What is Devstral 2?
Devstral 2 is Mistral AI’s next-generation open-source coding model family (123B and 24B variants) optimized for agentic software engineering tasks, released December 9, 2025.
When was Devstral 2 released?
It was officially released on December 9, 2025, alongside Mistral Vibe CLI for terminal-native coding automation.
What is the performance of Devstral 2?
Devstral 2 achieves 72.2% on SWE-bench Verified, setting SOTA among open-weight models for real-world software engineering benchmarks.
Is Devstral 2 free to use?
Currently free via Mistral API during initial period; post-free API pricing is $0.40/$2 per million tokens (input/output) for the 123B version.
What is the context window for Devstral 2?
It supports a 256,000 token context window, enabling full codebase analysis in a single pass.
How does Devstral 2 compare to other models?
It outperforms many open peers on SWE-bench and is more cost-efficient than closed models like Claude Sonnet for real tasks.
Can Devstral Small 2 run locally?
Yes, the 24B Devstral Small 2 is designed for consumer hardware deployment with the same long context and strong performance.
What is Mistral Vibe?
Mistral Vibe is a native CLI agent powered by Devstral 2 for end-to-end code automation, custom workflows, and terminal integration.

Devstral 2


About This AI
Devstral 2 is Mistral AI’s next-generation coding model family released on December 9, 2025, available in Devstral 2 (123B parameters) and Devstral Small 2 (24B parameters).
It is a dense transformer optimized for agentic software engineering, supporting codebase exploration, multi-file edits, architecture-level context, and tool orchestration for autonomous code agents.
With a 256K token context window, it enables processing large repositories in one pass while maintaining coherence.
The model achieves 72.2% on SWE-bench Verified (SOTA among open-weight models), outperforming many peers in real-world GitHub issue resolution and software reasoning benchmarks.
Released under modified MIT license (Devstral 2) and Apache 2.0 (Small variant), it is fully open-source with weights on Hugging Face for local deployment.
Currently free via Mistral API during initial period; post-free pricing is $0.40/$2.00 per million tokens (input/output) for the 123B version and lower for Small.
Paired with Mistral Vibe CLI (terminal-native agent), it enables end-to-end code automation, custom subagents, slash-command skills, and workflow configuration.
Ideal for developers, AI agents, software teams, and researchers building autonomous coding systems, with strong cost-efficiency (up to 7x better than some closed models) and local runnability on consumer hardware for the Small version.
Key Features
- 256K token context window: Handles entire large codebases and long conversations without truncation
- Agentic coding excellence: Explores repositories, edits multiple files, maintains architecture context, and orchestrates changes
- SOTA open-weight performance: 72.2% on SWE-bench Verified for real-world software engineering tasks
- Multi-size variants: 123B dense for maximum capability; 24B Small for local/consumer hardware deployment
- Tool use and reasoning: Built for autonomous agents with reliable tool calling and multi-step planning
- Open-source availability: Full weights under modified MIT/Apache 2.0, easy Hugging Face integration
- Mistral Vibe CLI integration: Native terminal agent with custom subagents, skills, and workflows
- High cost-efficiency: Up to 7x more efficient than competitors like Claude Sonnet in real tasks
- Strong benchmarks: Competitive or leading in coding, reasoning, and agentic evaluations vs open peers
Price Plans
- Free (Current API period $0): Free access via Mistral API during launch period; also free on Experiment plan in Mistral Studio
- API Paid (Post-free $0.40/$2 per M tokens input/output for 123B): Standard token pricing after promo; lower for Small 2 ($0.10/$0.30)
- Le Chat Pro/Team ($14.99–$24.99/Month): Access to Devstral 2 via Vibe CLI, higher limits, and integrated coding workflows
Pros
- Top open coding model: Sets new SOTA for open-weight agentic coding with high SWE-bench score
- Long context support: 256K tokens enable full-repo understanding without splitting
- Free initial access: Currently free via API; Small variant runs locally on consumer GPUs
- Strong efficiency: Delivers high performance at lower cost than many closed models
- Developer-focused: Pairs perfectly with Mistral Vibe for terminal-native automation
- Open weights: Full customization, fine-tuning, and local deployment possible
- Rapid innovation: Builds on Mistral's fast release cadence for coding AI
Cons
- High compute for full model: 123B requires significant GPUs (e.g., 4+ H100 equivalents)
- Free period temporary: API moves to paid ($0.40/$2 input/output per M tokens) after initial offer
- Verbose outputs: Can produce longer responses unless prompted for conciseness
- Setup for local use: Small variant easier, but full needs enterprise hardware
- No native multimodal: Primarily text/code-focused; limited vision compared to some rivals
- Recent release: Adoption metrics still emerging; no massive user base numbers yet
Use Cases
- Autonomous code agents: Build agents that explore repos, fix bugs, refactor code across files
- Software engineering tasks: Resolve GitHub issues, implement features, review PRs
- Local development: Run Small variant on laptop for offline coding assistance
- CLI automation: Use Mistral Vibe for terminal-based end-to-end workflows
- Research in agentic AI: Fine-tune or extend for custom software agents
- Team productivity: Integrate into IDEs or CI/CD for faster iteration
- Cost-effective coding: High performance at fraction of closed-model costs
Target Audience
- Software developers: Needing agentic help for coding, debugging, and refactoring
- AI agent builders: Creating autonomous software engineering systems
- Open-source enthusiasts: Running and customizing top open coding models locally
- Dev teams: Boosting productivity with integrated CLI agents and API
- Researchers: Benchmarking or advancing agentic coding frontiers
- Startups/enterprises: Seeking efficient, open alternatives to proprietary LLMs
How To Use
- Access via API: Sign up at console.mistral.ai, get API key, and call Devstral 2 endpoint
- Try in Mistral Studio: Use free Experiment plan or Le Chat Pro for immediate testing
- Run locally (Small): Download weights from Hugging Face, set up with transformers/vLLM
- Use Mistral Vibe CLI: Install via pip/npm, configure with API key or local model
- Prompt for coding: Ask agentic tasks like 'fix this bug in repo X' or 'refactor module Y'
- Integrate in tools: Use in IDEs via extensions or CLI for seamless workflow
- Monitor usage: Track tokens in console; switch to Small for cost/latency savings
How we rated Devstral 2
- Performance: 4.8/5
- Accuracy: 4.7/5
- Features: 4.8/5
- Cost-Efficiency: 4.9/5
- Ease of Use: 4.5/5
- Customization: 4.9/5
- Data Privacy: 4.8/5
- Support: 4.4/5
- Integration: 4.7/5
- Overall Score: 4.7/5
Devstral 2 integration with other tools
- Mistral API: Direct integration for web/apps via console.mistral.ai endpoints
- Mistral Vibe CLI: Native terminal agent for local or API-powered end-to-end coding automation
- Hugging Face: Model weights and inference pipelines for local deployment and fine-tuning
- IDE Extensions: Compatible with VS Code, Cursor, Zed via CLI or API wrappers
- Agent Frameworks: Works with LangChain, LlamaIndex, AutoGen for custom agentic systems
Best prompts optimised for Devstral 2
- Explore this GitHub repository [repo URL], understand the architecture, and suggest improvements to the main module while maintaining compatibility
- Fix the failing test in file tests/unit.py by editing the function in src/core.py; reason step-by-step and apply changes
- Refactor this legacy codebase to use modern async patterns in Python; update multiple files and add type hints
- Implement a new feature: add user authentication with JWT to this Express.js app; include routes, middleware, and tests
- Analyze this pull request diff and provide a detailed code review with suggestions for security and performance
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