# GPT-5 Mini vs Mistral Large 3

> GPT-5 Mini is the stronger model overall, scoring 41.8 to 39.1 on the Noometry Index. Mistral Large 3 costs 1.8× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-mini-vs-mistral-large-3
- Last updated: 2026-10-11
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GPT-5 Mini scores higher in 5 categories and Mistral Large 3 in 4 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 Mini leads 45.6 to 36.0.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 70.3% for GPT-5 Mini and 50.9% for Mistral Large 3.
- Mistral Large 3 is cheaper at $0.25 / $0.75 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- GPT-5 Mini accepts more context: 400K tokens versus 262K.
- Mistral Large 3 has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 41.8 | 39.1 |
| Rank | 128 | 176 |
| Context | 400K | 262K |
| Input $/M | $0.25 | $0.25 |
| Output $/M | $2 | $0.75 |
| Weights | Proprietary | Open |

## Coding

- GPT-5 Mini: 40.1 (#146)
- Mistral Large 3: 34.4 (#237)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| LMArena Coding | 1406 | 1448 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| LMArena WebDev | — | 1230 |
| SWE-bench Multilingual | 39.7% | — |
| SciCode | 39.2% | — |
| WeirdML | 52.7% | — |
| ALE-Bench | 799.77 | — |
| AlgoTune | 1.38 | — |

## Agentic & Tool Use

- GPT-5 Mini: 31.1 (#70)
- Mistral Large 3: —

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| Terminal-Bench | 34.8% | — |
| Berkeley Function Calling Leaderboard | 55.5% | — |
| Vending-Bench 2 | -31.18 | — |

## Reasoning

- GPT-5 Mini: 23.9 (#168)
- Mistral Large 3: 15.2 (#319)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | 50.9% |
| LMArena Hard Prompts | 1380 | 1429 |
| ARC-AGI-2 | 4.4% | — |
| NYT Connections (extended) | — | 7.5% |
| ARC-AGI-1 | 54.3% | — |
| CritPt | 0% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | 8.2% | — |
| Thematic Generalization | — | 23% |
| Mystery Game Puzzles | 10% | — |
| DTBench | 80.5% | — |
| LMCA | 34.2% | — |
| Epoch Capabilities Index | 145.52 | — |
| ForecastBench | 61 | — |

## Math

- GPT-5 Mini: 46.7 (#69)
- Mistral Large 3: 38.7 (#129)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| LMArena Math | 1378 | 1414 |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 86.7% | — |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |

## Knowledge

- GPT-5 Mini: 45.6 (#86)
- Mistral Large 3: 36.0 (#177)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| Vectara Hallucination Rate | 12.9% | 14.5% |
| LMArena Expert | 1379 | 1421 |
| GPQA Diamond | 75% | — |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| GPQA (HELM) | 75.6% | — |

## Multimodal

- GPT-5 Mini: 35.6 (#85)
- Mistral Large 3: 38.2 (#66)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| LMArena Vision | 1202 | 1221 |
| VPCT | 40.2% | — |

## Multilingual

- GPT-5 Mini: 48.9 (#137)
- Mistral Large 3: 52.5 (#84)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | 1363 | 1413 |
| LMArena Chinese | 1385 | 1447 |
| LMArena French | 1386 | 1455 |
| LMArena German | 1366 | 1437 |
| LMArena Japanese | 1341 | 1394 |
| LMArena Korean | 1308 | 1384 |
| LMArena Russian | 1362 | 1411 |
| LMArena Spanish | 1355 | 1440 |

## Instruction Following

- GPT-5 Mini: 76.2 (#46)
- Mistral Large 3: 74.0 (#108)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | 1357 | 1403 |
| IFEval | 92.7% | — |

## Long Context

- GPT-5 Mini: 41.9 (#132)
- Mistral Large 3: 43.1 (#105)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | 1355 | 1413 |
| Fiction.LiveBench | 69.4% | — |

## Writing & Preference

- GPT-5 Mini: 55.2 (#148)
- Mistral Large 3: 60.0 (#101)

| Benchmark | GPT-5 Mini | Mistral Large 3 |
|---|---|---|
| LMArena Text | 1373 | 1428 |
| LMArena Creative Writing | 1325 | 1386 |
| EQ-Bench Creative Writing | 1313 | 1412 |
| LMArena Multi-Turn | 1363 | 1429 |
| Short-Story Creative Writing | 83.1% | — |
| WildBench | 85.5% | — |

## FAQ

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

GPT-5 Mini is the stronger model overall, scoring 41.8 to 39.1 on the Noometry Index. Mistral Large 3 costs 1.8× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.

### Which is cheaper, GPT-5 Mini 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 Mini lists at $0.25 and $2.

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

GPT-5 Mini scores higher on coding benchmarks: 40.1 versus 34.4 in the Noometry coding category.

### Which has the bigger context window?

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

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

21 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Mistral Large 3 has 24.