# GPT-5.3 Chat vs Mistral Large

> GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 31.9 on the Noometry Index. Mistral Large costs 1.6× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-3-chat-vs-mistral-large
- Last updated: 2026-10-11
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. GPT-5.3 Chat scores higher in 8 categories and Mistral Large in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-5.3 Chat leads 63.1 to 40.7.
- Mistral Large is cheaper at $2 / $6 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- Mistral Large accepts more context: 131K tokens versus 128K.
- Mistral Large has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 42.8 | 31.9 |
| Rank | 109 | 263 |
| Context | 128K | 131K |
| Input $/M | $1.75 | $2 |
| Output $/M | $14 | $6 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.3 Chat: 41.4 (#124)
- Mistral Large: 34.3 (#240)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| LMArena Coding | 1408 | 1277 |
| SciCode | — | 36.2% |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| ALE-Bench | — | 264.7 |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |

## Agentic & Tool Use

- GPT-5.3 Chat: —
- Mistral Large: 28.6 (#89)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |

## Reasoning

- GPT-5.3 Chat: 28.5 (#102)
- Mistral Large: 15.8 (#310)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1257 |
| SimpleBench | — | 22.5% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 43.5% |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| Epoch Capabilities Index | — | 128.52 |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |

## Math

- GPT-5.3 Chat: 38.2 (#142)
- Mistral Large: 18.2 (#291)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| LMArena Math | 1389 | 1262 |
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |

## Knowledge

- GPT-5.3 Chat: 38.8 (#140)
- Mistral Large: 30.1 (#230)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| LMArena Expert | 1397 | 1232 |
| GPQA Diamond | — | 51.3% |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |

## Multilingual

- GPT-5.3 Chat: 50.3 (#124)
- Mistral Large: 40.0 (#219)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| LMArena Non-English | 1382 | 1237 |
| LMArena Chinese | 1432 | 1240 |
| LMArena French | 1397 | 1325 |
| LMArena German | 1384 | 1254 |
| LMArena Japanese | 1352 | 1188 |
| LMArena Korean | 1346 | 1202 |
| LMArena Russian | 1400 | 1257 |
| LMArena Spanish | 1371 | 1268 |

## Instruction Following

- GPT-5.3 Chat: 72.8 (#129)
- Mistral Large: 67.9 (#191)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1378 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |

## Long Context

- GPT-5.3 Chat: 42.6 (#120)
- Mistral Large: 38.3 (#199)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1396 | 1261 |

## Writing & Preference

- GPT-5.3 Chat: 63.1 (#68)
- Mistral Large: 40.7 (#242)

| Benchmark | GPT-5.3 Chat | Mistral Large |
|---|---|---|
| LMArena Text | 1389 | 1266 |
| LMArena Creative Writing | 1355 | 1243 |
| EQ-Bench Creative Writing | 1690 | 985 |
| LMArena Multi-Turn | 1412 | 1260 |
| Short-Story Creative Writing | — | 69% |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |

## FAQ

### Is GPT-5.3 Chat better than Mistral Large?

GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 31.9 on the Noometry Index. Mistral Large costs 1.6× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.3 Chat or Mistral Large?

Mistral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.

### Is GPT-5.3 Chat or Mistral Large better for coding?

GPT-5.3 Chat scores higher on coding benchmarks: 41.4 versus 34.3 in the Noometry coding category.

### Which has the bigger context window?

Mistral Large does, with 131K tokens against 128K.

### How many benchmarks do GPT-5.3 Chat and Mistral Large share?

18 benchmarks have published results for both models. GPT-5.3 Chat has 18 scored results on Noometry and Mistral Large has 51.