[whatmodel.sh](../../README.md) / Codestral 22B v0.1 # Codestral 22B v0.1 Mistral AI · released 2024-05 · 22.25B params · mnpl **score 51.0** ±0.8 · #347 of 732 · from 7 benchmark results across 4 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Codestral 22B v0.1 among every model on that task or test, worst to best. 
task    vs the others         rank  counts  tests
coding  ▁▁▁▃▅▇●▇▅▄▃▁   #268 of 469     90%      4
coding · #268 of 469 · 90% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md), [Phi-3 Medium 128K Instruct](phi-3-medium-128k-instruct.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
HumanEval                            79.9%  ▁·▁▁▃▄▆▅●▃▁▁ beats 84/99  easy · avg 76%       36%  sharp
HumanEval+                           73.8%  ▁·▁▁▁▃▄▄▄●▃▁ beats 84/99  easy · avg 70%       33%  sharp · at its level
Aider code editing (133 Exercism …   51.1%  ▁·▁▂▄▁●▄█▆▂▂ beats 17/62  medium · avg 51%     13%  sharp · at its level
MBPP+                                61.9%  ▁···▁▁▂▅▇●▆▂ beats 41/75  easy · avg 66%      7.2%  at its level
other tests (3) 
test                                result  vs the others in its run    difficulty        counts  why
EvalPerf correctness (118 tasks, …   72.8%  ▂·▂▂▂▆●▆▆▆▃█ beats 8/29     easy · avg 74%      3.9%  typical
DSEffi-Bench Pass (1,000 data-sci…   27.4%  ▅··●▅·█·▃██▃ beats 2/15     hard · avg 23%      3.8%  typical
Open PT-LLM Leaderboard average (…   71.0%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 179/276  medium · avg 72%    1.6%  at its level · noisy
## 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
EvalPlus / EvalPerf published evaluatio…        4   81% ▇▇▇▇▇▇▇▇▇▇
Aider code editing benchmark runs               1   13% ▇▇
DSEffi-Bench: Demystifying Large Langua…        1    4% ▇
Open Portuguese LLM Leaderboard                 1    2% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 51.0
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · DSEffi-Bench: Demystifying Large Language Models' Capability in Efficient Data Science Code Generation ([CC BY 4.0](../../licenses/paper-2608.30248v1-CC-BY-4.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) ## 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 Codestral 22B v0.1 (Q4_K_M, needs ~13.3 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Codestral-22B-v0.1-GGUF/tree/0e6abe14d6aeaf2c99d5dc9973205e8e38692d90)) ```sh llama cli -hf bartowski/Codestral-22B-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 bartowski/Codestral-22B-v0.1-GGUF:Q4_K_M ``` Other versions: Q8_0 (~23.6 GB) `llama cli -hf bartowski/Codestral-22B-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 Codestral 22B v0.1 (Q4_K_M, needs ~13.3 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Codestral-22B-v0.1-GGUF/tree/0e6abe14d6aeaf2c99d5dc9973205e8e38692d90)) ```sh lms get https://huggingface.co/bartowski/Codestral-22B-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 (~23.6 GB) `lms get https://huggingface.co/bartowski/Codestral-22B-v0.1-GGUF@Q8_0 -y` · 4bit (~12.5 GB, Mac only) `lms get https://huggingface.co/rairahulr1/Codestral-22B-v0.1-mlx-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 Codestral 22B v0.1 (Q4_K_M, needs ~13.85 GB, from [ollama.com](https://ollama.com/library/codestral/tags)) ```sh ollama run codestral:22b-v0.1-q4_K_M ``` Other versions: Q8_0 (~27.2 GB) `ollama run codestral:22b-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 Codestral 22B v0.1 (4bit, needs ~12.5 GB, Mac only, from [huggingface.co/rairahulr1](https://huggingface.co/rairahulr1/Codestral-22B-v0.1-mlx-4Bit/tree/35138401347919124c08c86fa8cb93732cfba2f7)) ```sh mlx_lm.chat --model rairahulr1/Codestral-22B-v0.1-mlx-4Bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model rairahulr1/Codestral-22B-v0.1-mlx-4Bit ``` Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Codestral-22B-v0.1) [interactive version](https://whatmodel.sh/model/codestral-22b-v0-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)