# Mistral Large vs o1

> o1 is the stronger model overall, scoring 40.9 to 31.9 on the Noometry Index. Mistral Large costs 8.8× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/mistral-large-vs-o1
- Last updated: 2026-10-11
- Shared benchmarks: 36

## Summary

- They share 36 benchmarks with published results for both. Mistral Large scores higher in 1 category and o1 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where o1 leads 36.1 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 8.5% for Mistral Large and 73.3% for o1.
- Mistral Large is cheaper at $2 / $6 per million input/output tokens, against $15 / $60 for o1.
- o1 accepts more context: 200K tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.

## Snapshot

| | Mistral Large | o1 |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 31.9 | 40.9 |
| Rank | 263 | 143 |
| Context | 131K | 200K |
| Input $/M | $2 | $15 |
| Output $/M | $6 | $60 |
| Weights | Open | Proprietary |

## Coding

- Mistral Large: 34.3 (#240)
- o1: 46.1 (#70)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| LiveBench Coding | 47.1% | 69.7% |
| LMArena Coding | 1277 | 1367 |
| HumanEval+ | 62.2% | 89% |
| MBPP+ | 59.5% | 80.2% |
| Aider Polyglot | — | 61.7% |
| SciCode | 36.2% | — |
| WeirdML | — | 47.6% |
| BigCodeBench Instruct | 30% | — |
| BigCodeBench Complete | 38.3% | — |
| CadEval | — | 56% |
| ALE-Bench | 264.7 | — |

## Agentic & Tool Use

- Mistral Large: 28.6 (#89)
- o1: 24.6 (#117)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 38.4% | — |
| Cybench | — | 10% |
| METR Time Horizons | — | 51.1% |

## Reasoning

- Mistral Large: 15.8 (#310)
- o1: 27.9 (#111)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| SimpleBench | 22.5% | 41.7% |
| LiveBench Reasoning | 43.5% | 91.6% |
| LMArena Hard Prompts | 1257 | 1371 |
| DTBench | 65.1% | 74.7% |
| LiveBench Data Analysis | 50.1% | 65.5% |
| LMCA | 16.7% | 22.3% |
| Epoch Capabilities Index | 128.52 | 141.91 |
| LiveBench | 48.4% | 75.7% |
| ARC-AGI-1 | — | 30.7% |
| CritPt | 0% | — |
| Chess Puzzles | — | 15% |
| EnigmaEval | — | 5.7% |
| ForecastBench | 57.1 | — |

## Math

- Mistral Large: 18.2 (#291)
- o1: 36.1 (#175)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | 73.3% |
| LiveBench Math | 42.5% | 80.3% |
| LMArena Math | 1262 | 1388 |
| MATH Level 5 | 50.3% | 94.7% |
| FrontierMath (Feb 2025 set) | 0.3% | 9.3% |
| FrontierMath (Tiers 1-3) | — | 14.7% |
| Omni-MATH | 28.1% | — |

## Knowledge

- Mistral Large: 30.1 (#230)
- o1: 41.5 (#110)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| GPQA Diamond | 51.3% | 76.8% |
| Confabulations | 21.4% | 11.7% |
| LMArena Expert | 1232 | 1361 |
| Humanity's Last Exam | — | 8% |
| SimpleQA Verified | — | 41.1% |
| MMLU-Pro | 59.9% | — |
| Vectara Hallucination Rate | 4.5% | — |
| GPQA (HELM) | 43.5% | — |
| MMLU | 80% | — |

## Multimodal

- Mistral Large: —
- o1: 34.2 (#93)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| LMArena Vision | — | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |

## Multilingual

- Mistral Large: 40.0 (#219)
- o1: 48.6 (#142)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| LMArena Non-English | 1237 | 1358 |
| LMArena Chinese | 1240 | 1394 |
| LMArena French | 1325 | 1344 |
| LMArena German | 1254 | 1337 |
| LMArena Japanese | 1188 | 1346 |
| LMArena Korean | 1202 | 1396 |
| LMArena Russian | 1257 | 1356 |
| LMArena Spanish | 1268 | 1345 |

## Instruction Following

- Mistral Large: 67.9 (#191)
- o1: 74.8 (#86)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| LiveBench Instruction Following | 67.9% | 81.5% |
| LMArena Instruction Following | 1249 | 1367 |
| IFEval | 87.7% | — |

## Long Context

- Mistral Large: 38.3 (#199)
- o1: 50.3 (#9)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| LMArena Longer Query | 1261 | 1378 |
| Fiction.LiveBench | — | 83.3% |

## Writing & Preference

- Mistral Large: 40.7 (#242)
- o1: 55.6 (#144)

| Benchmark | Mistral Large | o1 |
|---|---|---|
| LMArena Text | 1266 | 1366 |
| LMArena Creative Writing | 1243 | 1348 |
| Short-Story Creative Writing | 69% | 70.2% |
| LMArena Multi-Turn | 1260 | 1369 |
| LiveBench Language | 39.4% | 65.4% |
| EQ-Bench Creative Writing | 985 | — |
| WildBench | 80.1% | — |

## FAQ

### Is Mistral Large better than o1?

o1 is the stronger model overall, scoring 40.9 to 31.9 on the Noometry Index. Mistral Large costs 8.8× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.

### Which is cheaper, Mistral Large or o1?

Mistral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; o1 lists at $15 and $60.

### Is Mistral Large or o1 better for coding?

o1 scores higher on coding benchmarks: 46.1 versus 34.3 in the Noometry coding category.

### Which has the bigger context window?

o1 does, with 200K tokens against 131K.

### How many benchmarks do Mistral Large and o1 share?

36 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and o1 has 52.