[whatmodel.sh](../../README.md) / Mistral 7B Instruct v0.2
# Mistral 7B Instruct v0.2
Mistral · released 2023-12 · 7.24B params · apache-2.0
**score 37.8** ±0.5 · #597 of 732 · from 24 benchmark results across 15 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Mistral 7B Instruct v0.2 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #414 of 469 61% 6
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #351 of 402 11% 4
coding · #414 of 469 · 61% 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 [DeepSeek LLM 7B Chat](deepseek-llm-7b-chat.md), [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [Code Llama 7B Instruct](codellama-7b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 42.1% ▁·▁▁▃●▆▅█▃▁▁ beats 24/99 easy · avg 76% 33% sharp · at its level · several runs
HumanEval+ 36.0% ▁·▁▁▁▃●▄▄█▃▁ beats 25/99 easy · avg 70% 16% sharp · at its level
HumanEvalSynthesize (HumanEvalPac… 30.7% ▁·▂▁▂●▅█▄▄▁▂ beats 7/35 medium · avg 62% 4.4% at its level
MBPP+ 37.0% ▁···▁▁●▅▇█▆▂ beats 6/75 easy · avg 66% 3.3% at its level
HumanEvalExplain (HumanEvalPack, … 24.1% ▄▇█▃▄●▄▁▇▃·▁ beats 19/35 medium · avg 46% 2.4% typical
HumanEvalFix (HumanEvalPack, 6-la… 26.8% ▁·▁▁▄██▄●▁▅▅ beats 21/35 medium · avg 51% 2.1% sharp
chat & writing · #351 of 402 · 11% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 17.1% ▁▂▂▃▆●█▆▄▄▁▁ beats 43/122 medium · avg 35% 5.2% sharp
LMArena (text) 1149.5 ▁▁▁▂●▃▃▆▆▇█▃ beats 38/385 avg 1260 2.8% blunt
Polyrating rating (bias-aware re-… 1054.0 ▂▁▂▁▃●█▆▇▄▂▂ beats 27/95 avg 1187 2.6% typical
AlpacaEval 1.0 win rate (vs text-… 92.8% ▂▂·▆·▇▃▆█●▄▂ beats 36/45 very easy · avg 94% 0.6% sharp · noisy
other tests (13)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 20.1% ▁▁▁▂▂●▂█▆▃▅▃ beats 15/61 medium · avg 38% 6.9% typical
EvoEval (overall) 14.7% ●▂▃▄▄█▁▄▅▃▁▃ beats 1/42 medium · avg 52% 6.2% at its level
MMLU-Pro 28.8% ▃●▅█▃·█▃▅··▂ beats 2/23 medium · avg 54% 3.4% typical
WildBench v2 WB-Score (task macro) 25.6337 WB-Score ▁·▁▁·▃●▄▇▆▅▆ beats 8/52 avg 43.019 WB-Score 3.0% sharp
Natural Questions (closed book) 24.7% ▁·▁·▁▂▁▂·▅▂● beats 19/28 medium · avg 48% 2.7% at its level
Polish EQ-Bench v2 (SpeakLeash) 53.3% ▁··▁▁▂▂▆●▆█▆ beats 27/70 medium · avg 62% 1.6% at its level
Open Persian LLM Leaderboard v2 a… 39.4% ▂▄●▃█▄▃▆▃▅▂▂ beats 13/81 medium · avg 57% 1.3% blunt · at its level
AraGen-v3 3C3H score (Inception, … 0.5% ●▁·▁▂▂▄█▄▅▄▄ beats 0/66 hard · avg 29% 0.6% typical
Open PT-LLM Leaderboard average (… 66.7% ▃▁▁▁▁▁▁▁▂●▅▁ beats 128/276 medium · avg 72% 0.6% at its level · noisy
EuroEval Life in the UK (English … 65.4% ▁▁▁▁▁▁▁●▄█▄▃ beats 35/146 easy · avg 82% 0.5% at its level · noisy
TriviaQA 63.9% ▁··▁█▃▃▁▄▅▄● beats 23/28 easy · avg 73% 0.3% at its level · noisy
HaluEval QA hallucination detecti… 23.0% ●▁▁·▁▁·▁▁▁▁▁ beats 3/27 hard · avg 66% 0.2% noisy
TruthfulQA MC2 66.8% ▅▃█▆▁▂▅···●▁ beats 30/31 medium · avg 57% 0.1% blunt · at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 40% ▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 18% ▇▇
Granite Code Models: A Family of Open F… 3 9% ▇
LiveBench model judgments 1 7% ▇
AlpacaEval 1.0 and 2.0 leaderboards 2 6% ▇
MMLU-Pro author-released evaluation runs 1 3% ▇
Hallucinations Leaderboard results 4 3% ▇
WildBench v2 leaderboard data 1 3% ▇
LMArena leaderboard dataset 1 3% ▇
Polyrating: A Cost-Effective and Bias-A… 1 3% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 2% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
Inception Arabic/Hindi leaderboards 1 1% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
EuroEval leaderboards 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 37.8
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-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)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · Hallucinations Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · 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)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## 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 Mistral 7B Instruct v0.2 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/tree/3a6fbf4a41a1d52e415a4958cde6856d34b2db93))
```sh
llama cli -hf TheBloke/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf TheBloke/Mistral-7B-Instruct-v0.2-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 Mistral 7B Instruct v0.2 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/tree/3a6fbf4a41a1d52e415a4958cde6856d34b2db93))
```sh
lms get https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-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 (~7.7 GB) `lms get https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF@Q8_0 -y` · 4bit (~4.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.2-4bit -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 Mistral 7B Instruct v0.2 (Q4_K_M, needs ~4.84 GB, from [ollama.com](https://ollama.com/library/mistral/tags))
```sh
ollama run mistral:7b-instruct-v0.2-q4_K_M
```
Other versions: Q8_0 (~9.19 GB) `ollama run mistral:7b-instruct-v0.2-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 Mistral 7B Instruct v0.2 (4bit, needs ~4.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.2-4bit/tree/64481e3a4a7bb0990c45c991958f8d6c00c1e785))
```sh
mlx_lm.chat --model mlx-community/Mistral-7B-Instruct-v0.2-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Mistral-7B-Instruct-v0.2-4bit
```
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
[interactive version](https://whatmodel.sh/model/mistral-7b-instruct-v0-2) · [JSON](https://whatmodel.sh/api?format=json&all=1)