Mistral AI, open weights

# Mixtral 8x7B

> Mixtral 8x7B by Mistral AI, released December 2023. Ranked #334 of 354 with a Noometry Index of 27.1. API: $0.70 in / $0.70 out per M tokens. 32K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/mixtral-8x7b
- Last updated: 2026-10-10
- Title: Mixtral 8x7B Benchmarks, Price & Rank (October 2026)

Mixtral 8x7B by Mistral AI ranks 334th of 354 ranked models on the Noometry Index as of October 2026, with a score of 27.1. Its strongest category is long context, where it ranks 260th. API pricing starts at $0.70 per million input tokens and $0.70 per million output tokens, with a 32K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #334 of 354
- **Index score:** 27.1
- **Evidence:** Confirmed 38 results
- **Provider:** [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral)
- **Released:** December 11, 2023
- **Weights:** Open weights
- **Reasoning:** No
- **Context window:** 32K
- **Max output:** 32K
- **Input price:** $0.70 / M
- **Output price:** $0.70 / M
- **Blended price:** $0.70 / M
- **Output speed:** Not measured
- **Value:** #121 of 219
- **Knowledge cutoff:** January 2024
- **Input:** text

## Category scores

Each category score combines every public result we have in that category.

Mixtral 8x7B category scores

1.  Coding 32.8
2.  Reasoning 18.2
3.  Math 18.8
4.  Knowledge 11.0
5.  Multilingual 29.6
6.  Instruction Following 51.0
7.  Long Context 33.4
8.  Writing & Preference 34.2
9.  0204060

Mixtral 8x7B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 32.8 | #269 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 18.2 | #285 | 2 |
| [Math](https://noometry.com/best/math) | 18.8 | #289 | 3 |
| [Knowledge](https://noometry.com/best/knowledge) | 11.0 | #301 | 4 |
| [Multilingual](https://noometry.com/best/multilingual) | 29.6 | #266 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 51.0 | #297 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 33.4 | #260 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 34.2 | #270 | 4 |

## Strengths and weaknesses

Categories where Mixtral 8x7B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Mixtral 8x7B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 32.8 | −6.0 | #269 of 340, top 80% |
| [Reasoning](https://noometry.com/best/reasoning) | 18.2 | −5.4 | #285 of 350, top 82% |
| [Writing & Preference](https://noometry.com/best/writing) | 34.2 | −19.6 | #270 of 312, top 87% |

### Weakest categories

Mixtral 8x7B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 51.0 | −20.3 | #297 of 305, top 98% |
| [Knowledge](https://noometry.com/best/knowledge) | 11.0 | −26.3 | #301 of 314, top 96% |
| [Multilingual](https://noometry.com/best/multilingual) | 29.6 | −17.8 | #266 of 297, top 90% |

## Closest competitors

The models ranked just above and below Mixtral 8x7B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Mixtral 8x7B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Llama 4 Scout](https://noometry.com/models/llama-4-scout) | #330 | 27.7 | $0.15 | 272 | [Compare](https://noometry.com/compare/llama-4-scout-vs-mixtral-8x7b) |
| [Llama 3.2 90B](https://noometry.com/models/llama-3-2-90b) | #331 | 27.5 | — | — | [Compare](https://noometry.com/compare/llama-3-2-90b-vs-mixtral-8x7b) |
| [Gemini 1.0 Pro](https://noometry.com/models/gemini-1-0-pro) | #332 | 27.3 | — | — | [Compare](https://noometry.com/compare/gemini-1-0-pro-vs-mixtral-8x7b) |
| [Mixtral 8x22B](https://noometry.com/models/mixtral-8x22b) | #333 | 27.1 | $3 | — | [Compare](https://noometry.com/compare/mixtral-8x22b-vs-mixtral-8x7b) |
| [Qwen Turbo](https://noometry.com/models/qwen-turbo) | #335 | 27.1 | $0.0875 | — | [Compare](https://noometry.com/compare/mixtral-8x7b-vs-qwen-turbo) |
| [Qwen3-1.7B](https://noometry.com/models/qwen3-1-7b) | #336 | 26.6 | — | — | [Compare](https://noometry.com/compare/mixtral-8x7b-vs-qwen3-1-7b) |
| [Mistral Nemo](https://noometry.com/models/mistral-nemo) | #337 | 26.4 | $0.15 | — | [Compare](https://noometry.com/compare/mistral-nemo-vs-mixtral-8x7b) |
| [Ministral 3B](https://noometry.com/models/ministral-3b) | #338 | 26.2 | $0.10 | — | [Compare](https://noometry.com/compare/ministral-3b-vs-mixtral-8x7b) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Mixtral 8x7B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1126 | #263 of 294, top 90% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [HumanEval+](https://noometry.com/benchmarks/humaneval-plus) | 39.6% | #37 of 45, top 83% |  | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |
| [MBPP+](https://noometry.com/benchmarks/mbpp-plus) | 49.7% | #33 of 38, top 87% |  | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |

### Reasoning

Mixtral 8x7B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1115 | #261 of 297, top 88% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 49.6% | #138 of 151, top 92% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Adversarial NLI](https://noometry.com/benchmarks/anli) | 55.2% | #5 of 9, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 118.47 | #176 of 213, top 83% |  | [Epoch AI](https://epoch.ai/eci) | 2023-12-11 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 56.3 | #65 of 72, top 91% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [HellaSwag](https://noometry.com/benchmarks/hellaswag) | 86.7% | #7 of 29, top 25% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [PIQA](https://noometry.com/benchmarks/piqa) | 83.6% | #9 of 27, top 34% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WinoGrande](https://noometry.com/benchmarks/winogrande) | 77.2% | #19 of 43, top 45% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Mixtral 8x7B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 10.5% | #56 of 57, top 99% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1147 | #253 of 285, top 89% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 10% | #74 of 79, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 9.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GSM8K](https://noometry.com/benchmarks/gsm8k) | 74.4% | #14 of 38, top 37% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Knowledge

Mixtral 8x7B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 29.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 30.6% | #169 of 186, top 91% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 33.5% | #56 of 58, top 97% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 29.6% | #55 of 57, top 97% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1088 | #253 of 273, top 93% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ARC (AI2) Challenge](https://noometry.com/benchmarks/arc-challenge) | 87.3% | #8 of 39, top 21% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 70.6% | #44 of 81, top 55% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OpenBookQA](https://noometry.com/benchmarks/openbookqa) | 85.8% | #5 of 19, top 27% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [TriviaQA](https://noometry.com/benchmarks/triviaqa) | 82.2% | #9 of 25, top 36% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

Mixtral 8x7B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1077 | #266 of 297, top 90% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1055 | #266 of 285, top 94% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1166 | #200 of 223, top 90% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1114 | #208 of 231, top 91% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 931 | #207 of 211, top 99% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 968 | #204 of 213, top 96% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1090 | #260 of 283, top 92% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1111 | #214 of 226, top 95% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Mixtral 8x7B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 57.5% | #56 of 57, top 99% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1109 | #266 of 298, top 90% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Mixtral 8x7B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1103 | #266 of 291, top 92% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Mixtral 8x7B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1132 | #262 of 297, top 89% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1109 | #261 of 295, top 89% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 67.3% | #54 of 57, top 95% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1115 | #260 of 295, top 89% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Mixtral 8x7B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [mistral](https://docs.mistral.ai/getting-started/models/) | $0.70 | $0.70 | — | 2026-10-10 |

[All Mistral AI API prices →](https://noometry.com/llm-pricing/mistral) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Mixtral 8x7B

-   [Mixtral 8x7B vs Mixtral 8x22B](https://noometry.com/compare/mixtral-8x22b-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Qwen Turbo](https://noometry.com/compare/mixtral-8x7b-vs-qwen-turbo)
-   [Mixtral 8x7B vs Gemini 1.0 Pro](https://noometry.com/compare/gemini-1-0-pro-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Qwen3-1.7B](https://noometry.com/compare/mixtral-8x7b-vs-qwen3-1-7b)
-   [Mixtral 8x7B vs Llama 3.2 90B](https://noometry.com/compare/llama-3-2-90b-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Mistral Nemo](https://noometry.com/compare/mistral-nemo-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Qwen3.8 Max](https://noometry.com/compare/mixtral-8x7b-vs-qwen3-8-max)
-   [Mixtral 8x7B vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-mixtral-8x7b)
-   [Mixtral 8x7B vs Muse Spark 1.3](https://noometry.com/compare/mixtral-8x7b-vs-muse-spark-1-3)

## Other Mistral AI models

-   [Mistral Large 4](https://noometry.com/models/mistral-large-4)43.1
-   [Mistral Medium 3.5](https://noometry.com/models/mistral-medium-3-5)40.2
-   [Mistral Large 3](https://noometry.com/models/mistral-large-3)39.1
-   [Mistral Medium](https://noometry.com/models/mistral-medium)36.3
-   [Magistral Medium](https://noometry.com/models/magistral-medium)35.2
-   [Devstral Small 2505](https://noometry.com/models/devstral-small)34.3
-   [Mistral Small](https://noometry.com/models/mistral-small)33.4
-   [Pixtral Large](https://noometry.com/models/pixtral-large)32.2

## Frequently asked questions

### How good is Mixtral 8x7B?

Mixtral 8x7B by Mistral AI ranks 334th of 354 ranked models on the Noometry Index as of October 2026, with a score of 27.1. Its strongest category is long context, where it ranks 260th. API pricing starts at $0.70 per million input tokens and $0.70 per million output tokens, with a 32K-token context window.

### How much does Mixtral 8x7B cost?

Mixtral 8x7B costs $0.70 per million input tokens and $0.70 per million output tokens on Mistral AI's own API.

### What is Mixtral 8x7B's context window?

Mixtral 8x7B accepts up to 32K tokens of input and can write up to 32K tokens in one response.

### Is Mixtral 8x7B open source?

Yes. Mixtral 8x7B's weights are downloadable; check the license for commercial terms.

### What are Mixtral 8x7B's strengths and weaknesses?

Relative to other ranked models, Mixtral 8x7B places best in coding, reasoning, writing & preference and lowest in instruction following, knowledge, multilingual.

### What is Mixtral 8x7B best at?

Its best category is long context, where it ranks 260th on Noometry.

### Cite this page

Noometry. (2026). Mixtral 8x7B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/mixtral-8x7b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/mixtral-8x7b.md).