# GPT-4.1 nano vs Mixtral 8x22B

> GPT-4.1 nano and Mixtral 8x22B score almost the same on the Noometry Index (27.9 vs 27.1), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/gpt-4-1-nano-vs-mixtral-8x22b
- Last updated: 2026-10-10
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. GPT-4.1 nano scores higher in 6 categories and Mixtral 8x22B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Mixtral 8x22B leads 19.9 to 8.5.
- The biggest single-benchmark swing is MATH Level 5: 70% for GPT-4.1 nano and 24.2% for Mixtral 8x22B.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 64K.
- Mixtral 8x22B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 27.9 | 27.1 |
| Rank | 327 | 333 |
| Context | 1.05M | 64K |
| Input $/M | $0.10 | $2 |
| Output $/M | $0.40 | $6 |
| Weights | Proprietary | Open |

## Coding

- GPT-4.1 nano: 24.1 (#330)
- Mixtral 8x22B: 24.2 (#329)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| WeirdML | 19% | 3.2% |
| LMArena Coding | 1306 | 1166 |
| Aider Polyglot | 8.9% | — |
| SciCode | 25.9% | — |
| BigCodeBench Instruct | — | 40.6% |
| BigCodeBench Complete | — | 50.2% |
| HumanEval+ | — | 72% |
| MBPP+ | — | 64.3% |

## Agentic & Tool Use

- GPT-4.1 nano: 26.5 (#104)
- Mixtral 8x22B: 23.1 (#127)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 33% | — |
| Cybench | — | 7.5% |

## Reasoning

- GPT-4.1 nano: 8.5 (#349)
- Mixtral 8x22B: 19.9 (#248)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| LMArena Hard Prompts | 1286 | 1150 |
| DTBench | 52.5% | 55.1% |
| Epoch Capabilities Index | 129.62 | 122.03 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 33.3% | — |
| ARC-AGI-1 | 0% | — |
| CritPt | 0% | — |
| LMCA | 5.5% | — |
| ForecastBench | — | 56.3 |

## Math

- GPT-4.1 nano: 26.9 (#252)
- Mixtral 8x22B: 22.9 (#275)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| Omni-MATH | 36.7% | 16.3% |
| LMArena Math | 1274 | 1184 |
| MATH Level 5 | 70% | 24.2% |
| OTIS Mock AIME 2024-2025 | 28.9% | — |
| FrontierMath (Feb 2025 set) | 1% | — |

## Knowledge

- GPT-4.1 nano: 21.8 (#273)
- Mixtral 8x22B: 15.1 (#293)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| GPQA Diamond | 48.9% | 34.1% |
| MMLU-Pro | 55% | 46% |
| GPQA (HELM) | 50.7% | 33.4% |
| LMArena Expert | 1272 | 1113 |
| SimpleQA Verified | 6% | — |
| MMLU | — | 77.8% |

## Multimodal

- GPT-4.1 nano: 29.2 (#113)
- Mixtral 8x22B: —

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| LMArena Vision | 1063 | — |

## Multilingual

- GPT-4.1 nano: 41.6 (#205)
- Mixtral 8x22B: 32.8 (#255)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| LMArena Non-English | 1260 | 1128 |
| LMArena Chinese | 1270 | 1116 |
| LMArena German | 1288 | 1141 |
| LMArena Japanese | 1198 | 1037 |
| LMArena Russian | 1261 | 1158 |
| LMArena French | — | 1166 |
| LMArena Korean | — | 1057 |
| LMArena Spanish | — | 1151 |

## Instruction Following

- GPT-4.1 nano: 67.8 (#193)
- Mixtral 8x22B: 57.7 (#266)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| IFEval | 84.3% | 72.4% |
| LMArena Instruction Following | 1267 | 1147 |

## Long Context

- GPT-4.1 nano: 23.7 (#296)
- Mixtral 8x22B: 34.7 (#247)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| LMArena Longer Query | 1283 | 1144 |
| Fiction.LiveBench | 25% | — |

## Writing & Preference

- GPT-4.1 nano: 40.5 (#243)
- Mixtral 8x22B: 36.9 (#262)

| Benchmark | GPT-4.1 nano | Mixtral 8x22B |
|---|---|---|
| LMArena Text | 1285 | 1162 |
| LMArena Creative Writing | 1260 | 1141 |
| WildBench | 81.2% | 71.1% |
| LMArena Multi-Turn | 1277 | 1130 |
| EQ-Bench Creative Writing | 946 | — |

## FAQ

### Is GPT-4.1 nano better than Mixtral 8x22B?

GPT-4.1 nano and Mixtral 8x22B score almost the same on the Noometry Index (27.9 vs 27.1), so choose on price, context window or the category you care about most.

### Which is cheaper, GPT-4.1 nano or Mixtral 8x22B?

GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Mixtral 8x22B lists at $2 and $6.

### Is GPT-4.1 nano or Mixtral 8x22B better for coding?

They score almost the same on coding (24.1 vs 24.2); test both on your own repository before choosing.

### Which has the bigger context window?

GPT-4.1 nano does, with 1.05M tokens against 64K.

### How many benchmarks do GPT-4.1 nano and Mixtral 8x22B share?

24 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Mixtral 8x22B has 34.