Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology

Devstral Medium

Model basics

Reasoning traces

Not supported

Context length

128K tokens

Max output length

4096 tokens

Model type

编程大模型

Release date

2025-07-10

Model file size

No data

MoE architecture

Total params / Active params

0.0B / N/A

Knowledge cutoff

No data

Inference modes

常规模式（Non-Thinking Mode）

Devstral Medium

Open source & experience

Code license

不开源

Weights license

不开源- 不开源

GitHub repo

GitHub link unavailable

Hugging Face

Hugging Face link unavailable

Live demo

https://chat.mistral.ai/chat

Devstral Medium

Official resources

Paper

Upgrading agentic coding capabilities with the new Devstral models

DataLearnerAI blog

MistralAI发布全新编程大模型：24B开源Devstral Small 1.1在SWE-Bench Verified评分超过旧版DeepSeek R1，编程大模型新的替代

Devstral Medium

API details

API speed

3/5

💡Default unit: $/1M tokens. If vendors use other units, follow their published pricing.

Standard pricingStandard

Modality	Input	Output
Text	$0.4	$2

Devstral Medium

Benchmark Results

No benchmark data to show.

Devstral Medium

Publisher

MistralAI

View publisher details

Devstral Medium

Model Overview

MistralAI发布的一个专门针对编程优化的编程大模型。本模型不开源，仅在官方API提供使用，它的价格只有Gemini 2.5 Pro 和GPT 4.1的1/4，但是在SWE-Bench Verfiied这种编程评测上不相上下，这意味着针对编程场景，使用Devstral Model是很划算的。

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