March 2026 Collection of third-party generative AI models validated by Red Hat AI for use across the Red Hat AI Product Portfolio. • 5 items • Updated • 4
Devstral Small 2 24B Instruct 2512
👁 Model Icon
👁 Validated BadgeDevstral 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.
ModelCar Storage URI: oci://registry.redhat.io/rhai/modelcar-devstral-small-2-24b-instruct-2512:3.0
Validated on vLLM: 0.14.1
Validated on RHAIIS: 3.4 EA1
Validated on RHOAI: 3.4 EA1
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.
Usage
Scaffolding
Together with Devstral 2, we are releasing Mistral Vibe, a CLI tool allowing developers to leverage Devstral capabilities directly in your terminal.
- Mistral Vibe (recommended): Learn how to use it here
Devstral 2 can also be used with the following scaffoldings:
You can use Devstral 2 either through our API or by running locally.
Mistral Vibe
The Mistral Vibe CLI is a command-line tool designed to help developers leverage Devstral’s capabilities directly from their terminal.
We recommend installing Mistral Vibe using uv for faster and more reliable dependency management:
uv tool install mistral-vibe
You can also run:
curl -LsSf https://mistral.ai/vibe/install.sh | sh
If you prefer using pip, use:
pip install mistral-vibe
To launch the CLI, navigate to your project's root directory and simply execute:
vibe
If this is your first time running Vibe, it will:
- Create a default configuration file at
~/.vibe/config.toml. - Prompt you to enter your API key if it's not already configured, follow these instructions to create an Account and get an API key.
- Save your API key to
~/.vibe/.envfor future use.
Local Deployment
The model can also be deployed with the following libraries:
vllm (recommended): See heresglang: See heretransformers: See here
We're thankful to the llama.cpp team and their community as well as the LM Studio and Ollama teams that worked hard to make these models also available for their frameworks.
You can now also run Devstral using these (alphabetical ordered) frameworks:
llama.cpp: To use community ones such as Unsloth's or Bartowski's make sure to use changes from this PR.LM Studio: https://lmstudio.ai/models/devstral-2Ollama: https://ollama.com/library/devstral-small-2
If you notice subpar performance with local serving, please submit issues to the relevant framework so that it can be fixed and in the meantime we advise to use the Mistral AI API.
vLLM (recommended)
SGLang
Transformers
Tests
To help test our model via vLLM or test that other frameworks' implementations are correct, here is a set of prompts you can try with the expected outputs.
- Call one tool
- Call tools one at a time subsequently
- Long context
- Chatting tech
- Small talk
Run the examples above with the following python script which assumes there is an OpenAI compatible server deployed at localhost:8000:
License
This model is licensed under the Apache 2.0 License.
You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.
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Base model
mistralai/Mistral-Small-3.1-24B-Base-2503