# GPT-5-Codex vs Mistral Large 3

> Mistral Large 3 is the stronger model overall, scoring 39.1 to 37.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-5-codex-vs-mistral-large-3
- Last updated: 2026-10-11
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. GPT-5-Codex scores higher in 2 categories and Mistral Large 3 in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5-Codex leads 30.9 to 15.2.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 70.3% for GPT-5-Codex and 50.9% for Mistral Large 3.
- Mistral Large 3 is cheaper at $0.25 / $0.75 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
- GPT-5-Codex accepts more context: 400K tokens versus 262K.
- Mistral Large 3 has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 37.9 | 39.1 |
| Rank | 192 | 176 |
| Context | 400K | 262K |
| Input $/M | $1.25 | $0.25 |
| Output $/M | $10 | $0.75 |
| Weights | Proprietary | Open |

## Coding

- GPT-5-Codex: 42.4 (#103)
- Mistral Large 3: 34.4 (#237)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| LMArena WebDev | — | 1230 |
| WeirdML | 54.5% | — |
| LMArena Coding | — | 1448 |

## Agentic & Tool Use

- GPT-5-Codex: 31.0 (#72)
- Mistral Large 3: —

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| Terminal-Bench | 44.3% | — |

## Reasoning

- GPT-5-Codex: 30.9 (#83)
- Mistral Large 3: 15.2 (#319)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | 50.9% |
| NYT Connections (extended) | — | 7.5% |
| Thematic Generalization | — | 23% |
| LMArena Hard Prompts | — | 1429 |

## Math

- GPT-5-Codex: —
- Mistral Large 3: 38.7 (#129)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| LMArena Math | — | 1414 |

## Knowledge

- GPT-5-Codex: —
- Mistral Large 3: 36.0 (#177)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| Vectara Hallucination Rate | — | 14.5% |
| LMArena Expert | — | 1421 |

## Multimodal

- GPT-5-Codex: —
- Mistral Large 3: 38.2 (#66)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| LMArena Vision | — | 1221 |

## Multilingual

- GPT-5-Codex: —
- Mistral Large 3: 52.5 (#84)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | — | 1413 |
| LMArena Chinese | — | 1447 |
| LMArena French | — | 1455 |
| LMArena German | — | 1437 |
| LMArena Japanese | — | 1394 |
| LMArena Korean | — | 1384 |
| LMArena Russian | — | 1411 |
| LMArena Spanish | — | 1440 |

## Instruction Following

- GPT-5-Codex: —
- Mistral Large 3: 74.0 (#108)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | — | 1403 |

## Long Context

- GPT-5-Codex: —
- Mistral Large 3: 43.1 (#105)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | — | 1413 |

## Writing & Preference

- GPT-5-Codex: —
- Mistral Large 3: 60.0 (#101)

| Benchmark | GPT-5-Codex | Mistral Large 3 |
|---|---|---|
| LMArena Text | — | 1428 |
| LMArena Creative Writing | — | 1386 |
| EQ-Bench Creative Writing | — | 1412 |
| LMArena Multi-Turn | — | 1429 |

## FAQ

### Is GPT-5-Codex better than Mistral Large 3?

Mistral Large 3 is the stronger model overall, scoring 39.1 to 37.9 on the Noometry Index.

### Which is cheaper, GPT-5-Codex 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-Codex lists at $1.25 and $10.

### Is GPT-5-Codex or Mistral Large 3 better for coding?

GPT-5-Codex scores higher on coding benchmarks: 42.4 versus 34.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-5-Codex does, with 400K tokens against 262K.

### How many benchmarks do GPT-5-Codex and Mistral Large 3 share?

1 benchmark has published results for both models. GPT-5-Codex has 3 scored results on Noometry and Mistral Large 3 has 24.