[whatmodel.sh](../../README.md) / Mixtral 8x7B Instruct v0.1
# Mixtral 8x7B Instruct v0.1
Mistral · released 2023-12 · 46.7B params (12.9B active) · apache-2.0
**score 42.2** ±0.4 · #530 of 732 · from 32 benchmark results across 21 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Mixtral 8x7B Instruct v0.1 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁●▆█▄▅▅▄▄▂▁ #289 of 313* 0.4% 1
coding ▁▁▁▃●▇█▇▅▄▃▁ #396 of 469 52% 8
chat & writing ▁▁▁▃▆●▆█▆▇▇▃ #313 of 402 13% 6
math ▁▁●▅█▆▄█▄▂▁▁ #386 of 431* 5.7% 2
agent tasks · #289 of 313 · 0.4% of its score
Beats [Bielik 11B v2.3 Instruct](bielik-11b-v2-3-instruct.md), [Mixtral 8x22B Instruct v0.1](mixtral-8x22b-instruct-v0-1.md), [GLM-4 9B Chat](glm-4-9b-chat.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen3 0.6B](qwen3-0-6b.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 42.2% ▁▁▁▁▁●▅█▅▄▁▁ beats 20/114 medium · avg 51% 0.4% blunt · at its level
coding · #396 of 469 · 52% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md), [Falcon3 3B Instruct](falcon3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 45.1% ▁·▁▁▃▄●▅█▃▁▁ beats 29/99 easy · avg 76% 25% sharp · at its level · several runs
HumanEval+ 39.6% ▁·▁▁▁▃▄●▄█▃▁ beats 29/99 easy · avg 70% 12% sharp · at its level
CRUXEval (ZeroEval, 800 problems) 44.9% ▁▂●▃▆█▆▃▂·▁▁ beats 10/47 medium · avg 57% 5.0% sharp · at its level
HumanEvalSynthesize (HumanEvalPac… 48.4% ▁·▂▁▂▆▅█●▄▁▂ beats 27/35 medium · avg 62% 3.4% at its level
MBPP+ 49.7% ▁···▁▁▂●▇█▆▂ beats 19/75 easy · avg 66% 2.4% at its level
HumanEvalExplain (HumanEvalPack, … 37.0% ▄▇█▃▄▇▄▁●▃·▁ beats 28/35 medium · avg 46% 2.1% at its level
HumanEvalFix (HumanEvalPack, 6-la… 39.3% ▁·▁▁▄██▄█▁●▅ beats 28/35 medium · avg 51% 2.0% sharp · at its level
WebApp1K-React pass@1 (onekq-ai) 12.7% ▃●▁█▁▄▅▃▄▇▁▄ beats 2/31 medium · avg 40% 0.5% sharp · noisy
chat & writing · #313 of 402 · 13% of its score
Beats [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 23.7% ▁▂▂▃▆▆●▆▄▄▁▁ beats 61/122 medium · avg 35% 5.3% sharp
Arena-Hard (v0.1, GPT-4-1106 judg… 23.4% ▂▁▁▅●▅▄▃▃▅▃▁ beats 21/60 medium · avg 47% 3.0% sharp
LMArena (text) 1197.2 ▁▁▁▂▃●▃▆▆▇█▃ beats 67/385 avg 1260 2.0% blunt
Polyrating rating (bias-aware re-… 1114.0 ▂▁▂▁▃▇█●▇▄▂▂ beats 56/95 avg 1187 1.9% typical
IFEval (aggregation unspecified) 55.1% ▁▂●▂▂▄▆██▅▂▁ beats 18/145 easy · avg 70% 0.4% at its level · small run
AlpacaEval 1.0 win rate (vs text-… 94.8% ▂▂·▆·▇▃▆█▆●▂ beats 41/45 very easy · avg 94% 0.3% sharp · noisy
math · #386 of 431 · 5.7% of its score
Beats [Yi 34B Chat](yi-34b-chat.md), [Llama 3 8B Instruct](llama-3-8b.md), [Command R (03-2024)](command-r-03-2024.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (ZeroEval, zero-shot) 70.1% ▁▄·●▄█▅▅▅███ beats 4/43 very easy · avg 87% 4.6% sharp · at its level
MATH level 5 9.3% ▂●▅▆▄█▆▃▅▂▃▄ beats 5/92 hard · avg 28% 1.2% sharp · too hard for it
other tests (14)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 63.2% ▁·▁▁●▄▄▅█▄▂▁ beats 5/53 easy · avg 75% 10% at its level
EvoEval (overall) 29.3% ▂▂▃▄●█▁▄▅▃▁▃ beats 14/42 medium · avg 52% 5.2% at its level
MMLU-Pro 41.9% ▃▂▅●▃·█▃▅··▂ beats 8/23 medium · avg 54% 2.9% at its level
WildBench v2 WB-Score (task macro) 31.4703 WB-Score ▁·▁▁·▃█●▇▆▅▆ beats 18/52 avg 43.019 WB-Score 2.2% sharp
CPTUB average (SpeakLeash, 0-5 ju… 54.6% ▁▂▂▂●█▃▆▅▄▂▃ beats 20/95 medium · avg 65% 1.7% at its level
Polish EQ-Bench v2 (SpeakLeash) 58.6% ▁··▁▁▂▂▆▅●█▆ beats 40/70 medium · avg 62% 1.2% at its level
BIG-Bench Hard 45.3% ▁▁▁▁▂▄●▆█▃▃▁ beats 50/155 medium · avg 62% 1.2% sharp · at its level · small run
ZebraLogic (ZeroEval, 1,000 puzzl… 8.7% ▁▁●▆██▁▁·▁▁▁ beats 6/57 hard · avg 15% 1.0% sharp · too hard for it
Open Persian LLM Leaderboard v2 a… 46.1% ▂▄▂●█▄▃▆▃▅▂▂ beats 20/81 medium · avg 57% 1.0% blunt · at its level
GPQA Diamond 30.6% ▁▁▁▁●▄▆▄▅▇█▅ beats 17/211 hard · avg 39% 0.8% sharp · too hard for it
KINA (Knowledge Index of Noah's A… 17.8% ▁▁●▁▃▄█▆▄▅▂▃ beats 2/39 very hard · avg 21% 0.6% typical
Open PT-LLM Leaderboard average (… 73.1% ▃▁▁▁▁▁▁▁▂●▅▁ beats 205/276 medium · avg 72% 0.5% at its level · noisy
EuroEval Life in the UK (English … 87.3% ▁▁▁▁▁▁▁▂▄█●▃ beats 106/146 easy · avg 82% 0.3% at its level · noisy
Vectara hallucination leaderboard… 79.9% ▁●▂▃▄▄█▆▃▂▂▁ beats 5/140 very easy · avg 94% 0.2% blunt · noisy
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 30% ▇▇▇▇
WildEval ZeroEval published results 4 20% ▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 14% ▇▇
Granite Code Models: A Family of Open F… 3 8% ▇
AlpacaEval 1.0 and 2.0 leaderboards 2 6% ▇
Arena-Hard-Auto v0.1 leaderboard 1 3% ▇
MMLU-Pro author-released evaluation runs 1 3% ▇
WildBench v2 leaderboard data 1 2% ▇
LMArena leaderboard dataset 1 2% ▇
Epoch AI 2 2% ▇
Polyrating: A Cost-Effective and Bias-A… 1 2% ▇
SpeakLeash CPTUB 1 2% ▇
HuggingFaceH4 zephyr-orpo-141b-A35b-v0.… 2 2% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
Knowledge Index of Noah's Ark 1 1% ▇
WebApp1K models leaderboard 1 0% ▇
Open Portuguese LLM Leaderboard 1 0% ▇
Berkeley Function Calling Leaderboard r… 1 0% ▇
EuroEval leaderboards 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 42.2
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · Granite Code Models: A Family of Open Foundation Models for Code Intelligence ([CC BY 4.0](../../licenses/paper-2405.04324v1-CC-BY-4.0.txt)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · HuggingFaceH4 zephyr-orpo-141b-A35b-v0.1 model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Knowledge Index of Noah's Ark ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Mixtral 8x7B Instruct v0.1 (Q4_K_M, needs ~26.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/tree/fa1d3835c5d45a3a74c0b68805fcdc133dba2b6a))
```sh
llama cli -hf TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~49.6 GB) `llama cli -hf TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Mixtral 8x7B Instruct v0.1 (Q4_K_M, needs ~26.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/tree/fa1d3835c5d45a3a74c0b68805fcdc133dba2b6a))
```sh
lms get https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~49.6 GB) `lms get https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF@Q8_0 -y` · 4bit (~26.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mixtral-8x7B-Instruct-v0.1-4bit -y` · 8bit (~49.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mixtral-8x7B-Instruct-v0.1-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Mixtral 8x7B Instruct v0.1 (Q4_0, needs ~28.52 GB, from [ollama.com](https://ollama.com/library/mixtral/tags))
```sh
ollama run mixtral:8x7b
```
Other versions: Q4_K_M (~28.52 GB) `ollama run mixtral:8x7b-instruct-v0.1-q4_K_M` · Q8_0 (~56.54 GB) `ollama run mixtral:8x7b-instruct-v0.1-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Mixtral 8x7B Instruct v0.1 (4bit, needs ~26.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Mixtral-8x7B-Instruct-v0.1-4bit/tree/4e8f70450ccdfa3a4b9a4133eaa0d88e43882383))
```sh
mlx_lm.chat --model mlx-community/Mixtral-8x7B-Instruct-v0.1-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Mixtral-8x7B-Instruct-v0.1-4bit
```
Other versions: 8bit (~49.6 GB, Mac only) `mlx_lm.chat --model mlx-community/Mixtral-8x7B-Instruct-v0.1-8bit`
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
[interactive version](https://whatmodel.sh/model/mixtral-8x7b-instruct-v0-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)