and a third of its $18 above that; the independent Artificial Analysis Coding Agent Index v1.3 at 64.4 against 30.3 for Gemini 3.1 Pro; flat pricing with no context-length surcharge; and general availability since July 9, 2026. Where Gemini 3.1 Pro leads: native multimodal input across text, image, video, audio, and PDF, where Grok takes only text and image; a marginally higher Artificial Analysis Intelligence Index of 57 against 54; a charted LMArena Elo of 1485, where Grok is not charted; double the context window at 1,000,000 tokens against 500,000; cheaper cached-input reads at $0.20 per million against Grok's flat $0.50; and availability in the European Union, where Grok 4.5 is not offered under the EU AI Act's systemic-risk designation. Input price is level at the standard band ($2 per million each). Neither model has an independently verified SWE-bench score: Grok 4.5 is too new to appear on vals.ai, and Gemini's 80.6 percent is self-reported on DeepMind's model card. Artificial Analysis also flags a 54 percent hallucination rate for Grok 4.5 on its AA-Omniscience index, a documented reliability caution, and SpaceXAI's 'Opus-class, much faster' framing is a vendor claim pending independent latency benchmarks. Best for cheap output, coding value, and predictable flat billing outside the EU: Grok 4.5. Best for native multimodal input, aggregate intelligence, long context, and EU availability: Gemini 3.1 Pro. Route output-heavy generation and agentic coding to Grok, and multimodal, very-long-context, Google-native, and EU-bound work to Gemini 3.1 Pro.

Split decision. Grok 4.5 wins measured intelligence on the one independent index that scores both — 54 to 44 on the Artificial Analysis Intelligence Index version 4.1 — and leads the AA Coding Agent Index v1.3 as well, at 64.4 against 31.4 for DeepSeek V4-Pro. DeepSeek V4 wins nearly everything else: several times cheaper per output token (about 7 times on V4-Pro, 21 times on V4-Flash), MIT-licensed open weights you can self-host, a 1,000,000-token context window that doubles Grok 4.5, a free tier, and EU availability, where Grok 4.5 is blocked. Pick Grok 4.5 for the higher measured capability and the higher coding score outside the EU; pick DeepSeek V4 for cost, open weights, long context, and availability.

Split decision, and the closest one in this series. GPT-5.6 Terra wins measured intelligence on the one independent index that scores both — 55 to 44 on the Artificial Analysis Intelligence Index version 4.1 — though on the AA Coding Agent Index v1.3 it is DeepSeek V4 Pro that carries the only charted score, at 31.44, with Terra absent. It also reads image input, edges the context window at 1,050,000 tokens, and clears Western data-residency requirements. DeepSeek V4 wins on cost and openness: roughly 4.6 times cheaper on input and about 13.8 times cheaper per output token on V4-Pro, MIT-licensed open weights you can self-host including on Huawei Ascend, and a larger 384K maximum output. What makes this matchup different from the pricier flagships is how small the gap has become: Terra is OpenAI's most price-competitive tier, so with the Batch API discount it lands under two and a half times DeepSeek on input and under seven times on output. Pick GPT-5.6 Terra for more measured intelligence, a charted coding score, image input, managed infrastructure, and US-hosted compliance; pick DeepSeek V4 for the lowest price, open weights, and self-hosting sovereignty.

This is a deliberate split, not a hedge, because Claude Fable 5 and GLM-5.2 are built for different buyers, and we will not crown a single overall winner. Claude Fable 5 is the more capable model and the only one of the two with elite independent verification: it scores 59.9 on the Artificial Analysis Intelligence Index as of August 4, 2026 (second only to Claude Opus 5's 60.7), posts an independent 95 percent on SWE-bench Verified via vals.ai, and holds a top-ten LMArena rating, all for a premium $10 per million input tokens and $50 per million output. GLM-5.2 answers with cost and openness: at $1.40 input and $4.40 output per million tokens it is roughly seven times cheaper on input and eleven times cheaper on output, adds a flat GLM Coding Plan from around $18 per month, ships MIT-licensed open weights you can self-host and fine-tune, and posts a strong Zhipu self-reported SWE-bench Pro of 62.1. Those two headline coding numbers measure different benchmarks under different regimes, Fable's 95 percent independently verified and GLM's 62.1 vendor-reported, so they cannot be stacked as a head-to-head. Best for top-tier independently verified intelligence, coding, and creative writing: Claude Fable 5. Best for price, open weights, self-hosting, and predictable flat billing: GLM-5.2. No single overall winner, pay the closed premium when capability and verification lead your decision, and self-host the open weights when cost and control do.

There is no single winner here, and that is a finding rather than a hedge. GPT-5.6 Terra is measurably the more intelligent model: 55 against GLM-5.2's 51 on the Artificial Analysis Intelligence Index, the one independent yardstick that scores them both, though on that lab's Coding Agent Index v1.3 it is GLM-5.2 that is charted, at 43.18, with Terra absent. GLM-5.2 is dramatically the cheaper model and the only one you can own: $1.40 input and $4.40 output per million tokens against Terra's $2.00 and $12 — a little over a third of the output bill — with MIT-licensed weights you can download, self-host, and fine-tune, and a flat GLM Coding Plan from around $18 per month. The decision reduces to one question: is the model your product, or your cost center? If output quality is what your customers pay for, Terra's four points are cheap and you should stop optimizing the invoice. If the model is infrastructure you run at volume, GLM-5.2 at a little over a third of the output cost, hostable inside your own walls, is the more rational buy — and at 51 on the independent index, the top open-weight score in the world, you are no longer sacrificing much capability to get it. On coding specifically, no verdict is possible: GLM-5.2's 43.18 on the Coding Agent Index v1.3 is independent, its 62.1 on SWE-bench Pro is vendor self-reported, the two measure different things on different scales, and Terra carries neither. Run both on your own repository.

This is a split decision, and both halves of it are emphatic. Claude Opus 4.8 is the more capable model, and the evidence points one way: 56 against GLM-5.2's 51 on the Artificial Analysis Intelligence Index — the only independent yardstick that scores them both — and 69.2 against 62.1 on SWE-bench Pro, the only coding benchmark both vendors report, though both of those coding figures are vendor self-reported rather than independently reproduced. Two different measures, same direction. GLM-5.2 answers with economics that are just as decisive: $1.40 input and $4.40 output per million tokens against Opus 4.8's $5 and $25, which is about 3.6 times and 5.7 times cheaper, plus MIT-licensed open weights you can self-host and fine-tune, a documented one-million-token context window where Anthropic publishes no figure at all for Opus 4.8, and a flat coding plan from around $18 per month. It gives up roughly 9 percent of measured intelligence to do all of that. The tie-breaker is not a benchmark, it is your cost structure: if the model's output is your product and a failed unattended run costs an engineer a day, five points of intelligence is cheap and you should stop optimizing the invoice. If the model is infrastructure you burn at volume, or if your data cannot leave your network, GLM-5.2 at about a fifth of the output cost, running on hardware you control, is the more rational purchase. Most engineering organizations should route the hard, unattended work to Opus 4.8 and everything else to GLM-5.2. One caveat that applies either way: the coding numbers on both sides are vendor-reported and Opus 4.8's context window is unpublished, so test on your own repository instead of trusting either leaderboard.

GLM-5.2 wins this comparison, narrowly, and not on capability. Grok 4.5 is measurably the smarter model — 54 against 51 on the independent Artificial Analysis Intelligence Index — and it is well ahead on the independent Artificial Analysis Coding Agent Index v1.3, 64.44 against 43.18. GLM-5.2 wins on everything that surrounds the model: it is cheaper on every metered line (USD 1.40 input and USD 4.40 output per million tokens against USD 2 and USD 6), it has twice the context at 1,000,000 tokens, its MIT-licensed weights are self-hostable, and it works everywhere today while Grok 4.5 launched blocked in the EU pending a rollout expected around mid-July 2026. Pick Grok 4.5 if you want the best independently measured model and can live with a managed, closed API. Pick GLM-5.2 if you need self-hosting, data residency, a hard cost ceiling, or more than 500,000 tokens of context. The story of this matchup is how close Grok came: at a 1.4 times price gap, SpaceXAI has priced a closed flagship into a range where open-weight economics stop being an automatic argument.

A genuine split by priority, not a hedge. Claude Opus 4.8 is the more capable model on both tests the two share: it leads the independent Artificial Analysis Intelligence Index 56 to 44 and the vendor-reported SWE-bench Pro 69.2 percent to 58.6 percent, and it was the steadier agent in our runs. Kimi K2.6 wins the economics decisively — open weights under a Modified MIT license you can self-host, a confirmed 256K context, and output priced about 6.25 times cheaper. Pick Opus 4.8 when measured intelligence, reliability, and a managed platform are non-negotiable; pick Kimi K2.6 when cost, openness, and control drive the decision.

This was the closest price call in the value tier until OpenAI cut GPT-5.6 Luna's rates on July 30, 2026. Luna now wins every price line: $0.20 per million input tokens against $0.95, $0.02 cached against $0.16, and $1.20 output against $4.00 — roughly 75 to 84 percent cheaper depending on the workload shape. Luna also carries the tiebreakers most hosted deployments feel: seven points of independent intelligence (51 vs 44 on the Artificial Analysis Index) and four times the context window (1,050,000 vs 256,000 tokens). Kimi K2.6 answers with open weights under a Modified MIT license, self-hosting, native vision, and an Agent Swarm of up to 300 sub-agents. Pick Luna as the hosted-API default for intelligence, long context, and cost; pick Kimi K2.6 for open weights, data residency, or multi-agent orchestration — no longer for a cheaper rate card.

Claude Fable 5 wins this comparison, narrowly, and on verified evidence rather than on value. It leads the index that measures both: 60 against Kimi K2.7 Code's 42 on the independent Artificial Analysis Intelligence Index v4.1, read on August 2, 2026 — second only to Claude Opus 5 — and it adds 1509 on LMArena and 95 percent on SWE-bench Verified as measured independently by vals.ai, neither of which Kimi has a third-party equivalent for. Kimi K2.7 Code has no independent coding score we have been able to find — its SWE-bench Verified figure of 60.4 percent is self-reported by Moonshot AI and unreplicated, so it cannot be set against Fable 5's verified 95 percent. The eighteen-point intelligence gap is real, but it sits on a generalist composite of nine mostly non-coding evaluations, and Kimi K2.7 Code is a coding-specialized model. That does not make K2.7 a bad model; it makes it an unverifiable one, which is a different and temporary problem. K2.7 wins more rows than Fable 5 here, and wins them decisively: roughly 10.5 times cheaper on input (USD 0.95 against USD 10), roughly 12.5 times cheaper on output (USD 4 against USD 50), open weights under a Modified MIT license, self-hostable, and fully documented architecturally. The rule: if you can evaluate the model yourself on your own tasks, pick K2.7 — your own measurement is worth more than eighteen points on a generalist index, and the price is not close. If you cannot, pick Claude Fable 5, because you are then choosing between a number a third party verified and a number a vendor asserted. If an independent coding result lands for Kimi K2.7 Code and confirms Moonshot's figures, this verdict should be revisited.

GPT-5.6 Terra wins this comparison, on an exchange rate rather than a knockout. It is the higher-scoring of the two on the index that measures both: 55 against Kimi K2.7 Code's 42 on the independent Artificial Analysis Intelligence Index v4.1, read on August 2, 2026. On coding, the independent Artificial Analysis Coding Agent Index v1.3 charts GPT-5.6 Terra at 62.28, through the Codex harness at max reasoning effort, and carries no Kimi K2.7 Code entry; nor have we found an independent coding score for Kimi K2.7 Code anywhere else — the coding figures Moonshot AI publishes are self-reported on its own harness, unreplicated, and drawn from a different benchmark family, so they cannot be set against Terra's charted score. The thirteen-point intelligence gap is real, but it sits on a generalist composite of nine mostly non-coding evaluations, and Kimi K2.7 Code is a coding-specialized model. That does not make Kimi K2.7 Code a bad model; it makes it an unverifiable one, which is a different and temporary problem. What tips the verdict is price. Terra costs roughly 2.6 times more on input (USD 2.50 against USD 0.95) and roughly 3.75 times more on output (USD 15 against USD 4), with cached input nearly level at USD 0.25 against USD 0.19 — the smallest premium any independently scored flagship charges over Kimi K2.7 Code, where the frontier tier charges ten to twelve times more. Thirteen index points plus roughly four times the context (1,050,000 tokens against 256,000) for that surcharge is an unusually good deal. Kimi K2.7 Code still wins price on every line, open weights under a Modified MIT license, self-hosting, and architectural transparency. The rule: if you can evaluate the model yourself, or you must self-host, or your volume runs to hundreds of millions of output tokens a month, pick Kimi K2.7 Code. Otherwise pick GPT-5.6 Terra, because the price of proof has rarely been this low. If an independent coding result lands for Kimi K2.7 Code and confirms Moonshot's figures, this verdict should be revisited.

Grok 4.5 wins this comparison, on an exchange rate rather than a knockout — and the win is scoped. It leads the one index that measures both: 54 against Kimi K2.7 Code's 42 on the independent Artificial Analysis Intelligence Index v4.1, read on August 2, 2026, and it adds 64.4 on the independent Artificial Analysis Coding Agent Index v1.3, measured through the Grok Build harness at high effort. Kimi K2.7 Code has no third-party coding score we have been able to find; the coding figures Moonshot AI publishes (SWE-bench Verified 60.4 percent, SWE-bench Pro 58.6) are self-reported on its own harness, unreplicated, and from a different benchmark family, so they cannot be set against Grok's charted numbers. The twelve-point intelligence gap is real, but that index is a generalist composite of nine mostly non-coding evaluations and Kimi K2.7 Code is a coding-specialized model — so it is measured and behind on breadth, not proven weaker at the job it was built for. What settles it is the price of that proof, and it has never been lower: Grok 4.5 costs about 2.1 times more on input (USD 2 against USD 0.95) but only about 1.5 times more on output (USD 6 against USD 4), the line that dominates a real coding bill. For a solo developer that premium is roughly eleven dollars a month, and it buys twelve index points plus close to double the context window (500,000 tokens against 256,000). Two caveats scope the win hard. Grok 4.5 is currently blocked in the European Union under the EU AI Act; SpaceXAI signalled a staged opening around mid-July 2026, so EU readers must verify availability for their region before acting on any of this. And Grok 4.5 carries a measured 54 percent hallucination rate on the independent AA-Omniscience evaluation, which argues for keeping a human or a test suite in the loop on anything requiring the model to know rather than to build. K2.7 remains the right call, without hesitation, if you are in the EU today, if you must self-host or keep data inside your own perimeter, if you burn hundreds of millions of output tokens a month, or if you can evaluate the model yourself and therefore do not need a third party to have done it for you. It is cheaper on every line, open on every axis, and available everywhere. If an independent coding result lands for Kimi K2.7 Code and confirms Moonshot's figures, this verdict should be revisited immediately.

Split verdict by use case — the widest intelligence gap in this batch of matchups set against a roughly 30-times price advantage. Claude Fable 5 is the strongest model on the one benchmark that scores both: number one overall on the Artificial Analysis Intelligence Index v4.1 at 60 versus 40 for Qwen 3.6 Plus at the review's mid-July 2026 reading (second behind Claude Opus 5 since July 24, 2026), with an independently verified 95% on SWE-bench Verified (vals.ai) and a 1509 LMArena rating. Qwen 3.6 wins everything about cost and openness: Qwen 3.6 Plus at $0.325 input and $1.95 output per million tokens is roughly 31x and 26x cheaper than Fable 5, the family ships Apache 2.0 open weights (Qwen3.6-27B, Qwen3.6-35B-A3B) you can self-host, and both models share a 1,000,000-token context. No single overall winner: best for peak intelligence, verified coding, and premium creative work is Claude Fable 5; best for cost, multimodality, and open-weight self-hosting is Qwen 3.6. Only the pricing is fetch-verified; the head-to-head score is the independent Artificial Analysis index, and the Qwen open-weight coding figures are vendor self-reported for the open models, not the Plus tier.

GPT-5.6 Sol is the narrow overall winner on measured capability, and by the widest independent margin in this series: 59 against 40 on the Artificial Analysis Intelligence Index v4.1, a 19-point gap that places Qwen 3.6 Plus below every model in the GPT-5.6 family, including the cheapest. Sol adds a charted independent AA Coding Agent Index v1.3 score of 66.57, second of 52, and a marginal context edge (1.05M against 1M). But Qwen 3.6 wins the economics decisively: Qwen 3.6 Plus costs $0.325 per million input tokens and $1.95 per million output tokens against Sol's $5.00 and $30.00 — about 15 times cheaper on both ends — and the family ships open weights under Apache 2.0, the most permissive license on the market. The critical catch: the 40-scoring model, Qwen 3.6 Plus, is closed; the open weights belong to smaller, separate models (Qwen3.6-27B and Qwen3.6-35B-A3B), so choosing Qwen for openness means running a different model than the one that posts the intelligence score. Coding evidence is asymmetric and we do not fake symmetry: Sol's AA Coding Agent Index v1.3 score of 66.57 is independent and applies to the exact model in play, while Qwen's SWE-bench Verified figures are vendor self-reported by Alibaba for the open-weight variants, so the two are never placed side by side. The decision reduces to one question, plus a second: is the capability you are buying capability you actually need, and which Qwen would you actually run? If the model's ceiling constrains your work, pay for Sol. If your tasks are well-specified enough that a 19-point gap never binds, or an Apache-2.0 open license is a hard requirement, Qwen 3.6 is the rational default.

GPT-5.6 Terra wins this comparison on measured capability, but the value case for Qwen 3.6 Plus is the strongest we have set against a GPT-5.6 tier. Both flagships are scored on the same version of the same independent index by the same evaluator: Terra 55, Qwen 3.6 Plus 40. Fifteen points on a scale whose highest posted score to date is 60 is not a rounding error; it is the difference between the leading group and the capable middle, and it shows up on long unsupervised chains as tasks that finish versus tasks that need you. Terra also holds a marginally larger context window and a marginal edge on context (1,050,000 tokens against 1,000,000). What makes the trade so sharp is the price. Qwen 3.6 Plus is roughly 7.7 times cheaper on both input and output — USD 0.325 against USD 2.50, USD 1.95 against USD 15 — the widest price gap in this series, and it wins measured intelligence per dollar by more than five times, with both sides of that ratio independently sourced. It is also natively multimodal (text, image, video) and belongs to a family with Apache 2.0 open-weight siblings, giving a self-hosting escape hatch Terra cannot match — though the Plus flagship itself is closed, API-only, exactly like Terra. The rule: if budget binds, or your volume runs to hundreds of millions of output tokens a month, or you need multimodal input, pick Qwen 3.6 Plus. Otherwise, if capability bounds your work and you need the only independent coding evidence in the pairing, pick GPT-5.6 Terra. One caution on the data: the independent 40 describes Qwen 3.6 Plus specifically, not the open-weight variants, which have no comparable independent score.

This comparison does not have a single winner, and we are not going to invent one — it is a genuine split. Grok 4.5 owns the measure: on version 4.1 of the independent Artificial Analysis Intelligence Index it scores 54 against Qwen 3.6 Plus's 40, a fourteen-point lead from the same evaluator with neither vendor involved, and it is the only one of the two with an independent coding result at all. If the thing you are buying is how good the model is at thinking, and you can access it, Grok 4.5 is the pick. Qwen 3.6 owns almost everything else: Qwen 3.6 Plus is about six times cheaper on input and about three times cheaper on output, carries a 1,000,000-token context window against Grok's 500,000, and accepts image and video as well as text, while its open-weight Apache 2.0 siblings let you self-host and keep data inside your own perimeter — and both the Plus API and the open weights are available in the European Union, where Grok 4.5 currently is not. For the large majority of teams that are cost-bound, context-bound, in the EU, or need open weights, Qwen 3.6 is the correct answer. Two caveats scope both sides: Grok 4.5 is blocked in the EU today with a staged opening signaled for around mid-July 2026, so verify your region before acting; and Grok 4.5 carries a measured 54 percent hallucination rate on AA-Omniscience, a failure rate on a different axis entirely from its Intelligence Index. On coding we deliberately declare no winner, because Grok's figure is independently charted while Qwen's only coding numbers are self-reported by Alibaba for its open-weight models, a different benchmark family that cannot honestly be set against the independent index.

GPT-5.6 Luna vs Claude Haiku 4.5 is a choice between capacity and latency, not a duel of intelligence — Luna wins that outright. On the independent Artificial Analysis Intelligence Index (v4.1) Luna scores 51 to Haiku's 24, a 27-point gap, and its 1,050,000-token context is more than five times Haiku's 200,000. Price no longer separates them in Haiku's favor: after OpenAI's July 30, 2026 cut, Luna costs $0.20 per million input tokens against Haiku's $1 and $1.20 output against Haiku's $5 — about five times and four times cheaper respectively. But Haiku was built for speed, not reasoning: at 91.7 tokens per second and a 0.82-second first token it is the decisively better engine for latency-bound, high-volume, real-time work and large sub-agent fleets. Pick GPT-5.6 Luna for reasoning, long context, and raw capability — the right default for most buyers. Pick Claude Haiku 4.5 when latency and throughput are the product, where it beats Luna and it is not close.

This is a split verdict, and calling a single winner would be dishonest. GPT-5.6 Terra and Claude Haiku 4.5 are tuned for opposite ends of the same trade-off. Terra wins capability outright: it scores 55 to Haiku 4.5’s 24 on the independent, version-matched AA Intelligence Index v4.1 — a 31-point lead — carries an independently charted coding score, and offers a 1.05-million-token context window against Haiku 4.5’s 200,000. Claude Haiku 4.5 wins the axis it was built for: 91.7 tokens per second of output, a 0.82-second time to first token, and lower pricing on every published rate — $1 input, $0.10 cached input, and $5 output per million against Terra’s $2.00, $0.20, and $12. Pick Terra when the work rewards reasoning, depth, and long context; pick Haiku 4.5 when you are optimizing latency, throughput, and cost per token across high volume such as real-time interfaces and large sub-agent fleets. There is no universal winner — only the right tool for the axis you optimize.

Split decision, and the narrowest price gap in this series. GPT-5.6 Luna wins measured intelligence on the one independent index that scores both — 51 to 44 on the Artificial Analysis Intelligence Index version 4.1 — though on the AA Coding Agent Index v1.3 it is DeepSeek V4 Pro that carries the only charted score, at 31.44, with Luna absent. It also reads image input, edges the context window at 1,050,000 tokens, and clears Western data-residency requirements. DeepSeek V4 wins on openness and still leads on standard output pricing: about 1.4 times cheaper per output token on V4-Pro, MIT-licensed open weights you can self-host including on Huawei Ascend, near-free cache hits, and a larger 384K maximum output. But OpenAI's July 30, 2026 price cut reshaped the cost axis: at 0.20 dollars per million input tokens, GPT-5.6 Luna is now roughly 2.2 times cheaper than V4-Pro on input, and with the Batch API it undercuts V4-Pro on output as well. Pick GPT-5.6 Luna for more measured intelligence, a charted coding score, image input, managed infrastructure, cheaper input, and US-hosted compliance; pick DeepSeek V4 for open weights, self-hosting sovereignty, near-free cache hits, and the lowest standard output rate.

Split decision. GPT-5.6 Sol wins peak measured intelligence on the one independent index that scores both models the same way, leading 59 to 44 on the Artificial Analysis Intelligence Index version 4.1, and it is the only one of the two with a charted independent coding score, at 66.57 on the AA Coding Agent Index v1.3. It also reads image input and clears Western data-residency requirements through OpenAI hosting. MiniMax M3 wins on price and openness: roughly 16 times cheaper per input token and about 25 times cheaper per output token at standard rates, downloadable open weights you can self-host, a matching one-million-token context, and native text, image, and video input with computer use. Pick GPT-5.6 Sol for top measured capability, the OpenAI ecosystem, and US-hosted compliance; pick MiniMax M3 for price, open weights, and native multimodality.

There is no single winner here, and the split is the finding. GPT-5.6 Terra and MiniMax M3 are both scored on the same version of the same independent index — Terra 55, MiniMax 44 — so the eleven-point capability gap is real and externally measured, not either vendor's claim. Terra also carries a slightly larger 1,050,000-token context, and for quality-bound work that advantage earns its price; on the independent AA Coding Agent Index v1.3 Terra is charted at 62.28 through the Codex harness at max reasoning effort while MiniMax M3 has no entry, so that axis yields evidence on one side rather than a head-to-head. But the price gap is a chasm: MiniMax M3 is roughly 8.3 times cheaper on input and 12.5 times cheaper on output at standard rates, wins measured intelligence per dollar by about tenfold, and adds open weights, self-hosting, data residency, and native multimodality across text, image, and video — things a hosted API cannot offer at any price. Its one honest asterisk, rates that double above 512,000 tokens, still leaves it several times cheaper than Terra even at extreme context. The rule: if quality is your binding constraint — long unsupervised agents, unedited customer output, reasoning-heavy work — pick GPT-5.6 Terra. If cost, control, or modality is your binding constraint — high volume, self-hosting, compliance, or native image and video — pick MiniMax M3. Both are correct answers to different questions; the only wrong move is choosing on the price sticker or the benchmark alone without asking which constraint your own workload is actually bound by.

Grok 4.5 versus MiniMax M3 is a genuine value split, and we record it as one rather than forcing a winner. Grok 4.5 wins measured intelligence — 54 to 44 on the same independent Artificial Analysis Intelligence Index (version 4.1) — and it is the only model of the two with an independently charted coding score, so it owns the single axis where the capability evidence comes from outside the vendor. MiniMax M3 wins nearly everything else on the spec sheet: output that costs five times less at USD 1.20 against USD 6 per million tokens, twice the context at one million tokens, an open-weight mixture-of-experts design you can eventually self-host, published architecture, and native multimodality. That MiniMax M3 wins more rows does not settle the decision, because its advantages rest heavily on vendor-reported numbers and weights that had not shipped at the time of writing, while Grok 4.5's rest on independent measurement. Two caveats scope each side. Grok 4.5 is restricted in the European Union as of mid-July 2026 under the EU AI Act — SpaceXAI has signalled a staged opening, so this is today's state rather than a permanent one, and EU readers must verify access for their region before building. And Grok 4.5 carries a measured 54 percent hallucination rate on the independent AA-Omniscience evaluation, a failure rate on a different axis from its Intelligence Index, which argues for retrieval or human review on factual work. MiniMax M3's caveats are the verification gap and jurisdiction: until its weights ship, its capability numbers are the vendor's own and the only way to run it is a hosted API operated from China, under Chinese data law. The decision reduces to one question — do you value capability a third party has proven, at a premium, or price, context, and control on vendor faith? Buyers who need independently proven capability should pick Grok 4.5; buyers who optimize for cost, context length, and openness should pick MiniMax M3. There is no universal winner here, only the right tool for the axis that binds your work.

Claude Fable 5 versus MiniMax M3 is the widest capability-versus-cost spread we have tested — the sharpest fork in this comparison series. Claude Fable 5 is the most capable model in our coverage: it scores 60 on the independent Artificial Analysis Intelligence Index at version 4.1 against 44 for MiniMax M3 on the same index and the same version, a 16-point lead and the highest figure across every model we track. In our own production testing it held state deeper into long agent runs and reached correct results with fewer human interventions on the hardest multi-file migrations, backed by the deepest agent toolkit and the most mature production safety design in this matchup — refusals return a clean success response with an automatic fallback to Claude Opus 4.8, and refused-before-output requests are not billed. MiniMax M3 wins everything economic and open: its standard output price of 1.20 dollars per million tokens is about 42 times cheaper than Fable 5's 50 dollars, its input at 0.30 dollars is about 33 times cheaper than Fable 5's 10 dollars, the model ships open-weight for self-hosting, and it is natively multimodal across text, image, and video. Two nuances scope MiniMax's cost story: its rate doubles above 512K tokens, so filling the shared one-million-token window is not billed at the headline price, and its hosted API is operated by a Chinese company under China's data laws, though open weights make self-hosting the escape hatch. There is no single overall winner, and pretending otherwise would fail the reader. If you are capability-bound and the cost of being wrong dwarfs the token bill, Claude Fable 5 earns its premium. If you are cost-bound and running at scale, MiniMax M3 delivers a remarkable share of frontier quality for a fraction of the price. The 42-times output price gap and the 16-point independent intelligence gap point in opposite directions, and your workload decides.

Claude Opus 4.8 versus MiniMax M3 is a clean capability-versus-cost fork with no single overall winner. Claude Opus 4.8 is the more capable model on the neutral scale: it scores 56 on the independent Artificial Analysis Intelligence Index at version 4.1 against 44 for MiniMax M3 on the same index and version, a 12-point lead by a neutral evaluator, and it pairs that with the deeper agent ecosystem in this matchup — Dynamic Workflows for parallel subagents, effort controls, and a cautious self-checking reliability personality that held instructions tightly and needed fewer human interventions on the hardest work in our production testing. MiniMax M3 wins everything economic and open: its standard output price of 1.20 dollars per million tokens is about 21 times cheaper than Opus 4.8's 25 dollars, its input at 0.30 dollars is about 17 times cheaper than Opus 4.8's 5 dollars, it ships open-weight for self-hosting, and it is natively multimodal across text, image, and video. Two nuances scope MiniMax's cost story: its rate doubles above 512K tokens, so filling the shared one-million-token window is not billed at the headline price, and its hosted API is operated by a Chinese company under China's data laws, though open weights make self-hosting the escape hatch. Pick Claude Opus 4.8 when the cost of being wrong dwarfs the token bill and you want a deep managed ecosystem; pick MiniMax M3 when the token bill is the whole constraint and you can run at scale. The 21-times output price gap and the 12-point independent intelligence gap point in opposite directions, and your workload decides.

There is no single winner here, and the split is the finding. Claude Sonnet 5 and MiniMax M3 are both scored on the same version of the same independent index — Sonnet 53, MiniMax 44 — so the nine-point capability gap is real and externally measured, not either vendor's claim. Sonnet 5 also carries a managed Anthropic ecosystem and prices its 1,000,000-token context flat, and for quality-bound work those advantages earn their price. But the price gap, though narrower than the ones MiniMax opens against pricier flagships, still runs every way: MiniMax M3 is roughly 6.7 times cheaper on input and 8.3 times cheaper on output — wins measured intelligence per dollar by about sevenfold, and adds open weights, self-hosting, data residency, and native multimodality that a hosted API cannot offer at any price. Its one honest asterisk, rates that double above 512,000 tokens, still leaves it several times cheaper than Sonnet even at extreme context. The rule: if quality is your binding constraint — long unsupervised agents, unedited customer output, reasoning-heavy work — pick Claude Sonnet 5. If cost, control, or modality is your binding constraint — high volume, self-hosting, compliance, or native image and video — pick MiniMax M3. Both are correct answers to different questions; the only wrong move is choosing on the price sticker or the benchmark alone without asking which constraint your own workload is actually bound by.

Split decision, but no longer the split this page used to describe. GPT-5.6 Luna wins independent capability on the one index that scores both the same way, leading MiniMax M3 51 to 44 on the Artificial Analysis Intelligence Index version 4.1 — and since OpenAI's July 30, 2026 price cut it wins on price too. Output is an exact tie at 1.20 dollars per million tokens on both models, while Luna takes input at 0.20 dollars against 0.30, cached reads at 0.02 against 0.06, and long context at 0.40 and 1.80 against 0.60 and 2.40, plus a 50 percent Batch API discount MiniMax does not offer. MiniMax M3 keeps what no price cut can touch: open weights you can download, self-host, and fine-tune, native multimodality, and a 1,000,000-token context window. Neither model charges a flat rate across its full window, contrary to what an earlier version of this comparison stated. Pick GPT-5.6 Luna if you are renting tokens through an API; pick MiniMax M3 if you need to own and run the model yourself.

Split verdict, no single overall winner. Muse Spark 1.1 wins on paper: it scores a point higher on the Artificial Analysis Intelligence Index v4.1 (51 to 50) and is cheaper on both sides of the card (input $1.25 versus $1.50 and output $4.25 versus $9.00 per million tokens). But Muse Spark 1.1 is a US-only closed preview behind a waitlist, while Gemini 3.5 Flash is generally available worldwide today and engineered for roughly four times flagship speed with broad native multimodal input. Best on paper: Muse Spark 1.1. Best you can deploy now, everywhere, and fast: Gemini 3.5 Flash.

Split decision. Gemini 3.5 Flash wins measured intelligence on the one index that scores both the same way, leading MiniMax M3 50 to 44 on the Artificial Analysis Intelligence Index version 4.1, and it is built for speed, running several times faster than frontier-class models. Both share a 1,048,576-token context window, but Gemini prices it flat while MiniMax doubles above 512K tokens. MiniMax M3 wins on cost and openness: exactly seven and a half times cheaper per output token and five times cheaper per input token at standard rates, open weights you can self-host and fine-tune, and native multimodality. Pick Gemini 3.5 Flash for the higher independent score, raw speed, and flat long-context pricing; pick MiniMax M3 for cost, open weights, and self-hosting.

Gemini 3.5 Flash vs Claude Haiku 4.5 is a genuine tie — the two win different, non-overlapping things. Gemini 3.5 Flash leads on the independent Artificial Analysis Intelligence Index (v4.1) by 50 to 24, a 26-point gap, and carries five times the context at 1,000,000 tokens against 200,000, plus native audio and video input. Claude Haiku 4.5 is cheaper on both price lines — $1 input and $5 output against $1.50 and $9 — landing roughly 36 to 42 percent lower on a real bill, and it publishes class-leading latency of 91.7 tokens per second and a 0.82-second first token that Gemini does not match. Pick Gemini 3.5 Flash for reasoning, long context, and mixed-media input; pick Claude Haiku 4.5 for cost-sensitive, latency-bound, high-volume work. There is no overall winner: buy the axis you need.

There is no single winner — Gemini 3.5 Flash and GLM-5.2 answer different questions. GLM-5.2 wins the numbers table: it is cheaper on both input and output tokens ($1.40 and $4.40 per million versus $1.50 and $9.00), edges Gemini on the independent Artificial Analysis Intelligence Index (51 versus 50), ships open-weight under an MIT license for full self-hosting, and is coding-specialized. Gemini 3.5 Flash wins on speed (roughly 4x faster than frontier models), native multimodal input across text, image, audio, and video, an instant fully managed API with zero operational burden, and flat pricing across the entire 1M-token context. Pick GLM-5.2 to minimize token cost and own your stack; pick Gemini 3.5 Flash for latency-sensitive, multimodal, or zero-ops production workloads.

Claude Sonnet 5 wins overall; Muse Spark 1.1 wins on price. On the independent Artificial Analysis Intelligence Index v4.1, Sonnet scores 53 to Muse Spark 1.1’s 51, it is generally available worldwide today, and it sits in a mature ecosystem with a real track record. Muse Spark 1.1 is cheaper on both input and output — roughly 2.35 times cheaper on output — but it is a US-only public preview behind a waitlist. Pick Muse Spark 1.1 for high-volume US workloads that have cleared the waitlist; pick Claude Sonnet 5 for higher measured intelligence, worldwide availability, and maturity.

Split verdict, no single winner. Muse Spark 1.1 wins measured intelligence, leading the independent Artificial Analysis Intelligence Index version 4.1 by 51 to 44, and it prices its full one-million-token context at a flat rate. MiniMax M3 wins price, openness, and access: it is more than four times cheaper on input at standard rates, ships open weights you can self-host, and is generally available worldwide, while Muse Spark 1.1 remains a closed, United States-only preview. Pick Muse Spark 1.1 if you are a United States team that can get preview access and want the extra intelligence; pick MiniMax M3 if you are anywhere else, want to self-host, or care most about cost.

Muse Spark 1.1 and GPT-5.6 Luna tie at 51 on the Artificial Analysis Intelligence Index (v4.1), so intelligence is a wash. The split now comes down to price and access, and both point the same way. After OpenAI's July 30, 2026 price cut, GPT-5.6 Luna is cheaper on input at $0.20 per million tokens against $1.25 — about 6.25 times less — and cheaper on output at $1.20 against $4.25, roughly 72 percent less. It also carries a slightly larger 1,050,000-token context and is generally available worldwide, while Muse Spark 1.1 ships only as a closed, United States-only preview. GPT-5.6 Luna takes the overall win outright: it is no longer a trade between input price and output price, because Luna now wins both.

A genuine tie on measured intelligence, broken by openness and access, and we will not invent an overall winner where the numbers refuse to separate. Muse Spark 1.1 and GLM-5.2 both score 51 on the independent Artificial Analysis v4.1 Intelligence Index, and Muse Spark 1.1's token prices undercut GLM-5.2's by cents — $1.25 input and $4.25 output per million against $1.40 and $4.40, roughly 5 percent cheaper on a balanced job. If intelligence and sticker price were the whole story it would lean, faintly, to Muse Spark 1.1. But Muse Spark 1.1 is closed weights behind a United States-only preview waitlist, while GLM-5.2 is open-weight under the MIT license, globally available, self-hostable, fine-tunable, coding-specialized, and backed by a flat GLM Coding Plan from around $18 per month. GLM-5.2's own caveats are real — its production API is hosted in China and its 62 percent SWE-bench Pro figure is Zhipu self-reported and unverified, never to be stacked against Muse Spark 1.1's independent scores — but its open weights give buyers an escape hatch Muse Spark 1.1 cannot match. Best for US-based multimodal or agentic work at the lowest metered token price: Muse Spark 1.1. Best for openness, global access, self-hosting, and heavy-usage coding value, and our overall pick: GLM-5.2. At equal measured intelligence and near-identical price, access decides.

This is a genuine split, and the tightest Sol has faced from an open challenger: GPT-5.6 Sol keeps a narrow lead on measured capability while Kimi K3 answers with a decisive price cut and an open-weight promise. On the independent Artificial Analysis Intelligence Index version 4.1, Sol scores 59 to Kimi K3's 57 — a two-point gap on the same harness, down from the seventeen points Sol held over the older Kimi K2.7 Code. Sol also leads the independent agentic-coding index, 66.57 against 61.34 on the Coding Agent Index v1.3, and a marginally larger 1,050,000-token context against Kimi K3's 1,000,000. Kimi K3 wins the economics: $3 input and $15 output per million tokens against Sol's $5 and $30 — 40 percent cheaper on input and exactly half the price on output — plus a roughly 2.8-trillion-parameter Mixture-of-Experts architecture with about 50 billion active parameters, Kimi Delta Attention, native vision, and open weights promised under a Modified MIT license. The critical caveat: those weights are not public yet, and Moonshot targets July 27, 2026, so today Kimi K3 is open-weight-in-waiting rather than downloadable. Moonshot's other headline numbers are self-reported in its own harness and never placed beside Sol's independent scores. We do not crown one winner because the evidence honestly splits: pick GPT-5.6 Sol for the hardest reasoning, the largest context, and a proven independent coding record; pick Kimi K3 for output cost, architectural efficiency, and open weights you can plan around.

Split by what you weight. On the one shared independent yardstick, the Artificial Analysis Intelligence Index version 4.1, Kimi K3 scores 57 to Claude Opus 4.8's 56, and it costs about 1.67 times less per token (3 dollars input and 15 dollars output versus 5 and 25). Both ship a 1-million-token context window. So Kimi K3 wins the paper metrics: a marginally higher independent score, lower price, and open weights by design. But its open weights were not published at launch, promised for July 27, 2026 under Modified MIT, and it is days old, while Claude Opus 4.8 has been proven in production since late May, is fully available today, and sits inside a mature agentic ecosystem (Claude Code, computer use, Bedrock, Vertex). Choose Kimi K3 for the higher score, lower cost, and open weights on paper; choose Claude Opus 4.8 for maturity, reliability, and being completely available right now.

Kimi K3 is the smarter model — 57 on the Artificial Analysis Intelligence Index version 4.1 against Kimi K2.6's 44, a 13-point jump, plus a 1-million-token context window (four times K2.6's 256K). But K3 costs roughly three times more ($3.00 input and $15.00 output per million tokens versus $0.95 and $4.00), and its open weights are not out yet — Moonshot targets July 27, 2026. Kimi K2.6 is the better pick for high-volume, cost-sensitive coding and for teams that need open weights they can self-host today; Kimi K3 is the pick when raw intelligence and long-context reach matter more than the token bill. There is no single winner: choose K2.6 for budget and open-weight control now, K3 for frontier capability.

There is no single winner: GLM-5.2 vs Kimi K3 is a genuine split. Kimi K3 is the more intelligent model — 57 versus 51 on the independent Artificial Analysis Intelligence Index version 4.1, the largest openly announced architecture, and the only one with native vision — but it costs about 2.1 times more on input and 3.4 times more on output, and its open weights are still pending, targeted for July 27, 2026. GLM-5.2 is the cheaper, coding-specialized pick, with a flat GLM Coding Plan from about $18 per month and MIT weights you can self-host today. Pick Kimi K3 for maximum capability if the higher bill and short wait are acceptable; pick GLM-5.2 for cost, coding focus, and immediate self-hosting. All benchmark figures are either from the independent Artificial Analysis index or clearly labeled vendor self-reported and not yet independently verified.

This is a genuine tie, because MiniMax M3 and Kimi K3 answer different questions. Kimi K3 is the more intelligent model by a wide, independently measured margin — 57 against 44 on the same version-4.1 index, a 13-point lead that shows up in real reasoning and agentic work. MiniMax M3 wins the two things K3 cannot match today: it is roughly ten times cheaper on input and twelve and a half times cheaper on output, and its open weights are already published while K3's wait until July 27, 2026. Pick MiniMax M3 when cost, volume, or self-hosting now is your constraint; pick Kimi K3 when the hardest reasoning is your bottleneck and you can absorb the premium.

There is no overall winner here, and that is a conclusion rather than a hedge: Claude Fable 5 and Kimi K3 answer different questions. On the same version 4.1 of the independent Artificial Analysis Intelligence Index, read mid-July 2026, Claude Fable 5 scored 60 — then the highest figure any model had recorded — while Kimi K3 scored 57, third overall at that reading and above Claude Opus 4.8 at 56. Claude Opus 5, charted July 24, 2026, now leads at 60.7, with Fable 5 at 59.9 and Kimi K3 at 57.1 as of August 4, 2026. That three-point gap is the narrowest an open-weight model has come to the closed summit, and it is independently measured rather than asserted. Against it, Kimi K3 charges USD 3 per million input tokens and USD 15 per million output tokens against Claude Fable 5’s USD 10 and USD 50 — roughly 3.3 times cheaper both ways — matches Fable’s 1,000,000-token context window, discloses its mixture-of-experts architecture, and is licensed as open-weight with the weights promised for July 27, 2026. Kimi K3’s coding and agentic benchmarks are Moonshot-reported and not yet independently replicated, so they are never set against Fable 5’s figures on this page. The rule that settles it: if the hardest task in your workload sits above what a 57-index model can finish, buy Claude Fable 5, because those extra points of measured ceiling are the whole reason it exists. If it sits below that line, and for most teams it does, buy Kimi K3, which delivers near-frontier intelligence, the same context window, and openness in waiting for about a third of the cost. Best for measured intelligence and a verified ceiling: Claude Fable 5. Best for price, openness, and value at near-frontier quality: Kimi K3.

There is no overall winner here, and that is a conclusion rather than a hedge: Claude Fable 5 and Kimi K2.6 are not substitutes for one another. Both models are scored on the same version 4.1 of the independent Artificial Analysis Intelligence Index, which makes the trade unusually clean — Claude Fable 5 posts 60, the highest score any model has recorded, while Kimi K2.6 posts 44. That is a real, third-party-measured gap of 16 points. Against it stands a price gap that runs the other way and runs harder: Claude Fable 5 charges USD 10 per million input tokens, USD 1 cached, and USD 50 per million output tokens, while Kimi K2.6 charges USD 0.95, USD 0.16, and USD 4 — roughly 10.5 times cheaper on input and 12.5 times cheaper on output, with open weights under a Modified MIT license, self-hosting, a published ceiling of 300 sub-agents across 4,000 coordinated steps, and a fully disclosed mixture-of-experts architecture. On coding, the evidence is asymmetric and cannot be stacked: Claude Fable 5's 95 percent on SWE-bench Verified was measured independently by vals.ai, whereas Kimi K2.6's 58.6 on SWE-bench Pro is a different benchmark measured by Moonshot AI itself, so the two figures are never set against each other on this page. The rule that settles it: if the hardest task in your workload sits above what a 44-index model can complete, buy Claude Fable 5 — Kimi K2.6 will not finish the job at any price and the premium is irrelevant. If it sits below that line, and for most teams it does, buy Kimi K2.6 — Claude Fable 5 is then charging roughly eleven times more for a ceiling you will never reach. Best for measured intelligence, verified coding evidence, and 1M-token context: Claude Fable 5. Best for price, open weights, data residency, and high-volume agentic loops: Kimi K2.6.

GPT-5.6 Terra wins this comparison, and it is the narrowest call in this family — because for once the open model came with receipts. Both models are scored on the same version of the same independent index by the same evaluator: Terra 55, Kimi K2.6 44. Eleven points on a scale whose highest posted score to date is 60 is not a rounding error; it is the difference between the leading group and the head of the tier below, and it shows up on long unsupervised chains as tasks that finish versus tasks that need you. Terra also carries roughly four times the context (1,050,000 tokens against 256,000). Both models are charted on the independent AA Coding Agent Index v1.3, but through different harnesses: Terra 62.28 through Codex at max reasoning effort, Kimi K2.6 32.62 through Claude Code — so Terra takes that axis too, with the harness gap stated rather than hidden. What makes the verdict close is the price. Kimi K2.6 is cheaper on every line — USD 0.95 against USD 2.50 on input, USD 0.16 against USD 0.25 cached, USD 4 against USD 15 on output — and it wins measured intelligence per dollar by roughly a factor of three, with both sides of that ratio independently sourced. It also wins open weights under a Modified MIT license, self-hosting, full architectural disclosure, and a published agent orchestration ceiling of up to 300 subagents across as many as 4,000 steps. GPT-5.6 Terra is simply the closest an independently scored closed model gets to Kimi K2.6 on price — roughly 2.6 times on input where the frontier tier charges eight to twelve times more — which makes eleven measured points plus four times the context an unusually good buy. The rule: if budget is your binding constraint, or you must self-host, or your volume runs to hundreds of millions of output tokens a month, pick Kimi K2.6. Otherwise pick GPT-5.6 Terra. One caution on the data: Kimi K2.6's index score is 44, not the 54 widely quoted, which comes from a previous version of the index and is not comparable to current-version scores.

Grok 4.5 wins this comparison, and unusually for a closed-versus-open matchup it wins on measurement rather than on faith. Both models have been scored by the same independent evaluator on the same yardstick — version 4.1 of the Artificial Analysis Intelligence Index — and Grok 4.5 takes 54 against Kimi K2.6's 44. Ten points, no vendor involved. (The 54 widely circulated for Kimi K2.6 comes from an earlier version of that index and is stale; on the current version it is 44, and we never use the old figure as a score.) Add close to double the context window, 500,000 tokens against 256,000, and Grok 4.5 leads on the two things hardest to work around. What settles it is the price of those ten points, and it has never been lower: Grok 4.5 costs about 2.1 times more on input (USD 2 against USD 0.95) but only about 1.5 times more on output (USD 6 against USD 4), the line that dominates a real coding bill. For a solo developer that premium is roughly eleven dollars a month. Two caveats scope the win hard. Grok 4.5 is currently blocked in the European Union under the EU AI Act; SpaceXAI signalled a staged opening around mid-July 2026, so EU readers must verify availability for their region before acting on any of this — it is the fastest-moving fact on the page. And Grok 4.5 carries a measured 54 percent hallucination rate on the independent AA-Omniscience evaluation, a failure rate on a different axis entirely from its Intelligence Index, which argues for keeping a human or a test suite in the loop on anything requiring the model to know rather than to build. On coding, we deliberately declare no winner: Grok 4.5's coding figure is independently charted, Kimi K2.6's is self-reported by Moonshot AI on its own harness and belongs to a different benchmark family, so the two cannot honestly be set against each other. Kimi K2.6 remains the right call, without hesitation, if you are in the EU today, if you must self-host or keep data inside your own perimeter, if you burn hundreds of millions of output tokens a month, or if your workload decomposes into the massively parallel agent swarm Grok has no answer to. It is cheaper on every line, open on every axis, available everywhere, and — unlike most of the open-weight field — genuinely measured by somebody other than the company selling it.

Split verdict, no forced winner. Claude Sonnet 5 wins on price (2 dollars per million input tokens versus 10 dollars) and measured speed (about 79 tokens per second), making it the value pick for volume. Claude Fable 5 wins on raw capability: 60 on the Artificial Analysis Intelligence Index versus 53 at the review's mid-July 2026 reading (59.9 to 53.4 as of August 4, 2026, with only Claude Opus 5 higher), first on LMArena at an Elo of 1509 at that reading, and an independent 95 percent on SWE-bench Verified where Sonnet 5 is not listed. Both are Anthropic models on one API, so most teams should run Sonnet 5 as the default and escalate to Fable 5 only for the hardest work.

Split decision. Grok 4.5 wins cheaper output (6 dollars per million tokens), a measured 2.49 dollars per task, and the only charted independent coding score (64.4 on the AA Coding Agent Index v1.3, via Grok Build at high effort). Claude Sonnet 5 wins the 1 million-token context window (double Grok 4.5), the only independently measured speed (about 79 tokens per second), a free Claude.ai path, and EU availability, where Grok 4.5 is blocked. On raw intelligence they tie (54 to 53). Pick Grok 4.5 for output-heavy work outside the EU; pick Claude Sonnet 5 for context, ecosystem, and EU compliance.

GPT-5.6 Luna wins this matchup narrowly on the axis that defines it — cost-efficient, long-context work. It is cheaper on every price line, and after OpenAI's July 30, 2026 price cut the margin is wide: $0.20 per million input tokens against Sonnet 5's $2 rate, about ten times less. The gap widens further because Anthropic's tokenizer needs about 30 percent more tokens for the same text. Luna gives up only two points of aggregate intelligence (51 versus 53). Neither model is charted on the independent Artificial Analysis Coding Agent Index, so agentic coding is not settled here by either side. Claude Sonnet 5 answers with the deeper Claude ecosystem, free consumer access on Claude.ai, and that marginal intelligence edge. Pick Luna for high-volume, cost-sensitive workloads; pick Sonnet 5 if you live in the Claude tooling stack or need free access.

Split verdict, no forced winner. GPT-5.6 Terra wins on price (about five times cheaper, $2.00 vs $10 per million input tokens), cost per task (roughly $0.55 vs $11.80), and a slightly larger 1,050,000-token context window. Claude Fable 5 wins on raw capability: 60 on the Artificial Analysis Intelligence Index versus 55, first on LMArena at an Elo of 1509, and an independent 95 percent on SWE-bench Verified where Terra is not listed. They run on different platforms, so most teams should standardize on Terra for high-volume, cost-sensitive work and reach for Fable 5 on the hardest reasoning, verified coding, and premium writing.

A split verdict between OpenAI's flagship and Anthropic's balanced value tier, and we will not fake a single overall winner. GPT-5.6 Sol is the capability leader: on the independent Artificial Analysis Intelligence Index it scores 59 against Claude Sonnet 5's 53, it places second at 66.57 on the Coding Agent Index v1.3 where Sonnet 5 is not charted, it adds an ultra multi-agent reasoning mode Sonnet 5 has no equivalent to, a marginally larger 1,050,000-token context against 1,000,000, a newer February 16, 2026 knowledge cutoff versus January 2026, and it is the only one of the two charted on LMArena at 1486 (No.8). Claude Sonnet 5 is the price, speed, and reach leader: its $2 per million input tokens and $10 per million output undercut Sol's $5 and $30 by roughly 60 to 67 percent; Artificial Analysis measures it faster at 79 output tokens per second against Sol's 74.5; it is the default model on the free and Pro plans of Claude.ai; and it reaches a 300,000-token output ceiling via a Batch beta. One honest caveat sits on the price story: Anthropic states Sonnet 5's newer tokenizer counts roughly 30 percent more tokens for the same text, which narrows the effective discount. Neither model has been submitted to independent SWE-bench Verified. Best for peak capability, agentic coding, and the deepest reasoning: GPT-5.6 Sol. Best for the lowest price, measured speed, and the widest free reach: Claude Sonnet 5. No single overall winner — the real question is not which is better but whether your workload needs the flagship, and for many teams the balanced Claude is already enough.

Split decision. GPT-5.6 Sol wins peak measured intelligence on the one independent index that scores both — 59 to 44 on the Artificial Analysis Intelligence Index version 4.1 — and leads the AA Coding Agent Index v1.3 at 66.57 against DeepSeek V4 Pro's 31.44. It also reads image input and clears Western data-residency requirements. DeepSeek V4 wins on cost and openness: roughly 34 times cheaper per output token on V4-Pro and over 100 times cheaper on V4-Flash, MIT-licensed open weights you can self-host including on Huawei Ascend, a matching 1,000,000-token context, and a larger 384K maximum output. Pick GPT-5.6 Sol for top measured capability, charted coding, multimodal input, and US-hosted compliance; pick DeepSeek V4 for cost, open weights, and self-hosting.

GPT-5.6 Sol is the winner on measured capability, and by the widest independent margin in this series: 59 against 44 on the Artificial Analysis Intelligence Index v4.1, a 15-point gap that places Kimi K2.6 below every model in the GPT-5.6 family, including the cheapest. Sol adds a charted independent AA Coding Agent Index v1.3 score of 66.57, second of 52, and roughly four times the context window (1.05M against 256K). But Kimi K2.6 wins the economics decisively: $0.95 per million input tokens and $4.00 per million output tokens against Sol’s $5.00 and $30.00 — about five times cheaper on input and exactly seven and a half times cheaper on output — on a model whose open weights you can self-host and fine-tune under a Modified MIT license. On the independent Coding Agent Index v1.3 both are charted, but through different harnesses — Sol 66.57 through Codex at max effort, Kimi K2.6 32.62 through Claude Code — and we state the harness rather than presenting the 33.95-point gap as if one agent had run both. What stays asymmetric is Moonshot AI's own SWE-bench Pro 58.6, a vendor self-reported figure we never place beside an independent one. The decision reduces to one question: is the capability you are buying capability you actually need? If the model’s ceiling constrains your work, pay for Sol. If your tasks are well-specified enough that a 15-point index gap never binds, the 7.5-times output premium is headroom you will never touch, and Kimi K2.6 is the rational default.

A clean split between a premium closed flagship and an open-weight value challenger, and we will not invent a single overall winner. GPT-5.6 Sol leads the one independent board that scores both — Artificial Analysis charts it second of ten on its Coding Agent Index v1.3 at 66.57 against GLM-5.2's 43.18 in seventh, and scores it 59 on its Intelligence Index — and it adds an ultra multi-agent reasoning tier, Programmatic Tool Calling, a marginally larger 1,050,000-token context, and a US-hosted stack. GLM-5.2 answers with price and openness: $1.40 per million input tokens and $4.40 per million output tokens against Sol's $5.00 and $30.00, roughly a seventh of the cost on output, plus a flat GLM Coding Plan from around $18 per month and MIT-licensed weights you can self-host and fine-tune. Every GLM-5.2 benchmark is Zhipu self-reported and not yet independently verified, and its production API is hosted in China; the two benchmarks both vendors publish, SWE-bench Pro at 64.6% for Sol against 62.1% for GLM-5.2 and Terminal-Bench 2.1 at 88.8% against 81.0%, are self-reported on each side and measured separately, too close and too differently run to break the tie. Best for independently validated capability, advanced reasoning, and US hosting: GPT-5.6 Sol. Best for price, open weights, and self-hosting freedom: GLM-5.2. No single overall winner — route verifiable, orchestration-heavy, compliance-bound work to GPT-5.6 Sol, and cost-sensitive, self-hosted, or fine-tuning work to GLM-5.2.

A clean split verdict across two different bets. GPT-5.6 Sol is the premium closed flagship from OpenAI; Kimi K2.7 Code is Moonshot AI's open-weight challenger. On the independent Artificial Analysis Intelligence Index v4.1 they are not close — Sol scores 59 to Kimi's 42, a seventeen-point gap on the same harness — and Sol adds a second-of-ten Coding Agent Index v1.3 placement at 66.57 (Kimi K2.7 Code is not listed there), a 1,050,000-token context to Kimi's 256K, and faster output at about 74.5 tokens per second versus 47.6. Kimi answers on price and freedom: $0.95 input and $4.00 output per million tokens against Sol's $5.00 and $30.00 — roughly five times cheaper on input and about seven and a half times cheaper on output — plus open weights under a Modified MIT license that you can download and self-host today, which Sol's closed API can never match. Best for peak capability, agentic coding, and the largest context: GPT-5.6 Sol. Best for lowest cost per token, open-weight self-hosting, and no vendor lock-in: Kimi K2.7 Code. No single overall winner — Sol buys the ceiling, Kimi buys control and a far smaller bill.

A true split verdict, capability against value, inside one model family. GPT-5.6 Sol and GPT-5.6 Terra share the same 1,050,000-token context window, the same February 16, 2026 knowledge cutoff, the same 128,000-token output ceiling, and the same Programmatic Tool Calling toolbox — so this is not a fight over features, it is a fight over how much capability you need and how much you want to pay for it. On the independent Artificial Analysis leaderboards, Sol leads: 59 to 55 on the Intelligence Index, and 66.57 to 62.28 on the Coding Agent Index v1.3, both through the Codex harness at max reasoning effort, read August 2, 2026 — the one board where the two tiers can be compared like for like, and the gap there is 4.29 points. Sol alone gets the new ultra multi-agent reasoning mode. Terra answers on price: after OpenAI's July 30, 2026 cut it costs $2.00 input and $12.00 output per million tokens — 40 percent of Sol's rate on both sides — for a model OpenAI positions as GPT-5.5-competitive. Artificial Analysis measured about $0.55 per task for Terra against about $1.04 for Sol before that cut, so Terra's real-world cost advantage is now wider than those figures show. Best for the hardest coding, long-horizon agents, and workloads where the four-point intelligence gap changes outcomes: GPT-5.6 Sol. Best for high-volume business work at a tight budget, where the gap is marginal and the price is not: GPT-5.6 Terra. No single overall winner — pick Sol for the ceiling, Terra for the bill.

A genuine split verdict between two flagships that human voters can barely tell apart. On LMArena, the one independent leaderboard that scores both under identical conditions, GPT-5.6 Sol sits at 1486 Elo and Gemini 3.1 Pro at 1485 — a single point, deep inside the statistical noise. Beyond that dead heat the two vendors publish different benchmarks, so we compare only where the ground is solid and flag every gap. Where GPT-5.6 Sol leads: it places second of ten on Artificial Analysis's Coding Agent Index v1.3 at 66.57, where Gemini 3.1 Pro sits last at 30.34; it carries a marginally larger 1,050,000-token context, double the output ceiling at 128,000 tokens against 64,000, a February 2026 knowledge cutoff against Gemini's January 2025, an ultra multi-agent reasoning mode, and general availability as of July 9, 2026. Where Gemini 3.1 Pro leads: price, decisively — $2 against $5 per million input tokens and $12 against $30 output at the standard tier, both vendor-verified at the source; native multimodal input across text, image, video, audio, and PDF where Sol takes only text and image; native Google Search and Maps grounding; and the deepest first-party distribution of any frontier vendor. On broad intelligence the two are inside the noise — Artificial Analysis Intelligence Index 59 for Sol against 57 for Gemini, measured on different index snapshots. Neither model has an independently verified SWE-bench score: Sol has not been submitted, and Gemini's 80.6 percent is self-reported on DeepMind's model card. Best for the independent coding-agent leaderboard, longest output, newest knowledge, and GA stability: GPT-5.6 Sol. Best for price, native video and audio input, and the Google ecosystem: Gemini 3.1 Pro. No single overall winner — route capability-benchmark-critical agentic coding and long-output work to GPT-5.6 Sol, and cost-sensitive, multimodal, and Google-native work to Gemini 3.1 Pro.

A split verdict between two balanced mid-tier models built for the same job — high-volume production work at a good capability-to-cost ratio — and we will not fake a single overall winner. On the one independent aggregate both appear on, GPT-5.6 Terra edges ahead: Artificial Analysis scores it 55 on the Intelligence Index against Claude Sonnet 5's 53, a two-point gap that sits inside benchmark noise. Neither model is charted on the independent Coding Agent Index v1.3. Terra carries the only published per-task cost figure (about $0.55 to run the Intelligence Index), a marginally larger 1,050,000-token context against Sonnet 5's 1,000,000, and a newer February 16, 2026 knowledge cutoff versus January 2026. Sonnet 5 answers on price and reach: its rate of $2 per million input tokens and $10 per million output matches Terra's $2.00 input and undercuts its $12 output, it is the default model on the free and Pro plans of Claude.ai where Terra is API-, Codex-, and Work-tier only, and Artificial Analysis measures it at 79 output tokens per second where it publishes no Terra figure. One honest caveat levels the price story: Anthropic notes Sonnet 5's newer tokenizer counts roughly 30 percent more tokens for the same text, which narrows the effective-cost gap. Neither model has been submitted to independent SWE-bench Verified. Best for the independent intelligence and coding indices, per-task economics, longest context, and newest knowledge: GPT-5.6 Terra. Best for the lowest price and the widest consumer reach: Claude Sonnet 5. No single overall winner — route capability-benchmark-sensitive and long-context work to Terra, and price-sensitive or consumer-facing work to Sonnet 5, and test both on your own prompts before committing.

GPT-5.6 Sol is the better model at the same price. It leads GPT-5.5 on the Artificial Analysis Intelligence Index (59 versus 55), leads the AA Coding Agent Index v1.3 at both effort settings the two share on the Codex harness — 65.09 to 61.49 at xhigh, 60.61 to 54.36 at medium — adds the ultra multi-agent reasoning tier and programmatic tool calling, and ships a fresher February 2026 knowledge cutoff — all for the identical $5.00 input and $30.00 output per million tokens, with the same 1,050,000-token context window. Upgrade to Sol for new agentic and coding-heavy builds, long-horizon automation, and anything that benefits from deeper reasoning. The case for staying on GPT-5.5 is narrower but real: it is the only one of the two with a public, independently verified SWE-bench Verified score (82.6 percent on vals.ai, where Sol has not been submitted) and it carries months of production track record. GPT-5.5 is not deprecated — Sol is an addition to OpenAI's lineup, not a forced replacement, so migrate on your own schedule.

A split verdict, and we will not fake a single overall winner. Elon Musk positions Grok 4.5 as "Opus-class, much faster" at less than half the price, and on the independent Artificial Analysis Coding Agent Index v1.3 the claim has partial support: Grok 4.5 scores 64.4 through Grok Build at less than half the token price, against 56.70 for Claude Opus 4.8 through Claude Code at the same high effort. But "Opus-class" is not yet independently confirmed against Opus 4.8 on verified coding: Grok 4.5 is only two days old and does not yet appear on vals.ai's independent SWE-bench Verified leaderboard, where Claude Opus 4.8 is verified at 88.6 percent — so the independently verified coding edge is Opus 4.8's. Grok 4.5 is also dramatically cheaper, at $2 per million input tokens and $6 per million output against Opus 4.8's $5 and $25, both vendor-verified, and Artificial Analysis measures its cost per task at about $2.49 on its Intelligence Index run, low in the frontier tier. The "Opus-class" label does not extend across the board. On the Artificial Analysis Intelligence Index, Grok 4.5 scores 54 and ranks fourth in the frontier field, while Opus 4.8 reads between 56 and 61 depending on configuration — higher at both ends. On reliability, Artificial Analysis's AA-Omniscience test flags a 54 percent hallucination rate for Grok 4.5, and Opus 4.8 carries a 1,000,000-token context window to Grok's 500,000. One hard practical fact sits outside the benchmarks: Grok 4.5 is not available in the EU under the AI Act, while Opus 4.8 is. Best for the lowest token price, coding value per dollar, and vendor-stated speed on non-EU work: Grok 4.5. Best for independently verified coding, measured intelligence, reliability, long context, and EU availability: Claude Opus 4.8. "Opus-class" is credible on coding value and price, but unconfirmed on independently verified coding and unproven on general intelligence and reliability — so route accordingly rather than crown one model.

A split verdict between two frontier flagships, and we will not fake a single overall winner. GPT-5.6 Sol and Claude Opus 4.8 charge the same $5 per million input tokens; Opus 4.8 is cheaper on output at $25 against $30 per million. On independent leaderboards the two split cleanly: Artificial Analysis charts GPT-5.6 Sol second of 52 on its Coding Agent Index v1.3 at 66.57, against 60.54 for Opus 4.8 at the same max effort in the Claude Code harness, while Claude Opus 4.8 posts an independently verified 88.6 percent on the vals.ai SWE-bench Verified suite, a leaderboard GPT-5.6 Sol has not been submitted to. Sol carries a marginally larger 1,050,000-token context and a February 2026 knowledge cutoff, and is measured cheaper per task at about $1.04 on Artificial Analysis's Intelligence Index run. Opus 4.8 carries cheaper output tokens, the stronger documented computer-use record, and the longer public track record. On broad intelligence the two are inside the noise — Intelligence Index 59 for Sol against a configuration-dependent 56 to 61.4 for Opus 4.8 — and on LMArena human preference they sit at 1486 to 1482. Best for the independent coding-agent leaderboard, longest context, newest knowledge, and per-task economics: GPT-5.6 Sol. Best for independently verified SWE-bench, cheaper output tokens, and computer-use maturity: Claude Opus 4.8. No single overall winner — route capability-benchmark-critical agentic-coding and long-context work to GPT-5.6 Sol, and independently verifiable, output-heavy, or browser-agent work to Claude Opus 4.8.

A split verdict between capability and raw token price, and we will not fake a single overall winner. Both models reached general availability on the same day, July 9, 2026. On the independent leaderboards where both appear, GPT-5.6 Sol is the measured leader: Artificial Analysis scores it 59 on the Intelligence Index against Grok 4.5's 54, charts it second of 52 on the Coding Agent Index v1.3 at 66.57 to Grok's 64.44 in sixth, and — counterintuitively — measures it cheaper to run per task, about $1.04 versus $2.49 on the same Intelligence Index evaluation. Sol also carries more than double the context (1,050,000 versus 500,000 tokens) and adds an ultra multi-agent reasoning mode Grok does not offer. Grok 4.5 answers on the rate card: $2 per million input tokens and $6 per million output against Sol's $5 and $30 — less than half on both sides, verified on SpaceXAI's documentation — and SpaceXAI positions it as 'Opus-class, much faster.' Two caveats weigh on Grok: Artificial Analysis's AA-Omniscience test flags a 54 percent hallucination rate, and the model is not available in the EU under the AI Act. Best for measured capability, long context, per-task economics, multi-agent work, and EU teams: GPT-5.6 Sol. Best for the lowest token rate card and vendor-stated speed on high-volume, non-EU workloads: Grok 4.5. No single overall winner — route capability-critical and EU work to Sol, cost-sensitive high-volume work to Grok 4.5.

A genuine split verdict, capability against value. On the independent leaderboards where both models appear, Claude Fable 5 is the measured capability leader of this pair: at the review's mid-July 2026 reading, Artificial Analysis ranked it No.1 on the Intelligence Index at 60 (one point above Sol's 59) and it held the No.1 LMArena Elo at 1509 against Sol's 1486 at No.8 — both No.1 spots have since passed to Claude Opus 5, with Fable 5 at 59.9 to Sol's 58.9 on the index as of August 4, 2026 — and on the independently run SWE-bench Verified suite it posts 95 percent while GPT-5.6 Sol has not been submitted at all. GPT-5.6 Sol answers on economics and agentic throughput: it costs half of Fable 5 at the sticker ($5 versus $10 input, $30 versus $50 output per million tokens, both vendor-verified), Artificial Analysis measures its cost to run the Intelligence Index at $1.86 per task versus $3.15 for Fable 5 (measured August 2, 2026), it ranks second on the AA Coding Agent Index v1.3 at 66.57 against Fable 5's 65.85 (Claude Code with Opus 5 leads at 66.74), and it adds an ultra multi-agent reasoning mode Fable 5 does not offer. Best for peak measured intelligence, human preference, and independently verified coding: Claude Fable 5. Best for price, cost per task, agentic-coding-index throughput, and multi-agent workloads: GPT-5.6 Sol. No single overall winner — route frontier reasoning to Fable 5 and high-volume agentic work to Sol.

This is a genuine split with no single overall winner. Claude Sonnet 5 and Gemini 3.1 Pro lead in different lanes and barely meet on the same benchmark. Where they do — SWE-bench Pro — Sonnet 5 is ahead, 63.2% to 54.2%, and it backs that with a documented 81.2% OSWorld-Verified computer-use score and generally available, production-stable shipping. Gemini 3.1 Pro owns the reasoning-and-multimodal lane just as clearly: 94.3% on GPQA Diamond and 77.1% on ARC-AGI-2 are scores Sonnet 5 does not publish, and native handling of audio, video, and PDFs is a capability Sonnet 5 lacks — with the caveat that Gemini 3.1 Pro is still in preview months after its February 2026 release. Best for coding, computer use, and maturity: Claude Sonnet 5. Best for reasoning and multimodal input: Gemini 3.1 Pro. Many teams should run a split stack.

There is no overall winner: Claude Sonnet 5 and DeepSeek V4 are optimized for different priorities. Sonnet 5 wins the one shared benchmark, SWE-bench Pro (63.2 percent versus 55.4 percent), and is the only one of the two with documented computer use, vision, and a public safety system card. DeepSeek V4 counters with open MIT-licensed weights you can self-host, a matching one-million-token context window, and pricing that runs roughly four to five times cheaper on input and about eleven times cheaper on output. Pick Sonnet 5 for documented capability, multimodal input, or governance sign-off; pick DeepSeek V4 when token cost, open weights, self-hosting, or avoiding lock-in dominate and a text-only, China-hosted or self-hosted model works for you.

There is no overall winner: Claude Sonnet 5 and Qwen 3.6 are optimized for different priorities. Sonnet 5 wins the one shared benchmark, SWE-bench Pro (63.2 percent versus the open-weight Qwen3.6-27B's 53.5 percent), and is the only one of the two with a documented computer-use score (OSWorld-Verified 81.2) and a public safety system card. Qwen 3.6 counters with Apache 2.0 open weights (27B and 35B-A3B) you can self-host for free, a genuinely multimodal open model that reads image and video, a near-parity context window at the family level, and pricing that runs several times cheaper on its managed Plus tier and nothing per token on its open weights. Pick Sonnet 5 for documented capability, computer use, or governance sign-off; pick Qwen 3.6 when open weights, price, self-hosting, modality breadth, or avoiding lock-in dominate and you can self-host or accept a China-hosted managed API.

Claude Sonnet 5 is our narrow overall winner on demonstrated capability, transparency, and distribution: it reports 63.2 percent on SWE-bench Pro and 81.2 percent on OSWorld-Verified computer use, publishes those results on public standardized benchmarks, ships a documented system card, offers a 1-million-token context, and runs as the default model on the free and Pro plans of Claude.ai so you can test the exact model before paying for the API. Kimi K2.7 Code wins price and openness decisively — roughly $0.95 per million input tokens and $4.00 output versus Sonnet 5's $2 input and $10 output, with open weights under a Modified MIT license you can self-host and inspect. The catch is that the two models share no public benchmark on the same scale: Sonnet 5 reports SWE-bench Pro and OSWorld while K2.7 reports only Moonshot's own suites, with independent results still pending. So the decision comes down to how much you value demonstrated, third-party-shaped capability and safety documentation versus token economics and the freedom to self-host: cost-sensitive and control-conscious teams should lean K2.7, while teams that need documented capability, computer use, long context, and governance sign-off should lean Claude Sonnet 5.

This one is close on capability and split on everything else. Claude Sonnet 5 scores 63.2% on SWE-bench Pro (Anthropic system card) and GLM-5.2 scores 62.1% (Zhipu self-reported) — about a single point apart, close enough to call a near dead heat on agentic coding. The real fork is the distribution model. Pick Claude Sonnet 5 for the mature ecosystem, the documented safety posture, and zero-friction support: it is the default model on the Free and Pro plans of Claude, ships in Claude Code and the Claude API, and comes with a published system card. Pick GLM-5.2 for price, openness, and control: MIT open weights you can self-host, no regional lock-in, and $1.40 per million input tokens and $4.40 per million output tokens against Sonnet 5's $2 and $10. There is no single winner because the two models optimize for different things — Sonnet 5 sells you a polished, supported, closed product, and GLM-5.2 sells you a cheaper, open, self-hostable one at nearly the same coding score. Choose the distribution model your team can live with, because the capability gap will not decide it for you.

Claude Sonnet 5 is the narrow overall winner on capability and distribution: it leads the one clean shared benchmark, SWE-bench Pro, at 63.2% versus Kimi K2.6’s 58.6% (a 4.6-point edge), is the only one of the two with a published computer-use score (81.2% OSWorld-Verified), carries a documented safety profile, and plugs into the mature Claude ecosystem. But Kimi K2.6 wins price and openness decisively — roughly two to three times cheaper per token ($0.95 input / $4.00 output per 1M) with open weights under Modified MIT you can self-host and an Agent Swarm scaling to 300 sub-agents. The choice comes down to how large a capability gap justifies giving up openness and paying more: a 4.6-point edge is real but modest, so cost-sensitive and control-conscious teams should lean Kimi K2.6, while teams that value documented capability, computer use, safety, and ecosystem should lean Claude Sonnet 5.

Claude Sonnet 5 is the narrow overall winner and the better value: as Anthropic's mid-tier model it edges OpenAI's flagship GPT-5.5 on the one benchmark both report on the same scale — SWE-bench Pro, 63.2% versus 58.6% — while costing roughly half to a third as much per token and publishing an 81.2% OSWorld-Verified computer-use score GPT-5.5 does not match. GPT-5.5 is not beaten across the board, though: it holds the SWE-bench Verified crown at an OpenAI-reported 88.7% (first place at launch), topped the Artificial Analysis Intelligence Index, and offers a five-level reasoning-effort scale plus the broader ChatGPT and Codex ecosystem. Because the two lead on different coding benchmarks, the decision comes down to which test you trust and whether a mid-tier price outweighs a flagship's headline number: most value-conscious teams should lean Claude Sonnet 5, while teams anchored to the Verified crown, reasoning granularity, or the OpenAI ecosystem should pick GPT-5.5.

Upgrade to Claude Sonnet 5 unless you have a validated, pinned deployment you are not ready to touch. This is not a close call and it is not a tie — it is a generational upgrade that costs less than the model it replaces. Sonnet 5 scores 63.2% on SWE-bench Pro against Sonnet 4.6's 58.1% (a 5.1-point gain) and 81.2% on OSWorld-Verified against 78.5% (a 2.7-point gain), improves on hallucination, sycophancy, and prompt-injection resistance with cyber safeguards on by default, and is the new default model on the Free and Pro plans of Claude.ai. It costs less than Sonnet 4.6, at $2 per million input tokens and $10 per million output tokens against $3 and $15. Because both models share the same Messages API and SDKs, switching is a one-line model-string change. The only reason to stay on Sonnet 4.6 is a pinned, validated production deployment where the cost of re-qualifying the model outweighs the upgrade — a real but narrow case. For everyone else, Sonnet 5 is the model to run.

Default to Claude Sonnet 5, and escalate to Claude Opus 4.8 by exception. For the roughly four-in-five production tasks that are high-volume, latency-sensitive, or cost-bound, Sonnet 5 is the correct default: it scores 63.2% on SWE-bench Pro against Opus 4.8's 69.2% — about 91% of the capability — at $2 per million input tokens and $10 per million output tokens, roughly 40% of the flagship's price. Reserve Opus 4.8 for the hardest long-horizon problems, the most safety-sensitive agentic deployments, and the coordinator seat in multi-agent systems, where its six-point SWE-bench Pro lead and lower rate of misaligned behavior are worth paying 2.5 times for. This is not a tie of equals; it is a division of labor — Opus 4.8 is the more capable and safer model, Sonnet 5 is the smarter default for most work, and the best architecture usually uses both.

Split verdict by use case. GPT-5.5 wins raw capability and agentic coding — it leads the Artificial Analysis Intelligence Index roughly 55 to 44 and SWE-bench Verified roughly 88.7 percent to 80.6 percent. DeepSeek V4 wins cost, open weights, and self-hosting by a landslide: V4-Flash output at 0.28 dollars per million tokens is over 100 times cheaper than GPT-5.5 output at 30 dollars. No single overall winner — pick GPT-5.5 for the strongest closed model and US-hosted compliance, DeepSeek V4 for cost, sovereignty, and downloadable MIT-licensed weights.

Split verdict by category, with no single overall winner because the trade-off is genuine. On the one yardstick that scores both the same way — the Artificial Analysis Intelligence Index — GPT-5.5 sits in the frontier tier and leads, while Grok 4.3 lands in the upper-mid tier, and GPT-5.5 also leads the GDPval-AA agentic-accuracy suite (where Grok 4.3 ranks number two overall), so GPT-5.5 is the smarter, more capable model on the comparable evidence. Grok 4.3 is roughly four times cheaper on input and twelve times cheaper on output ($1.25 input and $2.50 output versus $5 input and $30 output per million tokens, both verified), and it adds native PPTX, PDF, and XLSX file generation plus real-time access to X Corp data that GPT-5.5 does not offer. Both accept text and image (vision) input and neither accepts native video. Both share a 1 million token context window, so size is not the tiebreaker. Best for peak intelligence, agentic accuracy, and the OpenAI ecosystem: GPT-5.5. Best for cost, real-time data, and native file output: Grok 4.3. All benchmark numbers are vendor- or evaluator-reported; only pricing is fetch-verified directly from each vendor.

Split verdict, not a hedge. Seedance 2.0 wins on raw single-shot photoreal quality, real-world physics, native one-pass synchronized audio, the richest multimodal reference stack in the tier (up to 9 images, 3 videos and 3 audio clips per request) and pay-per-second flexibility. Kling 3 Omni wins on multi-shot storyboard control, reference video character lock, about 93 percent character consistency across cuts, native 4K resolution, multilingual lip-sync across five languages, the Omni Elements library and predictable monthly billing. Pick Seedance 2.0 if you're producing single-shot photoreal clips through an API and paying per second. Pick Kling 3 Omni Pro if you're producing multi-shot episodic content with a recurring character cast at 4K. Teams shipping the highest-quality Chinese-model AI video in 2026 are increasingly running both — Seedance 2.0 for raw single-shot takes, Kling 3 Omni for the multi-shot narrative layer.

Split by budget. Claude Opus 4.8 is the more capable and reliable model — it leads every verifiable standard public benchmark (88.6% SWE-bench Verified, 69.2% SWE-bench Pro, 84% Online-Mind2Web) and was the steadier agent in our hands-on testing, with a 1M-token context window Kimi cannot match. Kimi K2.7 Code is the smarter buy on value: open weights under a Modified MIT license you can self-host, at roughly one-sixth the output cost, delivering about 85-90% of Opus 4.8's quality on our tasks. Choose Opus 4.8 when correctness, agentic autonomy, and long context are mission-critical and budget allows; choose K2.7 when cost, openness, or data sovereignty drive the decision.

DeepSeek V4 is the overall winner for most coding teams in 2026: it is the only model here with independently verified frontier scores (80.6% SWE-bench Verified, 93.5% LiveCodeBench), it has a 1M context window, a clean MIT license, and a V4-Flash tier roughly 7x cheaper on input and 14x cheaper on output than Kimi K2.7 Code. K2.7 wins on agentic tool-use (its strongest self-reported category), native multimodal vision via MoonViT, and reasoning-token efficiency — making it the better fit for MCP-heavy agent fleets and vision-in-code workflows, but its lack of any independent benchmark keeps it behind DeepSeek V4 on demonstrated capability.

There is no single winner — these two answer different questions. GLM-5.2 wins decisively on cost (roughly 6.8x cheaper on output tokens), open-weight MIT licensing, and self-hosting freedom, while edging GPT-5.5 on the vendor-published SWE-bench Pro number (62.1 vs 58.6). GPT-5.5 wins on ecosystem maturity, a public system card for compliance, and the least operational burden. Pick GLM-5.2 to save money and own your stack; pick GPT-5.5 to buy polish, support, and a paper trail — and note that some organizations cannot use a Chinese-lab model regardless of quality.

GPT-5.5 is the stronger model; Kimi K2.7 Code is the better value. Best raw capability, agentic breadth and 1M context: GPT-5.5. Best cost per token, open-weight self-hosting and budget vision-in-code: Kimi K2.7 Code. A split, attributed verdict — most teams will use both.

DeepSeek V4 takes the overall pick for most teams because its price is decisive at volume — V4-Flash runs at roughly one-tenth the input cost and one-sixteenth the output cost of GLM-5.2 — and it backs that with the broadest verified coding coverage (80.6 SWE-bench Verified, 93.5 LiveCodeBench). But this is a close, split decision, not a blowout: GLM-5.2 actually leads on the one suite both vendors report, SWE-bench Pro (62.1 vs 55.4), essentially ties on GPQA Diamond (91.2 vs 90.1), and wins on predictable flat-rate billing through the GLM Coding Plan plus a deeper coding-agent ecosystem. Pick DeepSeek V4 when token volume and raw cost dominate; pick GLM-5.2 when you want the strongest reported agentic coding capability, a flat monthly bill, and tight Claude Code / Cline integration. All benchmark figures here are vendor self-reported and not yet independently verified.

Claude Fable 5 wins this head-to-head on capability — official specs (a 128K output ceiling double Gemini's, a 1M context window, general availability since June 9, 2026) plus an early-reported 26-point SWE-Bench Pro lead (80.3% vs 54.2%, not independently verified) make it the most capable model of the pair. Gemini 3.1 Pro keeps the value crown: vendor-confirmed pricing at $2 input and $12 output per million tokens up to 200K context (five times cheaper on input), native audio and video multimodality, and the only officially documented reasoning score in the matchup (GPQA Diamond 94.3%). Best for capability-critical agentic work: Claude Fable 5. Best for cost-sensitive scale and multimodal pipelines: Gemini 3.1 Pro.

A true split verdict: capability versus value. On SWE-bench Pro — the one agentic coding benchmark with same-harness figures for both models — early third-party coverage reports 80.3% for Claude Fable 5 versus OpenAI's reported 58.6% for GPT-5.5, a 21.7-point gap, and in our first 48 hours running both side-by-side on the same production tasks, Fable 5 needed fewer human interventions on the hardest multi-file and long-horizon work. But GPT-5.5 wins price decisively — $5 input and $30 output per million tokens versus $10 and $50, both fetch-verified June 10, 2026 — posts the strongest published SWE-bench Verified figure (88.7%, a different benchmark where Fable 5 has no number), offers reasoning that can be switched fully off, and avoids Fable 5's mandatory 30-day Covered Model retention. Best for the hardest agentic work and production failure handling (refusals as HTTP 200 with auto-fallback to Opus 4.8): Claude Fable 5. Best for price, compliance flexibility, and ecosystem: GPT-5.5. Fable 5's benchmark lead is early and not independently reproduced; we scope it accordingly. No single overall winner — route frontier tasks to Fable 5 and volume to GPT-5.5.

Split verdict by workload, with the widest margins we have measured between two frontier-tier models. The capability crown goes to Claude Fable 5: it topped the independent Artificial Analysis Intelligence Index at the June 2026 reading (second behind Claude Opus 5 since July 24, 2026) and sits well ahead of Grok 4.3, its reported 80.3 percent on SWE-Bench Pro is the highest of any generally available model (early reported figure, flagged as such), and in our hands-on production use it needed fewer human interventions than any model we have run. The cost-and-speed crown goes to Grok 4.3 by an enormous margin: $1.25 input and $2.50 output per million tokens against $10 and $50 for Fable 5 — eight times cheaper on input, twenty times cheaper on output, both fetch-verified from vendor docs — plus a markedly higher output speed on the same Artificial Analysis harness, native PPTX/PDF/XLSX file generation, and real-time data from X that Fable 5 does not offer. Both models accept image input for vision and neither accepts video. Context window is a tie at one million tokens. We did not crown a single overall winner: best for raw capability, agentic coding, and reliability-critical pipelines is Claude Fable 5; best for cost, output speed, native file generation, and real-time data is Grok 4.3. Many teams should run both and route by task, using Fable 5's 90 percent prompt-caching discount to keep the expensive side affordable.

Split verdict by use case — the widest capability-versus-cost spread we have measured between two current frontier models. Claude Fable 5 was the strongest widely available model at the review's June 2026 reading: number one of 152 on the Artificial Analysis Intelligence Index then, 65 versus 52 for DeepSeek V4-Pro on that reading (59.9 to 44.3 on v4.1 as of August 4, 2026, second behind Claude Opus 5), roughly 80.3% on SWE-bench Pro per early third-party trackers, and 1932 on GDPval-AA, with a designed-in Opus 4.8 fallback and US hosting. DeepSeek V4 wins everything economic: output at $0.28 per million tokens (V4-Flash) is roughly 178 times cheaper than Fable 5's $50, the weights ship under an MIT license, output reaches 384K tokens, and self-hosting offers data control no closed API can match — Fable 5 is a Covered Model with mandatory 30-day retention. No single overall winner: best for peak capability, agentic coding, and managed US compliance is Claude Fable 5; best for cost, open weights, and self-hosted sovereignty is DeepSeek V4. Only the pricing is fetch-verified; benchmarks are attributed.

Claude Fable 5 wins on raw capability: it is Anthropic's most capable widely released model, a deliberate tier above Opus, and every early signal — including third-party SWE-bench Pro runs around 80.3 percent versus the 69.2 percent Anthropic published for Opus 4.8 — points the same way. Claude Opus 4.8 wins on value at exactly half the price ($5 input and $25 output per million tokens versus $10 and $50), keeps thinking optional, offers a 2.5x Fast Mode at Fable 5's standard price, remains compatible with zero-data-retention agreements, and is the recommended fallback when Fable 5's safety classifiers refuse a request. Run Opus 4.8 as the default, route the hardest agentic work to Fable 5 where the premium provably pays for itself, and keep Opus 4.8 wired in as Fable 5's safety net.

Claude Opus 4.8 is the better choice for any new work, and it costs exactly the same as Opus 4.7 — $5 per million input tokens and $25 per million output tokens. On Anthropic's own comparison table it wins all three head-to-head benchmarks (SWE-bench Verified 88.6% vs 87.6%, SWE-bench Pro 69.2% vs 64.3%, Terminal-Bench 2.1 74.6% vs 66.1%), and it adds Fast Mode plus a roughly 4x lower rate of letting flaws slip past its own code review. Be honest about the size of the gains: the SWE-bench Verified jump is only one point, so it is marginal. The real upgrade is on SWE-bench Pro (+4.9), Terminal-Bench (+8.5), agentic reliability, and the new Fast Mode option. Because the price is identical, there is no reason to start new projects on 4.7. The only case for staying on Opus 4.7 is if you have pinned it for production stability and your pipeline is validated against that exact version. Bottom line: Opus 4.8 for any new work — same price, better across the board; stay on 4.7 only if you have pinned it for production stability.

Split decision. For agentic software engineering, Claude Opus 4.8 leads on the one benchmark both vendors report (SWE-bench Verified 88.6% vs 80.6%) and is generally available today — but that 88.6% is press-relayed and not independently verified. For most other teams, Gemini 3.1 Pro is the better overall value: it wins the dimensions we could confirm with hard evidence — a 1M-token context window, top-tier published reasoning scores (GPQA Diamond 94.3%, ARC-AGI-2 77.1%), and vendor-confirmed pricing roughly 2.5 times cheaper than Opus 4.8. Best for agentic coding: Opus 4.8. Best for reasoning, context, and price: Gemini 3.1 Pro.

Split verdict by category, tilted toward Claude Opus 4.8 on the dimensions we can verify like-for-like. On SWE-bench Pro — the one coding benchmark both vendors report the same way — Anthropic reports 69.2% for Opus 4.8 versus OpenAI's reported 58.6% for GPT-5.5, so Opus 4.8 leads agentic coding by about 10 points, and it is about 17% cheaper on output ($25 versus $30 per million tokens, verified pricing). GPT-5.5 wins on declared context window (1,000,000 tokens versus undeclared) and on published web and economic-value benchmarks (BrowseComp Pro 90.1%, GDPval 84.9%). We did not crown a single overall winner: Terminal-Bench figures were measured on different versions (2.1 versus 2.0) and are not comparable, Anthropic's computer-use lead rests on a single reported figure with no verified GPT-5.5 counterpart, and we ran no controlled head-to-head. Best for agentic coding and output cost: Claude Opus 4.8. Best for large declared context and web tasks: GPT-5.5. All benchmark numbers are vendor-reported and press-relayed; only Opus 4.8 pricing is fetch-verified.

Leonardo.ai wins overall (8.8 out of 10) for image-first plus video plus 3D plus API at $12 per month Apprentice. Higgsfield AI wins for cinematic short-form video and viral social ads with 70+ camera presets, Soul ID and the Audio suite at $9 to $99 per month.

Split verdict — Devin wins the autonomous coding agent lane (3 of 4 tests, Self-QA via Devin Review, US enterprise compliance), Manus wins the autonomous generalist lane (Data ETL test, GAIA SOTA 86.5 percent, Linux sandbox + CodeAct). Run both at $40 per month combined if your backlog is mixed.

Cursor 3.3 edges Zed 1.1.8 overall (9.5 vs 9.0 on our 2026 rubric) thanks to a deeper agent stack — Composer 2, Background Agents, Bugbot — and the most mature codebase indexing on the market. But Zed wins outright on speed (sub-second cold boot, 120 FPS GPU rendering), native multiplayer, open-source posture, and price ($10 vs $20 per month Pro). Pick Cursor for solo agent-heavy workflows on big repos. Pick Zed for pair-programming teams, perf obsessives, and anyone allergic to vendor lock-in.

Tie by use case. GPT Image 2 wins on text rendering (99% Latin accuracy), layout reasoning, low-cost prototyping, and streaming UI. Nano Banana Pro wins on photorealism, multi-turn editing, 4K batch cost, and Google Workspace integration. Pick GPT Image 2 for text-heavy production; pick Nano Banana Pro for portrait, product, and lifestyle photography.

Split verdict, not a hedge. Veo 3.1 wins on raw photorealism (9.4 vs 8.7 on our internal benchmark), native synced audio in one pass, pay-per-second flexibility, SynthID provenance and API-first developer ergonomics. Kling 3 Omni wins on multi-shot storyboard control, reference video character lock, 93 percent character consistency across cuts, 12 named camera moves, multilingual lip-sync, Omni Elements library and predictable monthly billing. Pick Veo 3.1 if you're producing one-shot photoreal hero footage or API-first product workflows. Pick Kling 3 Omni Pro if you're producing multi-shot episodic content with a recurring character cast. Production teams shipping the highest-quality 2026 AI video are increasingly running both — Veo 3.1 for raw photoreal hero takes, Kling 3 Omni for the multi-shot narrative layer.

We tested both Eleven Music and Suno AI side-by-side for 3 weeks across 60+ generated tracks, and Suno AI wins overall as the more mature, full-song generator with 8-minute outputs, voice cloning, and Studio mode for stem editing. Eleven Music — launched April 2026 — produces remarkably clean 44.1kHz studio audio with native multilingual vocals in 11 languages and is half the price for hobbyists, but its 5-minute cap and lack of stem-separation Studio limit pro workflows. Pick Suno if you ship full songs (musicians, indie producers, podcasters needing intros). Pick Eleven Music if you produce short-form content in non-English languages and live inside the ElevenLabs ecosystem already.

ElevenLabs is the winner for content creators, podcasters, dubbing studios and audiobook publishers thanks to Eleven v3 expressive tags, 10,000-voice community library and 70+ language dubbing. Cartesia wins for real-time voice agents and developer infrastructure with Sonic-3 hitting 90ms time-to-first-audio (roughly 2-4x faster than ElevenLabs) at one fifth of the per-character cost. Same 9.0 overall score, different sweet spots: pick ElevenLabs for studio quality and breadth, pick Cartesia for low-latency agents and budget-conscious dev teams. Solo creators start at $6 per month on ElevenLabs Starter or $4 per month on Cartesia Pro.

ElevenLabs wins by default in May 2026 because Play.ht no longer exists. Meta acquired Play.ht on July 12, 2025, killed the API on July 26, stopped new sign-ups in August, and permanently shut the service down on December 31, 2025. ElevenLabs is the only operational platform of the two and now leads the TTS Arena at Elo 1196 with Eleven v3, while Play.ht voice clones, audio libraries, and projects were all deleted. For ex-Play.ht users migrating in 2026, ElevenLabs Creator at $11 per month is the closest functional and pricing equivalent to the old Play.ht Creator plan ($31.20 per month annual), with better voice quality and 70+ language support. There is no realistic scenario in May 2026 where Play.ht is the right answer.

ElevenLabs wins overall in 2026 — cleaner voice quality on emotional and conversational content, accessible voice cloning from $11 per month, 1,000+ voices in 32 languages, and a real-time streaming API hitting sub-500ms latency for production agents. Murf AI is the better pick for one specific persona: corporate L&D, marketing, and presentation teams who need a polished WYSIWYG editor with team collaboration and 200+ studio-clean voices in 35+ languages. If you ship narrated podcasts, audiobooks, conversational AI agents, or character voices, pick ElevenLabs. If you ship narrated explainer videos, training modules, or slide decks with multiple stakeholders, pick Murf. For pure cost per finished hour of speech, ElevenLabs is roughly 2.6x cheaper at the entry tier.

Claude Opus 4.7 wins overall (9.4 vs 7.4) for production engineering — 93% SWE-bench Verified, single-voice clarity, enterprise platform depth across AWS Bedrock, Vertex AI, and Foundry. Grok 4.20 wins on three specific dimensions: 2M-token context (2x Opus), API price (4x cheaper input, 10x cheaper output at base rates), and live X data ingestion through the Harper agent. Pick Opus for shipping code, building agents, or running long autonomous workflows. Pick Grok for ultra-long-context tasks, real-time X research, or high-volume budget-constrained API workloads. The choice is workflow-driven, not category-driven.

Claude Opus 4.7 wins overall (9.4 vs 9.0) for production agentic coding, self-verification, and SLA stability. Gemini 3.1 Pro Preview wins on raw reasoning (94.3% GPQA Diamond, 77.1% ARC-AGI-2), native multimodal video/audio input, and price ($2 input vs $5). Pick Opus 4.7 if you ship production agents and need predictable model availability. Pick Gemini 3.1 Pro Preview for research-heavy reasoning, multimodal extraction, or budget-sensitive workloads — but accept Preview-status risk: Google shut down Gemini 3 Pro Preview on March 9, 2026 with forced migration. Same can happen here.

Claude Opus 4.7 wins overall (9.4 vs 8.6) for production engineering — 93% SWE-bench Verified, single-voice clarity, enterprise platform depth across AWS Bedrock, Vertex AI, and Foundry, and reliability on 40+ tool-call autonomous runs. Grok 4.3 is the best second-tier-priced flagship of 2026 with three category-defining wins: native PPTX/PDF/XLSX file generation and the cheapest published frontier API ($1.25/$2.50 per million tokens — 4x and 10x cheaper than Opus 4.7). Pick Opus for shipping code, building agents, or running long autonomous workflows. Pick Grok 4.3 for slide generation, real-time X data, or cost-sensitive high-volume API workflows. The choice is workflow-driven, not category-driven.

Split verdict, not a hedge. Runway Gen-4.5 wins on creative tooling (Director Mode, Act-One, integrated timeline), cinematic motion (1216 Elo vs 1208), character consistency, predictable monthly billing and free-tier access. Veo 3.1 wins on raw photorealism (9.5/10 vs 8.5/10), native synced audio, 4K resolution, native 9:16 vertical and pay-per-second flexibility. If you're a video editor or filmmaker, pick Runway. If you're a developer or producing photoreal documentary or dialogue-heavy content, pick Veo 3.1. Production teams shipping the highest-quality 2026 AI video are increasingly running both — Veo for raw shots and audio, Runway for the editorial layer.

Claude Opus 4.7 (9.4/10) edges GPT-5.5 (8.6/10) overall on our production agentic coding workflows after two weeks side-by-side. Opus 4.7 wins agentic coding (93% SWE-bench Verified vs ~75%), frontier reasoning (46.9% on Humanity's Last Exam vs 41.4%), output pricing ($25 vs $30 per 1M tokens), and ships task budgets to cap autonomous run costs — a unique cost-control feature in April 2026. GPT-5.5 wins ecosystem breadth (Codex on every ChatGPT plan, Responses API, mature MCP and structured outputs), reasoning effort granularity (5 levels), and a narrow lead on the aggregate Artificial Analysis Intelligence Index. Pick Opus 4.7 for unattended autonomous coding and reasoning-heavy work; pick GPT-5.5 if you live in the OpenAI ecosystem or need granular reasoning effort control.

We use Claude Code and Cursor every single day to ship ThePlanetTools.ai. Claude Code wins overall on autonomous work, 1M-token context and code quality. Cursor wins everything visual — Design Mode, Cmd+K, tab completions. Both are $20/month. The real answer: run both, let the work decide which one opens.

We've used Claude Code daily for 14 months and built ThePlanetTools.ai with it. Then OpenAI relaunched Codex in February 2026 — we gave it 60 days of real work. Here's which terminal AI coding agent actually wins in 2026, category by category, with benchmarks, pricing and real-world results.

ChatGPT (8.5/10, $20/month) edges out Grok (8.2/10, $30/month) as the better all-around AI assistant in 2026 — stronger coding reliability (74.9% vs 43.6% on SWE-bench), more polished writing, and a far broader integration ecosystem at a lower price point. Choose Grok when you need real-time X data, top-tier math performance (95% AIME 2025), or an AI with genuine personality that won't hedge every answer into oblivion.

HeyGen wins for most users in 2026: Avatar IV delivers the most realistic AI avatars on the market, unlimited video on paid plans crushes Synthesia's strict minute caps, and 175+ language support with unlimited dubbing makes it the better value at $29/month. Synthesia is the right call for regulated enterprises — it's the only AI video platform holding ISO 42001, ISO 27001, SOC 2 Type II, and GDPR certifications simultaneously, making it indispensable for Fortune 500 compliance workflows.

Midjourney wins for raw artistic quality, stylistic depth, and creative output — it remains the gold standard for concept artists, illustrators, and creatives who prioritize visual craft. Adobe Firefly is the smarter choice for designers already in the Adobe ecosystem who need commercially safe, indemnified assets they can drop straight into client work. If you just want the best-looking images, go Midjourney. If you need legal peace of mind and Photoshop integration, Firefly is your tool.

Cursor wins overall for senior developers working on large, complex, multi-file projects — Composer 2's full-repo indexing, parallel subagents, and 1M-token Max Mode consistently outperform Copilot on agentic refactor work (28 min vs 41 min in our 15-file test, 4 manual interventions vs 9). GitHub Copilot remains the smarter pick if you need broad IDE support (8 native IDEs vs Cursor's VS Code fork), rock-solid GitHub workflow integration, or want a capable AI assistant at half the price ($10 vs $20 per month at Pro, $19 vs $40 per user at Business). For solo VS Code devs doing multi-file work, go Cursor. For JetBrains/Xcode teams or budget-conscious orgs, go Copilot. For first-time AI coding users, Copilot wins on lower entry friction. Many engineering teams run both seat-by-seat — Cursor for depth, Copilot for breadth.

Runway Gen-4.5 wins for professional filmmakers who need character consistency, 4K output, and a full post-production suite (Act-Two, Aleph, Workflows) — scoring 8.7/10 at $12/month. Choose Kling AI (8.0/10, from $6.99/month) if you prioritize best-in-class human motion realism, native audio-visual generation, and faster turnaround for social media content.

Lovable (8.9/10, $25/month) wins for non-technical founders and teams who need a complete full-stack app with built-in Supabase integration, SOC 2 compliance, and real-time collaboration out of the box. Choose Bolt.new (8.7/10, $25/month) if you are a developer who wants multi-framework flexibility across React, Vue, Svelte, and React Native, or if faster diff-based iteration and Figma import are priorities.

Cursor wins overall in 2026 on code quality (Composer 2 scores 73.7 on SWE-Bench Multilingual vs SWE-1.5's 40.08), enterprise scale (40,000 NVIDIA engineers, ~90 percent of Salesforce, $1B ARR), parallel agent orchestration, and corporate stability ($29.3B Anysphere). Windsurf wins on raw token throughput (SWE-1.5 at 950 tokens per second, ~5x faster than Composer 2's 200 tokens per second) and IDE flexibility (40+ plugins for JetBrains, Vim, NeoVim, Xcode). Both Pro plans now cost $20 per month after Windsurf's March 19, 2026 price hike eliminated the 25 percent budget advantage. Solo developers and enterprise buyers should pick Cursor; agencies running mixed-IDE teams or speed-first iterative workflows should pick Windsurf. Re-evaluate Windsurf in Q3-Q4 2026 after the Cognition-Devin integration ships.

Lovable and v0 are not direct competitors — they are tools for different jobs. Verdict: tie, persona-split. For solo developers and non-technical founders, Lovable wins because the bundled stack (React, Supabase, Stripe, hosting, custom domain) ships MVPs in hours and the flat $25 per month entry is the lowest-friction in the category. For agencies and enterprise teams already on Vercel infra, v0 wins because of multi-framework flexibility, agentic GitHub PR workflow, native shadcn/ui Design Mode, and Vercel's compliance posture (SOC 2, 99.99% SLA). Across our 5 named tests, Lovable won 2, v0 won 2, and 1 was a tie — picking between them is about which test resembles your real work, not which tool is universally better.

GPT-5.4 is the broader platform: 50% cheaper API ($2.50/$15 vs $5/$25 per MTok), native multimodal (DALL-E images, Sora video, Advanced Voice), polished computer use (OSWorld: 75.0%), and stronger performance on novel engineering problems (SWE-Bench Pro: 57.7% vs ~46%). Claude Opus 4.6 is the deeper specialist: #1 on standard coding (SWE-Bench Verified: 80.8%), #1 on abstract reasoning (ARC-AGI-2: 68.8% vs ~35%), #1 on terminal operations (Terminal-Bench 2.0: 65.4%), #1 on user satisfaction (Chatbot Arena), plus unique capabilities like Agent Teams, context compaction, and adaptive thinking. Choose ChatGPT if you need a do-everything assistant at lower cost. Choose Claude if coding depth, agent orchestration, reasoning, and writing quality are your priorities.