Model comparison
GPT-5.1 vs Mistral Large 3
GPT-5.1 is the stronger model overall, scoring 48.8 to 38.6 on the Noometry Index. Mistral Large 3 costs 9.2× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Mistral AISummary
- They share 20 benchmarks with published results for both. GPT-5.1 scores higher in 9 categories and Mistral Large 3 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.1 leads 39.9 to 14.6.
- Mistral Large 3 is cheaper at $0.25 / $0.75 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 262K.
- Mistral Large 3 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1 | Mistral Large 3 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 48.8 | 38.6 |
| Released | 2025-11-13 | 2025-12-02 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 8K |
| Input $ / M tokens | $1.25 | $0.25 |
| Output $ / M tokens | $10 | $0.75 |
| Results tracked | 63 | 24 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.9 (#59), Mistral Large 3: 34.1 (#230)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| LMArena WebDev | 1395 | 1230 |
| LMArena Coding | 1454 | 1447 |
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| SciCode | 43.3% | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| LiveBench Coding | 72.5% | — |
| ALE-Bench | 1,192 | — |
Agentic & Tool Use Not comparable
GPT-5.1: 32.5 (#55), Mistral Large 3: —
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| DeepResearch Bench | 42.8% | — |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.9 (#50), Mistral Large 3: 14.6 (#314)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| LMArena Hard Prompts | 1457 | 1429 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | — | 50.9% |
| NYT Connections (extended) | — | 7.5% |
| ARC-AGI-1 | 72.8% | — |
| CritPt | 4.9% | — |
| Chess Puzzles | 32% | — |
| EnigmaEval | 11.2% | — |
| Thematic Generalization | — | 23% |
| LiveBench Reasoning | 95.8% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 90.1% | — |
| LiveBench Data Analysis | 72.1% | — |
| LMCA | 43.9% | — |
| Epoch Capabilities Index | 149.66 | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |
Math GPT-5.1 leads
GPT-5.1: 50.3 (#45), Mistral Large 3: 36.0 (#131)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| LMArena Math | 1446 | 1415 |
| OTIS Mock AIME 2024-2025 | 88.6% | — |
| Omni-MATH | 46.4% | — |
| LiveBench Math | 94.5% | — |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5.1 leads
GPT-5.1: 50.5 (#65), Mistral Large 3: 35.5 (#173)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| Vectara Hallucination Rate | 10.9% | 14.5% |
| LMArena Expert | 1470 | 1421 |
| GPQA Diamond | 87.6% | — |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| MMLU-Pro | 57.9% | — |
| GPQA (HELM) | 44.2% | — |
Multimodal GPT-5.1 leads
GPT-5.1: 44.0 (#17), Mistral Large 3: 37.6 (#65)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| LMArena Vision | 1250 | 1222 |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual GPT-5.1 leads
GPT-5.1: 54.0 (#51), Mistral Large 3: 52.6 (#79)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | 1431 | 1412 |
| LMArena Chinese | 1495 | 1448 |
| LMArena French | 1451 | 1456 |
| LMArena German | 1439 | 1438 |
| LMArena Japanese | 1453 | 1399 |
| LMArena Korean | 1401 | 1384 |
| LMArena Russian | 1435 | 1412 |
| LMArena Spanish | 1430 | 1437 |
Instruction Following GPT-5.1 leads
GPT-5.1: 84.0 (#1), Mistral Large 3: 74.1 (#104)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | 1442 | 1402 |
| LiveBench Instruction Following | 93.3% | — |
| IFEval | 93.5% | — |
Long Context GPT-5.1 leads
GPT-5.1: 47.6 (#14), Mistral Large 3: 43.1 (#102)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | 1448 | 1413 |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.7 (#53), Mistral Large 3: 60.2 (#100)
| Benchmark | GPT-5.1 | Mistral Large 3 |
|---|---|---|
| LMArena Text | 1443 | 1427 |
| LMArena Creative Writing | 1427 | 1385 |
| LMArena Multi-Turn | 1449 | 1429 |
| EQ-Bench Creative Writing | — | 1412 |
| WildBench | 86.3% | — |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Mistral Large 3?
GPT-5.1 is the stronger model overall, scoring 48.8 to 38.6 on the Noometry Index. Mistral Large 3 costs 9.2× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Which is cheaper, GPT-5.1 or Mistral Large 3?
Mistral Large 3 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; GPT-5.1 lists at $1.25 and $10.
Is GPT-5.1 or Mistral Large 3 better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.9 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1 does, with 400K tokens against 262K.
How many benchmarks do GPT-5.1 and Mistral Large 3 share?
20 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Mistral Large 3 has 24.