# GPT-4.1 nano vs Mistral Large 3

> Mistral Large 3 is the stronger model overall, scoring 39.1 to 27.9 on the Noometry Index. GPT-4.1 nano costs 2.1× less per token, which makes it the better buy when Mistral Large 3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-4-1-nano-vs-mistral-large-3
- Last updated: 2026-10-12
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. GPT-4.1 nano scores higher in 0 categories and Mistral Large 3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Mistral Large 3 leads 60.0 to 40.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 33.3% for GPT-4.1 nano and 50.9% for Mistral Large 3.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.25 / $0.75 for Mistral Large 3.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 262K.
- Mistral Large 3 has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 27.9 | 39.1 |
| Rank | 327 | 176 |
| Context | 1.05M | 262K |
| Input $/M | $0.10 | $0.25 |
| Output $/M | $0.40 | $0.75 |
| Weights | Proprietary | Open |

## Coding

- GPT-4.1 nano: 24.1 (#330)
- Mistral Large 3: 34.4 (#237)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| LMArena Coding | 1306 | 1448 |
| Aider Polyglot | 8.9% | — |
| LMArena WebDev | — | 1230 |
| SciCode | 25.9% | — |
| WeirdML | 19% | — |

## Agentic & Tool Use

- GPT-4.1 nano: 26.5 (#104)
- Mistral Large 3: —

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 33% | — |

## Reasoning

- GPT-4.1 nano: 8.5 (#349)
- Mistral Large 3: 15.2 (#319)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| Kagi LLM Benchmark | 33.3% | 50.9% |
| LMArena Hard Prompts | 1286 | 1429 |
| ARC-AGI-2 | 0% | — |
| NYT Connections (extended) | — | 7.5% |
| ARC-AGI-1 | 0% | — |
| CritPt | 0% | — |
| Thematic Generalization | — | 23% |
| DTBench | 52.5% | — |
| LMCA | 5.5% | — |
| Epoch Capabilities Index | 129.62 | — |

## Math

- GPT-4.1 nano: 26.9 (#252)
- Mistral Large 3: 38.7 (#129)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| LMArena Math | 1274 | 1414 |
| OTIS Mock AIME 2024-2025 | 28.9% | — |
| Omni-MATH | 36.7% | — |
| MATH Level 5 | 70% | — |
| FrontierMath (Feb 2025 set) | 1% | — |

## Knowledge

- GPT-4.1 nano: 21.8 (#273)
- Mistral Large 3: 36.0 (#177)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| LMArena Expert | 1272 | 1421 |
| GPQA Diamond | 48.9% | — |
| SimpleQA Verified | 6% | — |
| MMLU-Pro | 55% | — |
| Vectara Hallucination Rate | — | 14.5% |
| GPQA (HELM) | 50.7% | — |

## Multimodal

- GPT-4.1 nano: 29.2 (#113)
- Mistral Large 3: 38.2 (#66)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| LMArena Vision | 1063 | 1221 |

## Multilingual

- GPT-4.1 nano: 41.6 (#205)
- Mistral Large 3: 52.5 (#84)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | 1260 | 1413 |
| LMArena Chinese | 1270 | 1447 |
| LMArena German | 1288 | 1437 |
| LMArena Japanese | 1198 | 1394 |
| LMArena Russian | 1261 | 1411 |
| LMArena French | — | 1455 |
| LMArena Korean | — | 1384 |
| LMArena Spanish | — | 1440 |

## Instruction Following

- GPT-4.1 nano: 67.8 (#193)
- Mistral Large 3: 74.0 (#108)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | 1267 | 1403 |
| IFEval | 84.3% | — |

## Long Context

- GPT-4.1 nano: 23.7 (#296)
- Mistral Large 3: 43.1 (#105)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | 1283 | 1413 |
| Fiction.LiveBench | 25% | — |

## Writing & Preference

- GPT-4.1 nano: 40.5 (#243)
- Mistral Large 3: 60.0 (#101)

| Benchmark | GPT-4.1 nano | Mistral Large 3 |
|---|---|---|
| LMArena Text | 1285 | 1428 |
| LMArena Creative Writing | 1260 | 1386 |
| EQ-Bench Creative Writing | 946 | 1412 |
| LMArena Multi-Turn | 1277 | 1429 |
| WildBench | 81.2% | — |

## FAQ

### Is GPT-4.1 nano better than Mistral Large 3?

Mistral Large 3 is the stronger model overall, scoring 39.1 to 27.9 on the Noometry Index. GPT-4.1 nano costs 2.1× less per token, which makes it the better buy when Mistral Large 3's lead doesn't matter for your workload.

### Which is cheaper, GPT-4.1 nano or Mistral Large 3?

GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Mistral Large 3 lists at $0.25 and $0.75.

### Is GPT-4.1 nano or Mistral Large 3 better for coding?

Mistral Large 3 scores higher on coding benchmarks: 34.4 versus 24.1 in the Noometry coding category.

### Which has the bigger context window?

GPT-4.1 nano does, with 1.05M tokens against 262K.

### How many benchmarks do GPT-4.1 nano and Mistral Large 3 share?

17 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Mistral Large 3 has 24.