Models / Code / Devstral Small 2505 API
Devstral Small 2505 API
24B coding model by Mistral AI & All Hands AI built for advanced agentic code tasks, topping SWE-bench scores.

Devstral Small 2505 API Usage
Endpoint
RUN INFERENCE
This model is available as a Together Dedicated Endpoints deployment.
Follow our Docs to configure an endpoint via our API or CLI.
JSON RESPONSE
RUN INFERENCE
This model is available as a Together Dedicated Endpoints deployment.
Follow our Docs to configure an endpoint via our API or CLI.
JSON RESPONSE
RUN INFERENCE
This model is available as a Together Dedicated Endpoints deployment.
Follow our Docs to configure an endpoint via our API or CLI.
JSON RESPONSE
Model Provider:
Mistral
Type:
Code
Variant:
Small
Parameters:
24B
Deployment:
✔️ Dedicated
Quantization
Context length:
128k
Pricing:
Run in playground
Deploy model
Quickstart docs
Quickstart docs
How to use Devstral Small 2505
Model details
Devstral is an agentic LLM for software engineering tasks built under a collaboration between Mistral AI and All Hands AI 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this benchmark.
It is finetuned from Mistral-Small-3.1, therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from Mistral-Small-3.1
the vision encoder was removed.
For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
Learn more about Devstral in this blog post.
Key Features
- 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 license allowing usage and modification for both commercial and non-commercial purposes.
- Context Window: A 128k context window.
- Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.
Benchmark Results
SWE-Bench
Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%.
Model | Scaffold | SWE-Bench Verified (%) |
---|---|---|
Devstral | OpenHands Scaffold | 46.8 |
GPT-4.1-mini | OpenAI Scaffold | 23.6 |
Claude 3.5 Haiku | Anthropic Scaffold | 40.6 |
SWE-smith-LM 32B | SWE-agent Scaffold | 40.2 |
When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B.

Prompting Devstral Small 2505
Applications & Use Cases
Looking for production scale? Deploy on a dedicated endpoint
Deploy Devstral Small 2505 on a dedicated endpoint with custom hardware configuration, as many instances as you need, and auto-scaling.
