DeepSeek R1 wins on coding (84 vs 72) and price ($0.55 vs $3/1M input). Mistral Large 2 wins on writing quality. For most workflows, DeepSeek R1 is the stronger default — open-source o1-class reasoning at a fraction of the cost.
The safest DeepSeek R1 vs Mistral Large 2 default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
DeepSeek R1 is the strongest answer here for DeepSeek R1 vs Mistral Large 2 — pick it when quality of output matters more than the $0.55/1M/1M input you pay for it.
GPT-5.1-Codex-Max is the cheaper way in for DeepSeek R1 vs Mistral Large 2, at $1.25/1M/1M input against DeepSeek R1's $0.55/1M/1M.
Mistral Large 2 is the fastest of these for DeepSeek R1 vs Mistral Large 2 — worth it when latency is what the reader notices, not the last few points of reasoning depth.
DeepSeek R1 leads on coding with a score of 84 vs 72 for Mistral Large 2.
DeepSeek R1 is cheaper at $0.55/1M input tokens vs $3/1M for Mistral Large 2.
DeepSeek R1 is the stronger default for coding tasks.
Choose DeepSeek R1 for math, science, complex reasoning, and multi-step problem solving at budget cost. Its coding and research scores are what carry the recommendation here.
Choose Mistral Large 2 when your work is mostly balanced team usage with EU data residency requirements — that is the workload it was tuned for.
Both models serve different primary workflows — DeepSeek R1 for math and science, 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 DeepSeek R1 vs Mistral Large 2 answer changes when cost, speed, or long-document depth leads the decision.
DeepSeek / Budget / Mar 24, 2026
Open-source o1-class reasoning at a fraction of the cost.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
Speed matters — R1's deliberate reasoning makes it wrong for interactive or high-throughput use cases.
Where the DeepSeek R1 vs Mistral Large 2 recommendation shifts once you weigh price or latency differently.
Open-source o1-class reasoning at a fraction of the cost.
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 |
|---|---|---|---|---|---|---|---|---|
| DeepSeek R1DeepSeek | $0.55/1M | $2.19/1M | $9.88 | 128k tokens | Deliberate | 84 | 60 | 89 |
| 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 DeepSeek R1 vs Mistral Large 2, what it is genuinely good at, and where we would steer you away from it.
Our pick for DeepSeek R1 vs Mistral Large 2. It scores 84/100 on the coding axis we weight this page by, and nothing else in this shortlist matches it on output quality.
Open-source reasoning model that matches o1-class performance on math, science, and complex coding at a fraction of the cost — the best open alternative to proprietary reasoning models.
Speed matters — R1's deliberate reasoning makes it wrong for interactive or high-throughput use cases.
The open-source reasoning model benchmark. If you need o1-class thinking at open-source pricing, nothing else competes.
Full pricing, benchmark table and release notes on the DeepSeek R1 page.
The fastest model in this shortlist for DeepSeek R1 vs Mistral Large 2. Pick it when turnaround is what your readers or users notice.
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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DeepSeek R1 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": DeepSeek R1 is aimed at math and science, Mistral Large 2 at balanced team usage with EU data residency requirements.
DeepSeek R1 is cheaper at $0.55/1M input and $2.19/1M output. Mistral Large 2 costs $3/1M input and $9/1M output.
Both DeepSeek R1 and Mistral Large 2 have the same 128K context window.
DeepSeek R1 is better for coding with a score of 84 vs Mistral Large 2's 72 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Mistral Large 2 is faster with a balanced speed rating (score: 3) vs DeepSeek R1's deliberate rating (score: 1). Speed matters most for interactive and high-throughput work; for batch jobs the DeepSeek R1 latency penalty is usually invisible.
Slow — deliberate reasoning takes significantly longer than standard models. Overkill for routine tasks where a faster model gets the same result. Same data sovereignty concerns as DeepSeek V3 for regulated industries. Avoid it if speed matters — R1's deliberate reasoning makes it wrong for interactive or high-throughput use cases. 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 DeepSeek R1 specifically, the gap shows up most on coding (84 vs 72).
Take a moderate workload of 10M input and 2M output tokens a month. DeepSeek R1 runs $9.88 (at $0.55/1M in and $2.19/1M out); Mistral Large 2 runs $48.00 (at $3/1M in and $9/1M out). That is a $38.12/month difference — DeepSeek R1 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 DeepSeek R1 for math and science, with Mistral Large 2 handling balanced team usage with EU data residency requirements. Since DeepSeek R1 is both the stronger and the cheaper option here, a split mainly makes sense if Mistral Large 2 covers a capability you specifically need.