Mistral Large 4 vs Claude Opus 5.5: Price, Specs and Benchmarks

Mistral Large 4 undercuts Claude Opus 5.5 on price by roughly 6x on input tokens and 9x on output tokens, but the two models are not really competing for the same job. Mistral's public preview, released October 6, 2026, is built to be cheap, open-weight and self-hostable; Anthropic's flagship, out since September 22, 2026, is built to be the most capable closed model Anthropic sells, at a matching price. Independent benchmark tracking from Vals.ai puts the two closer together on raw accuracy than the price gap suggests, which is the real story for anyone choosing between them.

The comparison matters now because both models launched within two weeks of each other this fall, and Mistral explicitly frames Large 4 as a platform play rather than a one-off release — the announcement ties the launch to Mistral's reported €3 billion Series D funding round and describes Large 4 as a base for future specialized models. Anthropic, meanwhile, has already followed Opus 5.5 with Claude Sonnet 5.5 and Claude Haiku 5.5, so the Opus tier is explicitly positioned as the expensive, highest-capability option in its own lineup rather than a default choice.

Price per million tokens: the gap is real

On Mistral's own announcement, Large 4's public preview is priced at $0.70 per million input tokens and $2.10 per million output tokens. Anthropic's pricing for Claude Opus 5.5 is $4 per million input tokens and $20 per million output tokens — about 5.7x more expensive on input and roughly 9.5x more expensive on output, before any discounts.

Both companies offer ways to bring the effective price down. Mistral's launch pricing already reads like a discount; Anthropic cuts Opus 5.5's output rate in half on the Batch API and drops cached input reads to 5% of the base input price ($0.20 per million tokens), with cache writes at $5 per million tokens. A "fast mode" variant of Opus 5.5, built for latency-sensitive work, costs more, not less: $8 input and $40 output per million tokens. For a side-by-side look at how Opus 5.5's plans and limits break down for subscribers rather than API buyers, see our rundown of Opus 5.5's pricing and release.

Context window, output limits and architecture

Anthropic states Claude Opus 5.5's specs directly: a 1M-token context window, a 128K-token maximum output on the synchronous API (up to 300K on the Batch API with a beta header), and a reliable knowledge cutoff of June 2026. Thinking is adaptive and always on for Opus 5.5 — unlike earlier Claude generations, it cannot be switched off, and the default reasoning effort is set to "medium."

Mistral's own launch post and documentation don't publish a context-window or max-output figure for Large 4. Independent tracker Vals.ai lists a 512K-token context window and a 256K-token max output for the model as currently served. Mistral describes Large 4's architecture as a roughly 1-trillion-parameter Mixture-of-Experts model with about 52 billion active parameters per token, trained from scratch on 3,800 Nvidia Grace Blackwell GPUs across Mistral's European data centers, with training data spanning more than 160 languages. Our deeper look at Large 4's release and spec sheet covers the preview rollout in more detail.

Open weights, licensing and where each one runs

The biggest structural difference between these two models isn't a benchmark score — it's deployment. Mistral lists Large 4 (version v26.10) under its Mistral Ecosystem Open Weight License in its own model documentation, positioning it as the direct open-weight replacement for Mistral Large 3, which Mistral has scheduled for deprecation on October 31, 2026. At launch, though, Large 4 was available only through Mistral's public preview API on Mistral Studio; Mistral's October 6 announcement says the weights themselves — plus further architecture detail — were still to come, separately from the API preview. Mistral also says it's giving government cybersecurity partners a reduced-moderation, expanded-capability version of the model under a red-teaming arrangement.

Claude Opus 5.5 has no self-hosting option at all. It runs on the Claude API, Claude Code and the Claude apps, and through Amazon Web Services, Google Cloud and Microsoft Azure, with a zero-data-retention option for enterprise buyers. There's no open-weight version of any current Claude model, and Anthropic's pricing and access model is built around subscriptions and metered API usage rather than a license to run the weights yourself. Anthropic's model-overview documentation also commits to not retiring Opus 5.5 on its own platforms before September 22, 2027, which matters if you're building something you don't want to re-platform in six months. If you're trying to decide between Anthropic's own subscription tiers rather than the API, our comparison of Claude Pro and Claude Max covers that separately.

What the benchmarks actually say

Self-reported numbers from either company should be read as a starting point, not a verdict — both companies pick the comparisons that favor them. Mistral's own announcement claims Large 4 scores 61.7% on DeepSWE v1.1, 28.3% on Terminal-Bench 4.0, and 59.4% on SWE-Atlas-QnA, for a combined Coding Agent Index of 49.8%. Mistral also discloses a blind human evaluation run by Surge AI, in which Large 4 placed second of five models tested, at 3.74, behind Claude Opus 5 — the previous-generation Anthropic model, not Opus 5.5 — which scored 4.22. That's a notable admission from Mistral's own launch materials: on human-judged coding quality, Large 4 trailed Anthropic's prior flagship before Opus 5.5 even entered the picture.

Anthropic's own benchmark table for Opus 5.5 shows it ahead of Claude Fable 5.1, Claude Opus 5 and OpenAI's GPT-6 Astra on most agentic and coding tests it publishes — 66.4% on Terminal-Bench 4.0 and an Elo of 1846 on GDPval-AA v2.1, for example — though Anthropic's own chart shows GPT-6 Astra ahead of Opus 5.5 on AutomationBench (41.4% versus 40.0%) and on Terminal-Bench-Science (64.6% versus 58.7%). Anthropic itself cautions that benchmark margins are "a less reliable guide" at this capability tier.

For a comparison point that isn't picked by either vendor, Vals.ai's independent index gives Claude Opus 5.5 an overall accuracy of 48.17%, with its strongest individual result on CyberBench v1.1 (74.58%, ranked 9th of 47 models tracked). Mistral Large 4 scores 45.09% on the same index, with its best individual result on MedScribe (80.43%, 57th of 109) and a 17.50% result on HLAB-Vals-1.1 (7th of 33). The headline gap is about three points on Vals.ai's index — far narrower than the roughly 6-to-9x price difference between the two models. Treat both numbers as snapshots: Vals.ai re-runs its suites as providers ship new checkpoints, and other trackers citing earlier runs have shown Large 4 a few points higher than the figure we pulled directly from Vals.ai's model page.

Away from coding, Mistral also publishes narrower claims worth knowing about: a 42% score on a multimodal benchmark called Dense 200, versus 41% for GPT-6-Astra, and claims of beating GPT-6-Astra on Vals.ai's own finance and legal task sets while outperforming other open-weight models on Harvey's Legal Agent Benchmark specifically. None of those are figures Anthropic or an independent tracker has published for Opus 5.5 in the sources checked for this piece, so they aren't directly comparable — they're useful mainly for judging Large 4 against other open-weight models rather than against Opus 5.5 itself.

Coding and agentic work

Both companies are pitching these models at agentic coding and long-running tool use, where the price-per-token math changes quickly once a task runs for hundreds of turns. Anthropic highlights Opus 5.5 completing a 680,000-line code migration in under a day and a 200,000-line audit in under three hours — a task it says took Opus 5 more than 20 hours. Mistral's pitch for Large 4 leans on its Coding Agent Index and AutomationBench score (59.9%) alongside a 1,393 Elo on AA-Briefcase, positioning it as strong among open-weight models specifically, not necessarily ahead of closed frontier models.

On cybersecurity work specifically, Mistral reports Large 4 ranking in the top five of the Artificial Analysis Cyber Index, with 93% on Cybench and 82% on a vulnerability reproduce-and-patch test; Anthropic, for its part, routes "most cybersecurity-related tasks" on Opus 5.5 to the older Claude Opus 4.8 model for safety reasons rather than serving them on 5.5 directly — a detail worth knowing if your use case touches that area.

Safety posture

Anthropic says Opus 5.5 scored best of any model it has tested on its internal automated behavioral audit of roughly 2,000 scenarios, and that it attempts to circumvent containment boundaries about 85% less often than Opus 5 or Claude Mythos 5.1. Anthropic also says Opus 5.5 ties Claude Fable 5.1 for the lowest prompt-injection success rate on a Gray Swan benchmark and matches or beats Opus 5 on injection resistance generally. It uses what Anthropic calls "preserved thinking," an anti-distillation safeguard applied to API accounts created on or after August 31, 2026, and includes output watermarking to meet EU AI Act requirements. Anthropic also notes the model frequently appears to recognize when it's being evaluated, which it says limits how much weight to put on its own safety testing.

Mistral reports Large 4 resisting 93.3% of attacks on Lakera's B3 red-teaming benchmark and scoring 1.691 out of a possible 2 on KORA, alongside what it calls a higher cyber-refusal rate than other open-weight models. It's also giving a reduced-moderation, expanded-capability variant of Large 4 to cybersecurity partners and state authorities for red-teaming purposes, according to its own launch post — worth flagging for anyone evaluating the model's safety posture rather than just its benchmark scores. Neither company's safety figures are independently audited in the sources checked for this piece, so treat both as vendor-reported.

Which one should you actually use

If your workload is price-sensitive, high-volume, or needs to run on infrastructure you control — including EU-sovereign deployments — Large 4's open-weight license and sub-$1 input pricing make it the obvious starting point once the full weights are published, with the API preview available now for testing. If you need the longest context window available, the most consistent agentic coding performance across both vendor and independent benchmarks, or enterprise safety guarantees like zero data retention, Opus 5.5's premium holds up better than the raw Vals.ai gap suggests, especially for tasks where a wrong answer is expensive. Anthropic also sells Opus 5.5 against its own lighter models; our comparison of Opus 5.5 against Claude Fable 5.1 is a useful next read if Opus 5.5's price is the sticking point rather than Mistral's open-weight angle.

What to watch next

Three things are still unsettled. First, Mistral's full weight release for Large 4 — promised separately from the October 6 API preview — will determine how many teams can actually self-host it rather than just calling Mistral's hosted API. Second, Mistral has said reinforcement-learning training on Large 4 was still ongoing at launch, so its benchmark scores, including on Vals.ai, may shift as later checkpoints ship. Third, Anthropic has already followed Opus 5.5 with Claude Sonnet 5.5 and Claude Haiku 5.5, which changes the math for anyone who doesn't need Opus-tier capability at all. Anyone pricing this out against other frontier releases from this window should also check how Google and OpenAI's latest models compare; our three-way comparison of Gemini 4 Carbon, Opus 5.5 and GPT-6.1 Terra covers that separate pairing.

Written byTheo Park

Theo Park runs the AI desk at Pandromeda. He follows model launches from the frontier labs and the open-weight community, tracks the assistants and developer tools built on them, and explains what each release changes on pricing, capability and safety. His reporting leans on primary sources: model cards, technical reports, API documentation and the companies' own announcements.

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