GPT-5.6 Luna vs Claude Haiku 4.5: Capacity vs Latency (2026)

GPT-5.6 Luna vs Claude Haiku 4.5 is not a duel of intelligence — Luna wins that outright — it is a choice between capacity and latency. Luna is OpenAI's economy GPT-5.6 tier at $1 per million input tokens and $6 output, scoring 51 on the independent Artificial Analysis Intelligence Index with a 1,050,000-token context. Claude Haiku 4.5 is Anthropic's fast small model at $1 input and $5 output, scoring 24 on the same index with a 200,000-token context, but built for speed: 91.7 tokens per second output and a 0.82-second time to first token. Input price is identical; Haiku is a dollar cheaper on output. Verdict: pick Luna if you need reasoning, long context, or raw capability; pick Haiku if latency and throughput are the product, where it beats Luna decisively.

Quick Verdict

Luna is the capability pick; Claude Haiku 4.5 is the latency-and-throughput pick. We ran both through their APIs in July 2026 and anchored the verdict to independent benchmarks rather than launch-day impressions. These two models share a price band but sit in different design centers, and pretending otherwise would mislead you. On the independent Artificial Analysis Intelligence Index (version 4.1), Luna scores 51 and Haiku scores 24 — a 27-point gap, more than double Haiku's number. That is not a rounding error; Luna is a far more capable model. But Haiku was never built to win that fight. It is engineered for speed, and it publishes the numbers to prove it: 91.7 tokens per second of output and a 0.82-second time to first token, tuned for real-time interfaces and fleets of sub-agents running at volume. Input price is a tie at $1 per million tokens, and Haiku is a dollar cheaper on output at $5 versus $6. So the honest framing is not "which is better" but "which axis are you optimizing."

• 🏆 GPT-5.6 Luna wins for: raw intelligence and reasoning, agentic and long-context work, and anything that needs a context window in the million-token range. It carries more than twice Haiku's independent Intelligence Index score and more than five times its context.
• 🏆 Claude Haiku 4.5 wins for: latency-sensitive, high-volume, real-time work — chat front-ends, autocomplete, moderation at scale, and large sub-agent swarms — where its 91.7 tokens per second and sub-second first token make it the right engine and Luna would be the wrong, over-heavy choice.
• 💰 Cheaper on tokens: Claude Haiku 4.5, modestly. Input is identical at $1 per million tokens; Haiku's $5 output undercuts Luna's $6, so any output-bearing workload costs roughly 10 to 15 percent less on Haiku.
• 🧠 Smarter on paper: GPT-5.6 Luna, and not narrowly — 51 versus 24 on the Artificial Analysis Intelligence Index is a wide, decisive gap.

GPT-5.6 Luna vs Claude Haiku 4.5 — Overview

What Is GPT-5.6 Luna?

GPT-5.6 Luna is the economy tier of OpenAI's GPT-5.6 family, which reached general availability on July 9, 2026. We cover it in depth in our GPT-5.6 Luna review. In the new naming scheme the number is the generation and the names are durable capability tiers: Sol is the flagship for the hardest problems, Terra is the balanced high-volume tier, and Luna is the fastest and most economical tier, built for summarization, drafting, classification, and routine automation. Luna carries a 1,050,000-token context window, a maximum output of 128,000 tokens, and a knowledge cutoff of February 16, 2026. It accepts text and image inputs and returns text; there is no native audio or native image generation, though image generation is available as a callable tool. Luna inherits the full GPT-5.6 platform — web search, file search, a code interpreter, a hosted shell, computer use, Model Context Protocol support, and Programmatic Tool Calling that lets the model write and run JavaScript in an isolated runtime. Crucially for this matchup, Luna is a reasoning-capable model: on the independent Artificial Analysis Intelligence Index (version 4.1) it scores 51, and it is priced at $1 per million input tokens, $0.10 cached, and $6 output.

What Is Claude Haiku 4.5?

Claude Haiku 4.5 is Anthropic's fast, small member of the Claude 4.5 family, and its entire design brief is speed and cost at volume rather than frontier reasoning. Our full write-up is in the Claude Haiku 4.5 review. It is billed at $1 per million input tokens and $5 output, carries a 200,000-token context window, and accepts text and image input. Where Haiku separates itself is latency and throughput: it generates output at roughly 91.7 tokens per second and returns its first token in about 0.82 seconds, which is what makes it a natural fit for real-time chat, code autocomplete, content moderation, and orchestrating large fleets of sub-agents that each do small, fast jobs. Anthropic positions it as delivering near-Sonnet-4-class coding in a small package and self-reports a SWE-bench Verified result of 73.3 percent — a vendor-measured number rather than an independently audited one, which matters for how much weight you put on it. On aggregate intelligence, though, the independent picture is blunt: Artificial Analysis places Haiku 4.5 in its non-reasoning category and scores it 24 on the version 4.1 Intelligence Index, ranked 30th of 79 models on that leaderboard. Haiku is not trying to out-think Luna; it is trying to out-run it, and on latency it does.

Features Comparison

We compared the two on the dimensions that actually separate a capacity model from a speed model: the individual price lines, independent benchmark standing, context, latency, and model class. Where a number is self-reported by the vendor or simply not published, we say so rather than paper over the gap. One deliberate omission: we do not put Luna's independent coding score in the same row as Haiku's vendor coding number, because they are measured on different benchmarks by different parties and stacking them side by side would be misleading. The coding question is handled separately, and carefully, below.

Count the rows and Haiku actually takes more of them — three against Luna's two, with three ties. But look at what each side wins. Haiku sweeps the speed cluster — output rate, first-token latency — and takes the one price line that differs, output. Luna takes the two heaviest capability rows: aggregate intelligence and context window. The rows are not equal in weight. A 27-point independent intelligence lead and a context window more than five times larger reshape what a model can do; a dollar off the output rate and a faster first token reshape how quickly it does the subset of work it can already handle. That tension — more rows for Haiku, more consequential rows for Luna — is the whole matchup, and we resolve it in the verdict.

Pricing — GPT-5.6 Luna vs Claude Haiku 4.5 in 2026

Both models use flat, per-token API pricing with no context-length tiers, so the sticker comparison is unusually clean. And it is close: input is identical at $1 per million tokens, and the only difference on the rate card is output, where Haiku's $5 undercuts Luna's $6. All figures below are per million tokens and were checked against each vendor's own pricing documentation in July 2026.

GPT-5.6 Luna Pricing

Claude Haiku 4.5 Pricing

Which Is Actually Cheaper? Haiku, but Only Just

Because input is a tie and Haiku wins output by a dollar, Haiku is the cheaper model on any workload that generates text — the more output-heavy the job, the wider the gap, but it stays modest. We priced two illustrative monthly workloads to show the shape. The assumptions are simple and stated; the point is the direction, not the exact dollar.

Read those two rows together. Haiku is always the cheaper token engine here — input is identical, so its lower output rate can only help — but the saving is small, roughly 4 to 13 percent depending on how much text you generate. One nuance Luna owns on paper: a published $0.10 cached-input rate, a 90 percent discount on repeated context that helps retrieval-heavy patterns; Haiku's cached-read economics are not part of the figures we verified, so we do not fold them into the comparison. The bottom line on money: Haiku wins it, but by a margin small enough that price should almost never be the deciding factor between these two. The decision is about capability versus latency, and that is where they genuinely diverge.

The Real Question: Latency vs Capacity

This is the section that matters, because it is the axis the whole comparison turns on. Strip away the near-identical pricing and you are left with two models pointed in opposite directions.

Luna is the capacity model. A 51 on the independent Intelligence Index and a 1,050,000-token context mean it can reason through multi-step problems, hold an entire large document or codebase in a single pass, and carry agentic work that requires the model to plan, not just react. When the task is hard — genuine reasoning, long-context synthesis, code that has to be correct — Luna's extra intelligence is not a luxury, it is the thing that gets the job done. Haiku, at 24 on the same index and in the non-reasoning category, will struggle with exactly those tasks; asking it to do heavy reasoning is asking it to do the one thing it was not built for.

Haiku is the latency model. Its 91.7 tokens per second of output and 0.82-second first token are not vanity metrics — they are the product. In a live chat interface, a code-completion widget, a voice agent, or a moderation queue, the difference between a sub-second response and a multi-second one is the difference between something that feels instant and something that feels sluggish, and users notice. And when you run a swarm of hundreds of small sub-agents, each doing a narrow task, per-agent latency and throughput compound into the total wall-clock time and cost of the whole system. This is where Haiku is not just competitive but decisively better: for latency-bound and high-volume real-time work, its speed beats Luna, and it is not close. Anthropic built Haiku precisely for the fleets-of-sub-agents pattern, and it shows.

So the honest guidance is a fork, not a ranking. If your bottleneck is how smart the model is, buy capacity — Luna. If your bottleneck is how fast and how many, buy latency — Haiku. Most of the mistakes teams make with these two come from buying the wrong axis: over-paying in latency for reasoning they do not need, or starving a hard task of the intelligence it does.

Hands-on — How They Performed Side-by-Side

We ran GPT-5.6 Luna and Claude Haiku 4.5 through their APIs in July 2026, using identical prompts and inputs on each task. Both models are recent, so we treat our runs as early hands-on and lean on the independent Artificial Analysis indices for the quantitative verdict rather than on first impressions. Here are four tasks we ran on both.

Test 1: A multi-step reasoning problem

We gave both models the same layered analysis task — read a scenario, work through the trade-offs, and produce a justified recommendation with the reasoning shown. This is where the intelligence gap stopped being a number on a leaderboard. Luna worked the problem in structured steps, caught a second-order consequence we had planted, and landed a defensible conclusion. Haiku answered faster and more confidently, but flattened the problem: it gave a plausible surface answer that missed the trade-off Luna caught, which is exactly what you would expect from a fast non-reasoning model asked to reason. Result: Luna wins decisively on genuine multi-step reasoning — this is the task Haiku is not built for.

Test 2: An agentic refactor across a small codebase

We asked each model to refactor a small TypeScript service — extract a module, update imports, and keep the test suite green. Both produced working edits, and Haiku is genuinely respectable at code for a small model. On the independent side, Luna is the only one of the pair with a published number on the Artificial Analysis Coding Agent Index, where it scores 75; that gives buyers a transparent, third-party figure to plan around, whereas Haiku's coding standing rests on a vendor self-report. In practice Luna handled the more tangled edits with fewer retries, while Haiku's advantage showed up as speed — it returned its attempts far quicker, which matters when a human is in the loop iterating. Result: Luna wins on measured coding capability and correctness on harder edits; Haiku wins on the responsiveness of the edit-run-repeat loop.

Test 3: High-volume classification at speed

We ran 5,000 short support tickets through each model for intent classification, a deliberately narrow, high-volume job with tiny outputs. Accuracy was close — this is well within what a small model handles cleanly — and here the whole point flipped to throughput. Haiku's 91.7 tokens per second and sub-second first token cleared the queue noticeably faster, and at $5 per million output tokens it billed a little less than Luna's $6 for the same volume. On a job like this, Luna's extra intelligence is wasted headroom you are paying a latency tax to carry. Result: Haiku wins high-volume, latency-sensitive classification on both speed and cost.

Test 4: Long single-document synthesis

We fed both models a 300-page technical manual and asked for a structured, cross-referenced summary. Luna ingested the entire document inside its 1,050,000-token window in one pass and cross-referenced sections cleanly. Haiku, capped at 200,000 tokens, needed the manual chunked and stitched, which added orchestration work on our side and a real risk of missed links between chunks. Within each chunk Haiku's summaries were fine and fast, but for a genuinely long single document, Luna's context window that is more than five times larger is a structural advantage, not a cosmetic one. Result: Luna wins long-context synthesis.

Winner per Category

🏆 Best Overall (for most buyers): GPT-5.6 Luna

For the broad question — which of these two is the better model for most real work — Luna is the pick, and not narrowly. A 27-point independent intelligence lead and a context window more than five times larger are large capability differences, and Haiku is only modestly cheaper and a tie on input. For anyone whose workload includes reasoning, long documents, or hard agentic tasks, Luna is the safer, more capable default. The exception is real and important, and it is the next category.

Best for Latency and Real-Time UX: Claude Haiku 4.5

When response time is what your users feel — live chat, autocomplete, voice, interactive agents — Haiku's 91.7 tokens per second and 0.82-second first token make it the correct engine, and Luna the wrong one. For latency-bound interfaces, this is not close: Haiku is built for exactly this and beats Luna clearly.

Best for High-Volume Sub-Agent Fleets: Claude Haiku 4.5

If your architecture fans work out across hundreds of small, fast sub-agents, per-agent latency and throughput compound into total system speed and cost. Haiku's speed plus its dollar-lower output rate make it the natural engine for swarms doing narrow tasks at scale — the pattern Anthropic explicitly designed it for.

Best for Reasoning and Agentic Work: GPT-5.6 Luna

Anything that requires the model to plan, weigh trade-offs, or hold a long chain of logic belongs to Luna. At 51 on the Intelligence Index versus Haiku's 24, and with a reasoning-capable design, it does the hard thinking Haiku is not built to do.

Best for Long Context: GPT-5.6 Luna

A 1,050,000-token window against 200,000 lets Luna handle large manuals, codebases, and contracts in a single pass, while Haiku needs long inputs chunked and stitched. For long single documents, Luna is the more comfortable and more reliable fit.

Cheapest per Token: Claude Haiku 4.5

With input tied and output a dollar lower, Haiku is the cheaper model on any output-bearing workload, typically by 4 to 13 percent depending on your output share. The margin is small, but if you have pinned down that you do not need Luna's intelligence, Haiku's is the leaner bill.

Pros and Cons

GPT-5.6 Luna Pros and Cons

What we liked about GPT-5.6 Luna

• Far higher independent intelligence. A 51 on the Artificial Analysis Intelligence Index against Haiku's 24 is a 27-point lead — the widest and most consequential gap between them.
• More than five times the context. A 1,050,000-token window versus 200,000 handles long single documents in one pass.
• Reasoning-capable. Luna can plan and work multi-step problems, making it the safer default for hard agentic and analytical tasks.
• Cheapest cached reads. A published $0.10 cached-input rate gives retrieval-heavy systems a real discount on repeated context.
• Full managed platform. Programmatic Tool Calling, code interpreter, hosted shell, computer use, and MCP come standard.

Where GPT-5.6 Luna falls short

• A dollar pricier on output. Its $6 output rate sits above Haiku's $5, so output-heavy jobs cost modestly more.
• No published speed figure to match Haiku. Luna is fast for its tier, but it does not publish a throughput or first-token number, and for pure latency-bound work Haiku's measured speed wins.
• Overkill for narrow high-volume jobs. On simple classification or moderation at scale, Luna's intelligence is headroom you pay a latency tax to carry.

Claude Haiku 4.5 Pros and Cons

What we liked about Claude Haiku 4.5

• Class-leading latency. Roughly 91.7 tokens per second of output and a 0.82-second first token make it feel instant in real-time interfaces.
• Cheaper on output. A $5 output rate undercuts Luna's $6 while matching it on input, so any generating workload bills a little less.
• Built for sub-agent fleets. Its speed and cost make it a strong engine for swarms of small agents doing narrow tasks at high volume.
• Strong coding for a small model. Anthropic self-reports a SWE-bench Verified figure of 73.3 percent, competitive with much larger models on that vendor benchmark.
• Vision input and a clean API. It accepts text and image input and slots into the wider Claude platform.

Where Claude Haiku 4.5 falls short

• Much lower independent intelligence. A 24 on the Artificial Analysis Intelligence Index trails Luna's 51 by 27 points; it is in the non-reasoning category and struggles with genuine multi-step reasoning.
• A fifth of the context, roughly. A 200,000-token window forces chunking on long documents that Luna handles in a single pass.
• Coding standing is vendor-measured. Its 73.3 percent SWE-bench Verified is self-reported, so buyers cannot cross-check it against an independent leaderboard the way they can Luna's coding score.
• Not the tool for hard thinking. The same design that makes it fast makes it a poor fit for tasks that need real reasoning or deep analysis.

When to Pick GPT-5.6 Luna vs Claude Haiku 4.5

Pick GPT-5.6 Luna if...

• Your workload needs genuine reasoning, planning, or multi-step analysis.
• You process very long single documents and need a context window in the million-token range.
• You are running hard agentic tasks where correctness matters more than a fast first token.
• You want the higher independent intelligence score and a verifiable third-party coding number.
• Your patterns lean on cached reads, where Luna's $0.10 cached rate helps.
• You are already building on OpenAI's stack with Programmatic Tool Calling and MCP.

Pick Claude Haiku 4.5 if...

• Latency is what your users feel — chat, autocomplete, voice, or interactive agents.
• You run high-volume, narrow tasks like classification or moderation where a small model is plenty.
• You are building large sub-agent fleets where per-agent speed and cost compound.
• Your workload is output-heavy and you want the lower output rate.
• You want near-instant responses and have confirmed you do not need heavy reasoning.
• You are already on the Claude platform and want its fastest, leanest tier.

Frequently Asked Questions

Is GPT-5.6 Luna better than Claude Haiku 4.5 in 2026?

For most real work, yes — GPT-5.6 Luna is the more capable model by a wide margin, scoring 51 on the independent Artificial Analysis Intelligence Index against Haiku's 24, with a context window more than five times larger. But "better" depends on your axis. Claude Haiku 4.5 is built for speed, not reasoning, and for latency-sensitive, high-volume, real-time work it beats Luna decisively. Pick Luna for capability and long context; pick Haiku when response time and throughput are the product. Neither is universally superior — they are pointed in different directions.

How much does GPT-5.6 Luna cost compared to Claude Haiku 4.5?

Input is identical at $1 per million tokens for both. The only rate-card difference is output: GPT-5.6 Luna charges $6 per million output tokens and Claude Haiku 4.5 charges $5, so Haiku is a dollar cheaper on generation. On an output-heavy workload Haiku comes out roughly 13 percent cheaper; on an input-heavy one the gap shrinks to a few percent. Luna also publishes a $0.10 cached-input rate for repeated context. So Haiku is modestly the cheaper token engine, but the margin is small enough that price should rarely decide between them.

Why does Claude Haiku 4.5 score only 24 when some sources say 55?

That 55 comes from a different index version or scoring mode, not the current benchmark used here. On the Artificial Analysis Intelligence Index version 4.1 — the same version that scores GPT-5.6 Luna at 51 — Claude Haiku 4.5 scores 24, in the non-reasoning category, ranked 30th of 79 models. Comparing an older-index or different-mode figure against a v4.1 score would be apples to oranges, so throughout this comparison we use the matched v4.1 numbers: 51 for Luna and 24 for Haiku. If you see 55 quoted, check which index version and mode it refers to before relying on it.

Which is faster, GPT-5.6 Luna or Claude Haiku 4.5?

Claude Haiku 4.5, clearly, and this is its whole reason to exist. It generates output at roughly 91.7 tokens per second and returns its first token in about 0.82 seconds, both tuned for real-time use. GPT-5.6 Luna is fast for its tier but does not publish a comparable throughput or first-token figure, so for latency-bound workloads Haiku is the measured winner. If your interface lives or dies on response time — chat, autocomplete, voice — Haiku is the right engine.

Which is better for coding, GPT-5.6 Luna or Claude Haiku 4.5?

Claude Haiku 4.5 is genuinely capable at code for a small, fast model, and Anthropic self-reports a SWE-bench Verified result of 73.3 percent — though that is a vendor-measured number rather than an independent one, so treat it as a signal, not a settled fact. GPT-5.6 Luna, measured on a different benchmark by an independent party, is the only one of the two that carries a published third-party agentic-coding score, giving buyers a figure they can cross-check. For raw responsiveness in an edit-run-repeat loop, Haiku's speed helps; for harder edits and externally verified coding capability, Luna is the stronger pick.

Is Claude Haiku 4.5 a reasoning model?

No — Artificial Analysis classifies it in the non-reasoning category, and its design center is speed and throughput rather than extended deliberation. That is exactly why its aggregate Intelligence Index score of 24 sits well below GPT-5.6 Luna's reasoning-capable 51. For tasks that need genuine multi-step reasoning, planning, or deep analysis, Haiku is the wrong tool and Luna is the right one. For fast, narrow, high-volume work where the model does not need to think hard, Haiku's speed is the advantage.

Which has the bigger context window?

GPT-5.6 Luna, by a wide margin: 1,050,000 tokens versus Claude Haiku 4.5's 200,000, more than five times the capacity. For short prompts the difference is immaterial, but for genuinely long single documents it is decisive — Luna can ingest a large manual, codebase, or contract in one pass, while Haiku needs the input chunked and stitched, which adds orchestration work and a small risk of missed cross-references between chunks.

Should I use Claude Haiku 4.5 for a fleet of sub-agents?

Yes — this is one of the workloads it was explicitly designed for. When you fan work out across hundreds of small sub-agents doing narrow tasks, each agent's latency and throughput compound into the total wall-clock time and cost of the whole system, and Haiku's roughly 91.7 tokens per second plus its lower output rate make it an efficient engine at that scale. GPT-5.6 Luna would give each agent more intelligence, but if the sub-tasks are simple you are paying a latency and cost premium for headroom you do not use.

Does either model support image input?

Both accept image input alongside text and return text. GPT-5.6 Luna takes text and image inputs and exposes image generation as a callable tool rather than a native output. Claude Haiku 4.5 also accepts text and image input. Neither generates images natively in this tier. For straightforward image understanding the two are comparable; the meaningful differences between them are intelligence, context size, and latency, not vision.

Is GPT-5.6 Luna smarter than Claude Haiku 4.5?

Yes, and by a wide margin. On the aggregate Artificial Analysis Intelligence Index version 4.1, Luna scores 51 and Haiku scores 24 — a 27-point gap, more than double Haiku's number. This is not a close call the way some same-tier comparisons are; Luna is a reasoning-capable model and Haiku is a fast non-reasoning one. That said, "smarter" only wins the tasks that need intelligence. For latency-bound, high-volume work, Haiku's speed makes it the better tool despite the lower score.

What are the best alternatives to GPT-5.6 Luna and Claude Haiku 4.5?

If you want more capability in OpenAI's family, step up to GPT-5.6 Terra, the balanced tier above Luna, or the GPT-5.6 Sol flagship for the hardest problems. For adjacent head-to-heads in the same class, our Claude Sonnet 5 vs Kimi K2.6 breakdown is useful company reading, and our best AI coding tools of 2026 guide maps the wider field of coding-focused models.

Which should a startup choose, GPT-5.6 Luna or Claude Haiku 4.5?

It depends on what your product does. If your app is a real-time interface — chat, autocomplete, voice, or a swarm of small agents — Claude Haiku 4.5's speed and lower output rate make it the sharper default. If your product needs reasoning, long-document work, or hard agentic tasks, GPT-5.6 Luna's far higher intelligence and much larger context are worth the modest output premium. Many teams run both: Haiku on the latency-critical front-end path, Luna for the heavy reasoning and long-context work behind it.

Final Verdict: Luna Wins Capability, Haiku Wins Latency

After running both side-by-side, our verdict is a clear overall win for GPT-5.6 Luna on the axis most buyers care about — raw capability — paired with an equally clear, honest carve-out for Claude Haiku 4.5 on latency. Price barely separates them: input is identical, Haiku is a dollar cheaper on output, and a real bill lands within a low double-digit percentage either way. What separates them is intelligence and context, where Luna leads by 27 independent points and more than five times the window, and speed, where Haiku publishes class-leading latency numbers that Luna does not match. If your work involves reasoning, long documents, or hard agentic tasks, go with GPT-5.6 Luna — it is the more capable model and the safer default. If your work is latency-bound and high-volume — real-time chat, autocomplete, voice, moderation at scale, or fleets of small sub-agents — Claude Haiku 4.5 is the right engine, and for those workloads it beats Luna and it is not close.

Score breakdown by category:

• Raw capability and intelligence: GPT-5.6 Luna 8.5 out of 10 vs Claude Haiku 4.5 5.5 out of 10 — a 27-point independent Intelligence Index gap is a wide, decisive lead for Luna.
• Speed and latency: GPT-5.6 Luna 7.0 out of 10 vs Claude Haiku 4.5 9.5 out of 10 — Haiku's measured 91.7 tokens per second and 0.82-second first token own this category.
• Context and long documents: GPT-5.6 Luna 9.0 out of 10 vs Claude Haiku 4.5 6.5 out of 10 — 1,050,000 tokens against 200,000 is a real gap.
• Value and pricing: GPT-5.6 Luna 8.0 out of 10 vs Claude Haiku 4.5 8.5 out of 10 — a near-tie, with Haiku modestly cheaper on output and input identical.

Final word: buy GPT-5.6 Luna if you want the higher independent intelligence, the far larger context, and reasoning-capable output — for most buyers comparing these two, it is the right default. Buy Claude Haiku 4.5 if latency, throughput, and real-time responsiveness are what you are optimizing, where its speed makes it the correct and better choice by a clear margin. This matchup is not about which model is stronger overall — it is about whether you need capacity or speed, and both models are excellent at the job they were built for. We last compared both in July 2026 and will revisit as independent latency and long-horizon reliability data matures. ThePlanetTools has no affiliate relationship with OpenAI or Anthropic; this verdict is editorially independent.