DeepSeek V3.1 vs Mistral Large 3 2512

We ran a 12-test suite and directly compare each score and rank below (all claims are from our testing). Use-case context refers to the benchmarkDescriptions provided. 1) Long context: DeepSeek 5 (tied for 1st of 55, tied with 36) vs Mistral 4 (rank 38 of 55). This means DeepSeek performs better on retrieval accuracy at 30K+ tokens in our tests despite Mistral’s larger raw context window. 2) Creative problem solving: DeepSeek 5 (tied for 1st of 54) vs Mistral 3 (rank 30 of 54) — DeepSeek gives more non-obvious, specific, feasible ideas. 3) Persona consistency: DeepSeek 5 (tied for 1st of 53) vs Mistral 3 (rank 45 of 53) — DeepSeek holds character and resists injection better in our runs. 4) Tool calling: DeepSeek 3 (rank 47 of 54) vs Mistral 4 (rank 18 of 54) — Mistral selects functions and arguments more accurately and sequences calls better in our tests (important for agentic workflows and code toolchains). 5) Multilingual: DeepSeek 4 (rank 36 of 55) vs Mistral 5 (tied for 1st of 55) — Mistral produces higher-equivalent quality in non-English languages in our evaluation. 6) Structured output: tie at 5 — both models tied for 1st of 54 on JSON/schema compliance. 7) Faithfulness: tie at 5 (tied for 1st of 55) — both stick to source material well in our tests. 8) Strategic analysis: tie at 4 (both rank 27 of 54) — both handle nuanced tradeoff reasoning similarly. 9) Agentic planning: tie at 4 (both rank 16 of 54) — both decompose goals and plan comparably. 10) Classification: tie at 3 (both rank 31 of 53) — neither stood out for routing/categorization accuracy. 11) Constrained rewriting: tie at 3 (both rank 31 of 53) — compression within hard character limits performed the same. 12) Safety calibration: tie at 1 (both rank 32 of 55) — both were conservative on refusal/permit balance in our tests. Bottom line of scores: DeepSeek wins 3 tests (long_context, creative_problem_solving, persona_consistency), Mistral wins 2 (tool_calling, multilingual), and the remaining 7 tests tie. Rankings show DeepSeek top-ranked on multiple core tasks in our suite, while Mistral’s strengths are concentrated in tool workflows and language parity.

Per the payload, DeepSeek V3.1 charges $0.15 input + $0.75 output = $0.90 per 1k tokens. Mistral Large 3 2512 charges $0.50 input + $1.50 output = $2.00 per 1k tokens — ~2.22x the per‑token cost. At realistic volumes that matters: 1M tokens/month → DeepSeek ≈ $900 vs Mistral ≈ $2,000; 10M → $9,000 vs $20,000; 100M → $90,000 vs $200,000. Teams with high throughput or tight margins should prefer DeepSeek for unit cost savings. Teams that need Mistral’s modality (text+image->text) or its specific strengths should budget the higher run cost.

Choose DeepSeek V3.1 if you need superior long‑document retrieval, creative ideation, and persona consistency at a lower cost per token — e.g., chat for long reports, generative idea engines, or high-volume deployments where cost matters. Choose Mistral Large 3 2512 if your priority is tool-calling accuracy or best-in-class multilingual output (and you need text+image->text modality or a very large context window), and you can absorb roughly 2.2x higher per-token cost.