# GPT-5.2 Codex vs Mistral Large

> GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 31.9 on the Noometry Index. Mistral Large costs 1.6× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-2-codex-vs-mistral-large
- Last updated: 2026-10-11
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. GPT-5.2 Codex scores higher in 2 categories and Mistral Large in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.2 Codex leads 41.0 to 28.6.
- Mistral Large is cheaper at $2 / $6 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- GPT-5.2 Codex accepts more context: 400K tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.2 Codex | Mistral Large |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 42.6 | 31.9 |
| Rank | 111 | 263 |
| Context | 400K | 131K |
| Input $/M | $1.75 | $2 |
| Output $/M | $14 | $6 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.2 Codex: 45.5 (#71)
- Mistral Large: 34.3 (#240)

| Benchmark | GPT-5.2 Codex | Mistral Large |
|---|---|---|
| ALE-Bench | 1,300 | 264.7 |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1339 | — |
| SWE-bench Multilingual | 66.3% | — |
| SciCode | — | 36.2% |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| LMArena Coding | — | 1277 |
| BigCodeBench Complete | — | 38.3% |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |

## Agentic & Tool Use

- GPT-5.2 Codex: 41.0 (#22)
- Mistral Large: 28.6 (#89)

| Benchmark | GPT-5.2 Codex | Mistral Large |
|---|---|---|
| Terminal-Bench | 66.5% | — |
| Berkeley Function Calling Leaderboard | — | 38.4% |

## Reasoning

- GPT-5.2 Codex: —
- Mistral Large: 15.8 (#310)

| Benchmark | GPT-5.2 Codex | Mistral Large |
|---|---|---|
| SimpleBench | — | 22.5% |
| 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

- GPT-5.2 Codex: —
- Mistral Large: 18.2 (#291)

| Benchmark | GPT-5.2 Codex | 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

- GPT-5.2 Codex: —
- Mistral Large: 30.1 (#230)

| Benchmark | GPT-5.2 Codex | 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

- GPT-5.2 Codex: —
- Mistral Large: 40.0 (#219)

| Benchmark | GPT-5.2 Codex | 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

- GPT-5.2 Codex: —
- Mistral Large: 67.9 (#191)

| Benchmark | GPT-5.2 Codex | Mistral Large |
|---|---|---|
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
| LMArena Instruction Following | — | 1249 |

## Long Context

- GPT-5.2 Codex: —
- Mistral Large: 38.3 (#199)

| Benchmark | GPT-5.2 Codex | Mistral Large |
|---|---|---|
| LMArena Longer Query | — | 1261 |

## Writing & Preference

- GPT-5.2 Codex: —
- Mistral Large: 40.7 (#242)

| Benchmark | GPT-5.2 Codex | 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 GPT-5.2 Codex better than Mistral Large?

GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 31.9 on the Noometry Index. Mistral Large costs 1.6× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.2 Codex or Mistral Large?

Mistral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.

### Is GPT-5.2 Codex or Mistral Large better for coding?

GPT-5.2 Codex scores higher on coding benchmarks: 45.5 versus 34.3 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.2 Codex does, with 400K tokens against 131K.

### How many benchmarks do GPT-5.2 Codex and Mistral Large share?

1 benchmark has published results for both models. GPT-5.2 Codex has 5 scored results on Noometry and Mistral Large has 51.