Tool Comparisons

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 the claim has real support: Grok 4.5 scores 76, roughly GPT-5.5 level, at less than half the token price. 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, 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 ranks GPT-5.6 Sol No.1 on its Coding Agent Index at 80, where Opus 4.8 is not separately charted, 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, ranks it No.1 on the Coding Agent Index at 80 to Grok's 76, 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: Artificial Analysis ranks it No.1 on the Intelligence Index at 60 (one point above Sol's 59), it holds the No.1 LMArena Elo at 1509 against Sol's 1486 at No.8, 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 about $1.04 per task versus about $11.80 for Fable 5, it tops the AA Coding Agent Index at 80 ahead of Fable 5, 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.

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.

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.

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 introductory rate. 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 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.

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.

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 during the introductory window through August 31, 2026, 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.

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 wins price and openness decisively — roughly $0.95 per million input tokens and $4.00 output versus Sonnet 5's $2 to $3 input and $10 to $15 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 Kimi 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 Kimi K2.7, while teams that need documented capability, computer use, long context, and governance sign-off should lean Claude Sonnet 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 nothing extra at the standard rate. 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. From September 1, 2026 it costs exactly what Sonnet 4.6 costs — $3 per million input tokens and $15 per million output tokens — and through August 31, 2026 it is cheaper at $2 and $10. 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.

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 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 Kimi 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. Kimi 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 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. 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 tops the independent Artificial Analysis Intelligence Index, 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 is the strongest widely available model: number one of 152 on the Artificial Analysis Intelligence Index at 65 versus 52 for DeepSeek V4-Pro, 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.

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.

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.

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.

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.

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 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 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 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 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 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.

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 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.

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.