# Mistral Large vs Qwen3.7 Flash

> Qwen3.7 Flash is the stronger model overall, scoring 39.5 to 31.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/mistral-large-vs-qwen3-7-flash
- Last updated: 2026-10-10
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. Mistral Large scores higher in 0 categories and Qwen3.7 Flash in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Flash leads 37.3 to 17.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 8.5% for Mistral Large and 86.7% for Qwen3.7 Flash.
- Qwen3.7 Flash is cheaper at $0.03 / $0.13 per million input/output tokens, against $2 / $6 for Mistral Large.
- Qwen3.7 Flash accepts more context: 1M tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.

## Snapshot

| | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 31.6 | 39.5 |
| Rank | 258 | 152 |
| Context | 131K | 1M |
| Input $/M | $2 | $0.03 |
| Output $/M | $6 | $0.13 |
| Weights | Open | Proprietary |

## Coding

- Mistral Large: 33.8 (#240)
- Qwen3.7 Flash: —

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| SciCode | 36.2% | — |
| BigCodeBench Instruct | 30% | — |
| LiveBench Coding | 47.1% | — |
| LMArena Coding | 1277 | — |
| BigCodeBench Complete | 38.3% | — |
| ALE-Bench | 264.7 | — |
| HumanEval+ | 62.2% | — |
| MBPP+ | 59.5% | — |

## Agentic & Tool Use

- Mistral Large: 28.4 (#83)
- Qwen3.7 Flash: —

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| Berkeley Function Calling Leaderboard | 38.4% | — |

## Reasoning

- Mistral Large: 15.4 (#305)
- Qwen3.7 Flash: 27.3 (#108)

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| Epoch Capabilities Index | 128.52 | 144.62 |
| SimpleBench | 22.5% | — |
| NYT Connections (extended) | — | 43.8% |
| CritPt | 0% | — |
| Chess Puzzles | — | 23% |
| LiveBench Reasoning | 43.5% | — |
| LMArena Hard Prompts | 1257 | — |
| Mystery Game Puzzles | — | 15% |
| DTBench | 65.1% | — |
| LiveBench Data Analysis | 50.1% | — |
| LMCA | 16.7% | — |
| ForecastBench | 57.1 | — |
| LiveBench | 48.4% | — |

## Math

- Mistral Large: 17.1 (#285)
- Qwen3.7 Flash: 37.3 (#100)

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | 86.7% |
| FrontierMath (Tiers 1-3) | — | 19.3% |
| Omni-MATH | 28.1% | — |
| LiveBench Math | 42.5% | — |
| LMArena Math | 1262 | — |
| MATH Level 5 | 50.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |

## Knowledge

- Mistral Large: 30.0 (#224)
- Qwen3.7 Flash: 48.9 (#69)

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| GPQA Diamond | 51.3% | 82.3% |
| MMLU-Pro | 59.9% | — |
| Confabulations | 21.4% | — |
| Vectara Hallucination Rate | 4.5% | — |
| GPQA (HELM) | 43.5% | — |
| LMArena Expert | 1232 | — |
| MMLU | 80% | — |

## Multilingual

- Mistral Large: 40.1 (#214)
- Qwen3.7 Flash: —

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| LMArena Non-English | 1237 | — |
| LMArena Chinese | 1240 | — |
| LMArena French | 1324 | — |
| LMArena German | 1254 | — |
| LMArena Japanese | 1187 | — |
| LMArena Korean | 1202 | — |
| LMArena Russian | 1257 | — |
| LMArena Spanish | 1268 | — |

## Instruction Following

- Mistral Large: 68.1 (#186)
- Qwen3.7 Flash: —

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| LiveBench Instruction Following | 67.9% | — |
| IFEval | 87.7% | — |
| LMArena Instruction Following | 1249 | — |

## Long Context

- Mistral Large: 38.3 (#194)
- Qwen3.7 Flash: —

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| LMArena Longer Query | 1262 | — |

## Writing & Preference

- Mistral Large: 40.9 (#237)
- Qwen3.7 Flash: —

| Benchmark | Mistral Large | Qwen3.7 Flash |
|---|---|---|
| LMArena Text | 1266 | — |
| LMArena Creative Writing | 1243 | — |
| Short-Story Creative Writing | 69% | — |
| EQ-Bench Creative Writing | 985 | — |
| WildBench | 80.1% | — |
| LMArena Multi-Turn | 1260 | — |
| LiveBench Language | 39.4% | — |

## FAQ

### Is Mistral Large better than Qwen3.7 Flash?

Qwen3.7 Flash is the stronger model overall, scoring 39.5 to 31.6 on the Noometry Index.

### Which is cheaper, Mistral Large or Qwen3.7 Flash?

Qwen3.7 Flash is cheaper. It lists at $0.03 per million input tokens and $0.13 per million output tokens; Mistral Large lists at $2 and $6.

### Which has the bigger context window?

Qwen3.7 Flash does, with 1M tokens against 131K.

### How many benchmarks do Mistral Large and Qwen3.7 Flash share?

3 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and Qwen3.7 Flash has 7.