# Codestral vs Mistral Large

> Mistral Large is the stronger model overall, scoring 31.9 to 30.6 on the Noometry Index. Codestral costs 6.7× less per token, which makes it the better buy when Mistral Large's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/codestral-vs-mistral-large
- Last updated: 2026-10-12
- Shared benchmarks: 5

## Summary

- They share 5 benchmarks with published results for both. Codestral scores higher in 1 category and Mistral Large in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where Mistral Large leads 34.3 to 27.3.
- The biggest single-benchmark swing is BigCodeBench Complete: 52.5% for Codestral and 38.3% for Mistral Large.
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $2 / $6 for Mistral Large.
- Codestral accepts more context: 256K tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.

## Snapshot

| | Codestral | Mistral Large |
|---|---|---|
| Provider | Mistral AI | Mistral AI |
| Noometry Index | 30.6 | 31.9 |
| Rank | 290 | 263 |
| Context | 256K | 131K |
| Input $/M | $0.30 | $2 |
| Output $/M | $0.90 | $6 |
| Weights | Proprietary | Open |

## Coding

- Codestral: 27.3 (#321)
- Mistral Large: 34.3 (#240)

| Benchmark | Codestral | Mistral Large |
|---|---|---|
| BigCodeBench Instruct | 41.8% | 30% |
| BigCodeBench Complete | 52.5% | 38.3% |
| ALE-Bench | 137.78 | 264.7 |
| HumanEval+ | 73.8% | 62.2% |
| MBPP+ | 61.9% | 59.5% |
| Aider Polyglot | 11.1% | — |
| SciCode | — | 36.2% |
| LiveBench Coding | — | 47.1% |
| LMArena Coding | — | 1277 |

## Agentic & Tool Use

- Codestral: —
- Mistral Large: 28.6 (#89)

| Benchmark | Codestral | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |

## Reasoning

- Codestral: 19.8 (#251)
- Mistral Large: 15.8 (#310)

| Benchmark | Codestral | Mistral Large |
|---|---|---|
| SimpleBench | — | 22.5% |
| Kagi LLM Benchmark | 32.5% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 43.5% |
| LMArena Hard Prompts | — | 1257 |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| Epoch Capabilities Index | — | 128.52 |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |

## Math

- Codestral: —
- Mistral Large: 18.2 (#291)

| Benchmark | Codestral | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| LMArena Math | — | 1262 |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |

## Knowledge

- Codestral: —
- Mistral Large: 30.1 (#230)

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

## Multilingual

- Codestral: —
- Mistral Large: 40.0 (#219)

| Benchmark | Codestral | Mistral Large |
|---|---|---|
| LMArena Non-English | — | 1237 |
| LMArena Chinese | — | 1240 |
| LMArena French | — | 1325 |
| LMArena German | — | 1254 |
| LMArena Japanese | — | 1188 |
| LMArena Korean | — | 1202 |
| LMArena Russian | — | 1257 |
| LMArena Spanish | — | 1268 |

## Instruction Following

- Codestral: —
- Mistral Large: 67.9 (#191)

| Benchmark | Codestral | Mistral Large |
|---|---|---|
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
| LMArena Instruction Following | — | 1249 |

## Long Context

- Codestral: —
- Mistral Large: 38.3 (#199)

| Benchmark | Codestral | Mistral Large |
|---|---|---|
| LMArena Longer Query | — | 1261 |

## Writing & Preference

- Codestral: —
- Mistral Large: 40.7 (#242)

| Benchmark | Codestral | Mistral Large |
|---|---|---|
| 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 Codestral better than Mistral Large?

Mistral Large is the stronger model overall, scoring 31.9 to 30.6 on the Noometry Index. Codestral costs 6.7× less per token, which makes it the better buy when Mistral Large's lead doesn't matter for your workload.

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

Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Mistral Large lists at $2 and $6.

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

Mistral Large scores higher on coding benchmarks: 34.3 versus 27.3 in the Noometry coding category.

### Which has the bigger context window?

Codestral does, with 256K tokens against 131K.

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

5 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Mistral Large has 51.