[whatmodel.sh](../../README.md) / Mistral 7B Instruct v0.3 # Mistral 7B Instruct v0.3 Mistral · released 2024-05 · 7.25B params · apache-2.0 **score 41.0** ±1.0 · #547 of 732 · from 15 benchmark results across 10 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 v0.3 among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
coding          ●··········█       #2 of 2*    7.8%      2
chat & writing  ▁▁▁▃▆●▆█▆▇▇▃   #318 of 402*     28%      2
math            ▁▁●▅█▆▄█▄▂▁▁    #410 of 431    7.8%      3
coding · #2 of 2 · 7.8% of its score 
test               result  vs the others in its run   difficulty      counts  why
HumanEval (maker)   38.4%  ▁▁▁▁▂▂●▆█▅▂▁ beats 63/205  easy · avg 80%    4.2%  sharp · at its level · noisy · small run
MBPP (maker)        50.2%  ▁▁▁▄▅●▆▅▄▁▂▁ beats 37/86   easy · avg 74%    3.6%  at its level · small run
chat & writing · #318 of 402 · 28% 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       20.6%  ▁▂▂▃▆●█▆▄▄▁▁ beats 54/122  medium · avg 35%     25%  sharp
Arena-Hard (version/judge unspeci…   44.3%  ▃▃▂●▃▆▅█▅▅▂▃ beats 6/30    easy · avg 70%      3.2%  at its level · small run
math · #410 of 431 · 7.8% of its score Beats [Command R (03-2024)](command-r-03-2024.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [OPT-125M](opt-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md) here, though they rank lower overall. 
test                               result  vs the others in its run  difficulty            counts  why
MATH level 5                         3.6%  ●▂▅▆▄█▆▃▅▂▃▄ beats 1/92   hard · avg 28%          4.8%  sharp · too hard for it
MATH (subset unspecified) (maker)   13.2%  ●▄▄▃█▇▅▆▄▃▄▁ beats 2/80   medium · avg 63%        2.7%  at its level · noisy
OTIS mock AIME 2024–2025             0.3%  ●▃▄▃▃▃▇▇█▃▂▅ beats 0/197  very hard · avg 7.2%    0.3%  sharp · too hard for it · noisy
other tests (9) 
test                                result  vs the others in its run    difficulty            counts  why
LiveBench 2024-07-26                 21.3%  ▁▁▁▂▂●▂█▆▃▅▃ beats 16/61    medium · avg 38%         30%  typical
CPTUB average (SpeakLeash, 0-5 ju…   55.3%  ▁▂▂▂●█▃▆▅▄▂▃ beats 22/95    medium · avg 65%        8.5%  at its level
Polish EQ-Bench v2 (SpeakLeash)      45.2%  ▁··▁▁▂▂●▅▆█▆ beats 15/70    medium · avg 62%        6.3%  at its level
Open Persian LLM Leaderboard v2 a…   41.0%  ▂▄●▃█▄▃▆▃▅▂▂ beats 14/81    medium · avg 57%        4.9%  blunt · at its level
AraGen-v3 3C3H score (Inception, …    5.1%  ▁▁·▁●▂▄█▄▅▄▄ beats 3/66     hard · avg 29%          3.0%  typical
Open PT-LLM Leaderboard average (…   68.1%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 144/276  medium · avg 72%        2.4%  at its level · noisy
Vectara hallucination leaderboard…   90.5%  ▁▁▂▃●▄█▆▃▂▂▁ beats 26/140   very easy · avg 94%     1.0%  blunt · noisy
Chess Puzzles (Epoch)                 0.0%  ●▁▁▁▁▂▃▃▃▂▁▁ beats 6/137    very hard · avg 5.8%    0.1%  sharp · too hard for it · noisy
GPQA Diamond                         15.2%  ▁▁▁▁▂▄▅▅███▄ beats 0/322    hard · avg 39%             —  failed run: Below the 25% guess rate, w…
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
LiveBench model judgments                       1   30% ▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards             1   25% ▇▇▇
Mistral AI vendor-reported benchmark sc…        4   14% ▇▇
SpeakLeash CPTUB                                1    8% ▇
SpeakLeash Polish EQ-Bench leaderboard          1    6% ▇
Epoch AI                                        3    5% ▇
Open Persian LLM Leaderboard v2                 1    5% ▇
Inception Arabic/Hindi leaderboards             1    3% ▇
Open Portuguese LLM Leaderboard                 1    2% ▇
Vectara hallucination leaderboard               1    1% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 41.0
Licences: 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)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
Not counted (1) 
source    test          score  why
Epoch AI  GPQA Diamond  15.2%  failed run: Below the 25% guess rate, while Epoch's Llama-3.1-8B-Instruct gets 0.27 and Llama-3-8B 0.26. Probably unparsed answers. Epoch's open-mistral-7b also scores 0.132.
## 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 v0.3 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-7B-Instruct-v0.3-GGUF/tree/29a785419661afc70b5cd91b5023a835b0092281)) ```sh llama cli -hf lmstudio-community/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf lmstudio-community/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M ``` Other versions: Q8_0 (~7.7 GB) `llama cli -hf lmstudio-community/Mistral-7B-Instruct-v0.3-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 v0.3 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-7B-Instruct-v0.3-GGUF/tree/29a785419661afc70b5cd91b5023a835b0092281)) ```sh lms get https://huggingface.co/lmstudio-community/Mistral-7B-Instruct-v0.3-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/lmstudio-community/Mistral-7B-Instruct-v0.3-GGUF@Q8_0 -y` · 4bit (~4.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.3-4bit -y` · 8bit (~7.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.3-8bit -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 Mistral 7B Instruct v0.3 (Q4_K_M, needs ~4.85 GB, from [ollama.com](https://ollama.com/library/mistral/tags)) ```sh ollama run mistral:7b-instruct-v0.3-q4_K_M ``` Other versions: Q8_0 (~9.2 GB) `ollama run mistral:7b-instruct-v0.3-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 v0.3 (4bit, needs ~4.1 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.3-4bit/tree/a4b8f870474b0eb527f466a03fbc187830d271f5)) ```sh mlx_lm.chat --model mlx-community/Mistral-7B-Instruct-v0.3-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Mistral-7B-Instruct-v0.3-4bit ``` Other versions: 8bit (~7.7 GB, Mac only) `mlx_lm.chat --model mlx-community/Mistral-7B-Instruct-v0.3-8bit` Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) [interactive version](https://whatmodel.sh/model/mistral-7b-instruct-v0-3) · [JSON](https://whatmodel.sh/api?format=json&all=1)