DeepSeek R1 vs Mistral Large 2
Head-to-head specifications
| Metric | DeepSeek R1 | Mistral Large 2 | Difference |
|---|---|---|---|
| MMLU (general capability) | 90.8% | 84.0% | +6.8% |
| Context window | 128K tokens | 128K tokens | — |
| Price (input / output per 1M) | Open weights | $2 / $6 | — |
| Access | Open weights | Proprietary API | — |
- DeepSeek R1 leads general capability (MMLU 90.8% vs 84.0%).
Verdict: DeepSeek R1 or Mistral Large 2?
DeepSeek R1 advantages
- General capability (+7%)
Mistral Large 2 advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the DeepSeek R1 if you need the strongest reasoning and accuracy.
Value for money
DeepSeek R1 is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
DeepSeek R1 vs Mistral Large 2: which should you choose?
DeepSeek R1 — DeepSeek large language model (2025) with a 128K-token context window and an MMLU score of 90.8%, released with open weights.
Mistral Large 2 — Mistral AI large language model (2024) with a 128K-token context window and an MMLU score of 84.0%.
DeepSeek R1 vs Mistral Large 2: DeepSeek R1 scores higher on the MMLU benchmark. DeepSeek R1 leads general capability (MMLU 90.8% vs 84.0%).
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the DeepSeek R1 scores 90.8% versus 84.0%. MMLU is a useful proxy for raw knowledge but does not capture instruction-following, coding, tool use, latency or safety, so treat it as one signal among several.
Context window
The DeepSeek R1 handles up to 128K tokens per request, which sets how much documentation, transcript or code it can reason over at once — decisive for retrieval-augmented and long-document workflows.
Pricing and access
DeepSeek R1 is open weights and Mistral Large 2 is proprietary api. Proprietary models bill per token via API; open-weight models can be self-hosted, trading per-call cost for infrastructure you manage. For production, weigh throughput, rate limits and data-residency needs alongside headline price.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the DeepSeek R1 better than the Mistral Large 2?
DeepSeek R1 is the clearly stronger overall choice, winning most of the dimensions that matter. DeepSeek R1 leads general capability (MMLU 90.8% vs 84.0%).
What is the main difference between the DeepSeek R1 and the Mistral Large 2?
DeepSeek R1 leads general capability (MMLU 90.8% vs 84.0%).
Which is better value?
DeepSeek R1 is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
Which should I choose?
Choose the DeepSeek R1 if you need the strongest reasoning and accuracy.
Methodology
Large language models are compared on the MMLU benchmark (a widely-cited 57-subject test of general knowledge and reasoning, reported as a percentage), maximum context window, and published API pricing per million input and output tokens. Open-weight models can also be self-hosted. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit.