SimonPu/ Devstral-Small:latest

345 2 days ago

Devstral is an agentic LLM for software engineering tasks. Devstral 2 models excel at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench.

vision tools 
ollama run SimonPu/Devstral-Small

Details

2 days ago

a0ac0503971e · 15GB ·

model
mistral3
·
24B
·
Q4_K_M
template
{{- $lastUserIndex := -1 }} {{- $hasSystemPrompt := false }} {{- range $index, $_ := .Messages }} {{
params
{ "min_p": 0.01, "temperature": 0.15 }

Readme

Updated:

. 2026/02/09 update new model support for Devstral-Small:2512-Q4_K_M

Devstral Small 2 24B Instruct 2512

Devstral is an agentic LLM for software engineering tasks. Devstral Small 2 excels at using tools to explore codebases, editing multiple files and power software engineering agents.
The model achieves remarkable performance on SWE-bench.

This model is an Instruct model in FP8, fine-tuned to follow instructions, making it ideal for chat, agentic and instruction based tasks for SWE use cases.

For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we invite companies to reach out to us.

Key Features

The Devstral Small 2 Instruct model offers the following capabilities: - Agentic Coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - Lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 256k context window.

Updates compared to Devstral Small 1.1: - Vision Capabilities: Enables the model to analyze images and provide insights based on visual content, in addition to text. - Improved Performance: Devstral Small 2 is a step-up compared to its predecessors. - Attention Softmax Temperature: Devstral Small 2 uses the same architecture as Ministral 3 using rope-scaling as introduced by Llama 4 and Scalable-Softmax Is Superior for Attention. - Better Generalization: Generalises better to diverse prompts and coding environments.

Use Cases

AI Code Assistants, Agentic Coding, and Software Engineering Tasks. Leveraging advanced AI capabilities for complex tool integration and deep codebase understanding in coding environments.

Benchmark Results

Model/Benchmark Size (B Parameters) SWE Bench Verified SWE Bench Multilingual Terminal Bench 2
Devstral 2 123 72.2% 61.3% 32.6%
Devstral Small 2 24 68.0% 55.7% 22.5%
GLM 4.6 355 68.0% – 24.6%
Qwen 3 Coder Plus 480 69.6% 54.7% 25.4%
MiniMax M2 230 69.4% 56.5% 30.0%
Kimi K2 Thinking 1000 71.3% 61.1% 35.7%
DeepSeek v3.2 671 73.1% 70.2% 46.4%
GPT 5.1 Codex High – 73.7% – 52.8%
GPT 5.1 Codex Max – 77.9% – 60.4%
Gemini 3 Pro – 76.2% – 54.2%
Claude Sonnet 4.5 – 77.2% 68.0% 42.8%

*Benchmark results presented are based on publicly reported values for competitor models. SWE Benchmark