Mistral Medium 3.5

Developer & Data Science ToolsModel Training & DeploymentAI AgentsLLM models

Power up your code agents with a 128-billion-parameter model featuring a 256,000-token context window, which excels at coding, reasoning, and following instructions. This model requires only 4 GPUs to run and achieves a score of 77.6% on SWE-Bench Verified

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June 2026

Developer & Data Science Tools, Model Training & Deployment

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Pros and cons

Pros

Expansive 256,000-token context window for large-scale codebase analysis
Resource-efficient architecture requiring only 4 GPUs for deployment
Strong benchmark performance with a 77.6% score on SWE-Bench Verified
Specialized instruction-following and reasoning for autonomous agent workflows

Cons

128B parameter size still requires significant dedicated enterprise-grade hardware
Performance in non-technical or creative domains requires independent verification
Users must confirm specific licensing terms for commercial or proprietary use
Potential for increased latency when processing the full 256,000-token context

FAQ

How does the large context window benefit software development?

The 256,000-token context window allows Mistral Medium 3.5 to ingest entire project directories or extensive documentation in a single prompt. This capability reduces the need for complex RAG architectures for some tasks, though buyers should monitor for 'lost in the middle' issues and evaluate latency when the window is fully utilized.

What are the infrastructure requirements for running this model?

Mistral Medium 3.5 is optimized to run on a 4-GPU configuration, which is efficient for a 128-billion-parameter model. While this lower hardware footprint is a competitive advantage, it still necessitates high-end compute resources. Organizations should evaluate their existing cloud or on-premise infrastructure costs before planning a full-scale deployment.