Model comparison · Updated May 2026
GPT-5.5 vs Mistral Large: Price, Context, Benchmarks (2026)
A direct, dated comparison of GPT-5.5 (OpenAI) and Mistral Large (Mistral). Every number below is sourced from official provider docs and public benchmarks. If you need to make this decision today, the verdict is at the top.
30-second verdict
- Cheaper: Mistral Large (input $2.00 vs $5.00 per 1M tokens).
- Longer context: GPT-5.5 at 1.1M vs 128K.
- Stronger on SWE-bench Verified: GPT-5.5 (~65% vs ~45%).
- Higher LMArena: GPT-5.5 (1442 vs 1380).
- Open weights: Mistral Large can be self-hosted.
Specs side-by-side
| Spec | GPT-5.5 | Mistral Large |
|---|---|---|
| Vendor | OpenAI | Mistral |
| Input price (per 1M tokens) | $5.00 | $2.00 |
| Output price | $30.00 | $6.00 |
| Context window | 1.1M | 128K |
| Release date | 2026-04-23 | 2025-02-01 |
| SWE-bench Verified | ~65% | ~45% |
| HumanEval | ~96% | ~88% |
| LMArena (approx) | 1442 | 1380 |
| Open weights | No | Yes |
| Capabilities | reasoning, code, vision | code |
Pricing from official OpenAI and Mistral docs. Benchmark numbers from SWE-bench Verified, HumanEval, and LMArena public leaderboards as of May 2026.
GPT-5.5 — strengths and weaknesses
Strengths. Frontier reasoning, broad ecosystem, strong tool use, multimodal in/out.
Weaknesses. Premium pricing, occasional over-editing in agent loops.
Best for. Hard reasoning, ambiguous specs, system design, agent planners.
Mistral Large — strengths and weaknesses
Strengths. EU-hosted, Apache-licensed open variants, solid tool use, predictable.
Weaknesses. Behind frontier on reasoning benchmarks.
Best for. EU compliance, on-prem deployments, mid-range workloads.
Which one should you pick?
Pick GPT-5.5 if: hard reasoning, ambiguous specs, system design, agent planners.
Pick Mistral Large if: eu compliance, on-prem deployments, mid-range workloads.
Use both if: you're building an agent or content pipeline. Route the high-stakes / hard-reasoning calls to whichever scores higher on the axis you care about, and the bulk / cheap calls to the other. Most production AI products run a 2-3 model router rather than betting on one.
Try them side-by-side
The Check.AI comparison tool lets you put both models in one table with all the numbers, switch capability filters, and share the resulting URL with your team.