Mistral · model passport
Mistral Large
mistral-large- 128,000 context
- Best percentile
- 22.9LiveCodeBench (Artificial Analysis)
- Composite standing
- 32.1shrunk · 3/27 axes
What the data says
Benchmarked on 3 of 27 tracked axes. Every figure below is the model's own published result on that board.
Weakest axes
- 0.7 pct — LiveBench (1.9%)
Uneven: 22.2 percentile points between its best and worst axis — 22.9 pct on LiveCodeBench (Artificial Analysis) versus 0.7 pct on LiveBench.
- LiveCodeBench (Artificial Analysis)22.9 pct17.8%Verify →
- MMLU-Pro (Artificial Analysis)18.7 pct51.5%Verify →
- LiveBench0.7 pct1.9%Verify →
- OverallNot benchmarked
- WebDev ArenaNot benchmarked
- Vision ArenaNot benchmarked
- Factuality ArenaNot benchmarked
- Search ArenaNot benchmarked
- Document ArenaNot benchmarked
- Agent ArenaNot benchmarked
- GPQA DiamondNot benchmarked
- AIME (math)Not benchmarked
- FrontierMathNot benchmarked
- SimpleQA VerifiedNot benchmarked
- ARC-AGI-2Not benchmarked
- Humanity's Last ExamNot benchmarked
- SimpleBenchNot benchmarked
- BFCL v4 (function calling)Not benchmarked
- SWE-bench Verified (live board)Not benchmarked
- Terminal-Bench 4.0Not benchmarked
- IFBench (Artificial Analysis)Not benchmarked
- HHEM Hallucination (Vectara)Not benchmarked
- SWE-bench VerifiedNot benchmarked
- Terminal-BenchNot benchmarked
- CodingNot benchmarked
- Instruction followingNot benchmarked
- Hard promptsNot benchmarked
Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.
How to run it
Use it hosted
mistralai/mistral-largeAPI parameters this endpoint supports
Example request
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"mistralai/mistral-large","messages":[{"role":"user","content":"Hello"}]}'Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.
mistral-large stands at 32.1 (raw mean 14.1) — measured on 3 of 27 axes.
mistral-large scores at percentile 0.7 on LiveBench.
Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →