Together AI | Devstral Small 2505 API

To run this model, you first need to deploy it on a Dedicated Endpoint.

Devstral Small 2505 API Usage

Endpoint

mistralai/Devstral-Small-2505

RUN INFERENCE

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "your-dedicated-endpoint-url",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
  }'

RUN INFERENCE

from together import Together

client = Together()

response = client.chat.completions.create(
    model="your-dedicated-endpoint-url",
    messages=[
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
)
print(response.choices[0].message.content)

RUN INFERENCE

import Together from "together-ai";

const together = new Together();

const response = await together.chat.completions.create({
  messages: [
    {
      role: "user",
      content: "What are some fun things to do in New York?"
    }
  ],
  model: "your-dedicated-endpoint-url"
});

console.log(response.choices[0].message.content)

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.

Looking for production scale? Deploy on a dedicated endpoint

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Devstral Small 2505 API

Devstral Small 2505 API Usage

How to use Devstral Small 2505

Model details

Key Features

Benchmark Results

SWE-Bench

Prompting Devstral Small 2505

Applications & Use Cases

Looking for production scale? Deploy on a dedicated endpoint

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