Mistral releases Devstral Medium and Small 1.1 with 61.6% SWE-Bench Verified score

Mistral AI has released two specialized coding models developed in collaboration with All Hands AI: Devstral Medium and Devstral Small 1.1. The models are designed specifically for agentic coding tasks, with emphasis on generalization across different prompts and agentic scaffolds.

Devstral Medium: API-only proprietary model

Devstral Medium achieves 61.6% on SWE-Bench Verified, according to Mistral AI. The company claims the model surpasses Gemini 2.5 Pro and GPT-4.1 at a quarter of the price, though specific benchmark comparisons were not provided.

Pricing for Devstral Medium (devstral-medium-2507):

• Input: $0.40 per 1M tokens
• Output: $2.00 per 1M tokens

The model is available through Mistral's API and supports on-premise deployment for enterprise customers. Custom fine-tuning is available for enterprises requiring task-specific optimization.

Devstral Small 1.1: Open-source Apache 2.0 release

Devstral Small 1.1 scores 53.6% on SWE-Bench Verified. Mistral claims this sets a new state-of-the-art for open models without test-time scaling, though the model maintains the same 24B parameter architecture as its predecessor.

Pricing for Devstral Small 1.1 (devstral-small-2507):

• Input: $0.10 per 1M tokens
• Output: $0.30 per 1M tokens

Key improvements over the previous version:

• Enhanced performance on SWE-Bench Verified (previous score not disclosed)
• Better generalization to different coding environments
• Support for both Mistral function calling and XML formats
• Optimized for use with OpenHands agentic framework

Technical specifications

Both models support:

• Multiple agentic scaffolds and prompting formats
• Integration with coding environments
• Function calling capabilities

Devstral Small 1.1 is released under the Apache 2.0 license, allowing unrestricted commercial and research use. The model is available for local deployment. Devstral Medium remains proprietary but can be deployed on private infrastructure through enterprise agreements.

Context window size, training data cutoff, and detailed architecture specifications were not disclosed.

What this means

Mistral is positioning itself in the increasingly competitive coding model space with a two-tier strategy: an open-source model for local deployment and experimentation, and a proprietary API model targeting enterprise customers. The 61.6% SWE-Bench Verified score for Devstral Medium, if independently verified, would be competitive with leading coding models, though claims of cost advantage over Gemini 2.5 Pro and GPT-4.1 require context on benchmark parity. The Apache 2.0 release of the 24B parameter Small model provides the open-source community with a capable coding agent foundation without licensing restrictions.