[whatmodel.sh](../../README.md) / Devstral Small 2 24B
# Devstral Small 2 24B
Mistral · released 2025-12 · 24B params · apache-2.0
**score 61.0** ±1.9 · #194 of 732 · from 6 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 Devstral Small 2 24B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄●▅▄▄▂▁ #137 of 313 81% 3
coding ▁▁▁▃▅▇█●▅▄▃▁ #142 of 469 65% 2
agent tasks · #137 of 313 · 81% of its score
Beats [o3](o3.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 59.1% ▁▁▁▁▁▁▂▄●▆▁▁ beats 73/138 hard · avg 23% 51% sharp · at its level · small run · seve…
Terminal-Bench 2.0 (maker) 22.5% ▂▂●▅████▆▂▃▃ beats 3/36 very hard · avg 11% 16% at its level · small run
SWE-bench Multilingual (maker) 55.7% ▁··▁▃▁●██▂▂▁ beats 17/65 hard · avg 30% 14% at its level · small run
coding · #142 of 469 · 65% of its score
Beats [Qwen3.5 Plus](qwen3-5-plus.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 59.1% ▁▁▁▁▁▁▂▄●▆▁▁ beats 73/138 hard · avg 23% 51% sharp · at its level · small run · seve…
SWE-bench Multilingual (maker) 55.7% ▁··▁▃▁●██▂▂▁ beats 17/65 hard · avg 30% 14% at its level · small run
other tests (2)
test result vs the others in its run difficulty counts why
Next Steps data preparation (bala… 74.2% ▄·▄···●·▄▄·▄ beats 3/6 medium · avg 52% 11% at its level
SCHEDBench subset, solver-mediate… 7.0% ●·▄·····▄··█ beats 0/4 very hard · avg 5.4% 6.0% noisy
source results share of the score
mistralai/Devstral-Small-2-24B-Instruct… 3 53% ▇▇▇▇▇▇
SERA: Soft-Verified Efficient Repositor… 1 28% ▇▇▇
Agentic coding without the cloud: evalu… 1 11% ▇
Improving Natural-Language Combinatoria… 1 6% ▇
prior: pull toward the middle 2% ▇
weighted average = 61.0
Licences: mistralai/Devstral-Small-2-24B-Instruct-2512 model card ([Apache-2.0](../../licenses/cards-gap-NOTICE.txt)) · SERA: Soft-Verified Efficient Repository Agents ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Agentic coding without the cloud: evaluating open-weight LLMs on longitudinal data preparation tasks ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Constrained Language Models via Formal Abstractions ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## 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 Devstral Small 2 24B (Q4_K_M, needs ~14.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Devstral-Small-2-24B-Instruct-2512-GGUF/tree/e471f62bf546b027d9f23f679bcd1a295eabf403))
```sh
llama cli -hf lmstudio-community/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M
```
Other versions: Q8_0 (~25.1 GB) `llama cli -hf lmstudio-community/Devstral-Small-2-24B-Instruct-2512-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 Devstral Small 2 24B (Q4_K_M, needs ~14.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Devstral-Small-2-24B-Instruct-2512-GGUF/tree/e471f62bf546b027d9f23f679bcd1a295eabf403))
```sh
lms get https://huggingface.co/lmstudio-community/Devstral-Small-2-24B-Instruct-2512-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 (~25.1 GB) `lms get https://huggingface.co/lmstudio-community/Devstral-Small-2-24B-Instruct-2512-GGUF@Q8_0 -y` · 4bit (~15.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/Devstral-Small-2-24B-Instruct-2512-4bit -y` · 8bit (~26.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-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 Devstral Small 2 24B (Q4_K_M, needs ~14.9 GB, from [ollama.com](https://ollama.com/library/devstral-small-2/tags))
```sh
ollama run devstral-small-2:24b
```
Other versions: Q8_0 (~29.3 GB) `ollama run devstral-small-2:24b-instruct-2512-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 Devstral Small 2 24B (4bit, needs ~15.1 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Devstral-Small-2-24B-Instruct-2512-4bit/tree/97228558fcbdd1adefd39d515155db3a894b37f7))
```sh
mlx_lm.chat --model mlx-community/Devstral-Small-2-24B-Instruct-2512-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Devstral-Small-2-24B-Instruct-2512-4bit
```
Other versions: 8bit (~26.6 GB, Mac only) `mlx_lm.chat --model mlx-community/mistralai_Devstral-Small-2-24B-Instruct-2512-MLX-8Bit`
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512)
[interactive version](https://whatmodel.sh/model/devstral-small-2) · [JSON](https://whatmodel.sh/api?format=json&all=1)