GPT-5.2
GPT-5.2 is the strongest answer here for GPT-5.2 vs Mistral Large 2 — pick it when quality of output matters more than the $1.75/1M/1M input you pay for it.
- Best for
- Serious coding and complex product work
- Price
- $1.75/1M
- Context
- 200k tokens
GPT-5.2 wins on coding (85 vs 72) and writing quality and price ($1.75 vs $3/1M input) and context window (200K vs 128K). For most workflows, GPT-5.2 is the stronger default — capable but outclassed — gpt-5.4 is now cheaper and better.
The safest GPT-5.2 vs Mistral Large 2 default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-5.2 is the strongest answer here for GPT-5.2 vs Mistral Large 2 — pick it when quality of output matters more than the $1.75/1M/1M input you pay for it.
Mistral Large 2 handles the same job for about 24% less per token. Start here and only move up if the output is not good enough.
GPT-5.2 is the fastest of these for GPT-5.2 vs Mistral Large 2 — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GPT-5.2 leads on coding with a score of 85 vs 72 for Mistral Large 2.
GPT-5.2 has the larger context window: 200K vs 128K for Mistral Large 2.
GPT-5.2 is cheaper at $1.75/1M input tokens vs $3/1M for Mistral Large 2.
Go with GPT-5.2 if you want one model to handle coding and research — it targets serious coding and complex product work.
Switch to Mistral Large 2 when your work is mostly balanced team usage with EU data residency requirements; on that narrower brief it is the better tool.
Both models serve different primary workflows — GPT-5.2 for serious coding and complex product work, Mistral Large 2 for balanced team usage with EU data residency requirements — so running each where it has a clear edge often beats forcing one to do both.
Switch the scoring lens to see whether the GPT-5.2 vs Mistral Large 2 answer changes when cost, speed, or long-document depth leads the decision.
OpenAI / Premium / Sep 3, 2026
Capable but outclassed — GPT-5.4 is now cheaper and better.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You're starting a new project — GPT-5.4 is cheaper and more capable.
Where the GPT-5.2 vs Mistral Large 2 recommendation shifts once you weigh price or latency differently.
Capable but outclassed — GPT-5.4 is now cheaper and better.
Best balanced generalist for EU teams with data residency needs.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-5.2OpenAI | $1.75/1M | $14.00/1M | $46 | 200k tokens | Balanced | 85 | 82 | 84 |
| Mistral Large 2Mistral | $3.00/1M | $9.00/1M | $48 | 128k tokens | Balanced | 72 | 72 | 71 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for GPT-5.2 vs Mistral Large 2, what it is genuinely good at, and where we would steer you away from it.
The default answer for GPT-5.2 vs Mistral Large 2 — 85/100 on the coding axis, and the model we would start with unless the price below rules it out.
Reliable OpenAI flagship for serious coding and product work — a strong default before GPT-5.4 was released.
You're starting a new project — GPT-5.4 is cheaper and more capable.
Still capable but GPT-5.4 at $2.50/1M input is now cheaper and better. Hard to justify GPT-5.2 at $12/1M for new projects.
Full pricing, benchmark table and release notes on the GPT-5.2 page.
Where most budgets should land for GPT-5.2 vs Mistral Large 2 — about 24% less per token than GPT-5.2, and still 72/100 on the coding axis.
Balanced enterprise model with consistent reasoning, good speed, and a dependable middle-ground — especially for European teams with data residency requirements.
You want best-in-class performance for any specific use case — the frontier leaders win.
A dependable generalist — especially relevant for EU teams that need data processed inside Europe.
Full pricing, benchmark table and release notes on the Mistral Large 2 page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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GPT-5.2 wins on more of the categories we score — coding, research, reasoning — so it is the better default of the two. Mistral Large 2 is the better pick when your work is mostly balanced team usage with EU data residency requirements. Neither is universally "better": GPT-5.2 is aimed at serious coding and complex product work, Mistral Large 2 at balanced team usage with EU data residency requirements.
GPT-5.2 is cheaper at $1.75/1M input and $14/1M output. Mistral Large 2 costs $3/1M input and $9/1M output.
GPT-5.2 has the larger context window at 200K tokens vs Mistral Large 2's 128K. For large document analysis, GPT-5.2 is the stronger pick.
GPT-5.2 is better for coding with a score of 85 vs Mistral Large 2's 72 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Both GPT-5.2 and Mistral Large 2 have similar speed profiles — rated balanced. Neither will be the bottleneck if latency is your deciding factor.
Superseded by GPT-5.4 for most use cases. Claude Sonnet 4.6 leads on both coding and writing quality. Avoid it if you're starting a new project — GPT-5.4 is cheaper and more capable. That is the main case for looking at Mistral Large 2 instead.
Not the best in any single benchmark category. Less community momentum than OpenAI, Anthropic, or Google. Avoid it if you want best-in-class performance for any specific use case — the frontier leaders win. Against GPT-5.2 specifically, the gap shows up most on coding (85 vs 72).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-5.2 runs $45.50 (at $1.75/1M in and $14/1M out); Mistral Large 2 runs $48.00 (at $3/1M in and $9/1M out). That is a $2.50/month difference — GPT-5.2 is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GPT-5.2 for serious coding and complex product work, with Mistral Large 2 handling balanced team usage with EU data residency requirements. Since GPT-5.2 is both the stronger and the cheaper option here, a split mainly makes sense if Mistral Large 2 covers a capability you specifically need.