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Mistral Medium 3.5
개발자 및 데이터 과학 도구Model 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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개발자 및 데이터 과학 도구Model Training & Deployment
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June 2026Developer & Data Science Tools, Model Training & Deployment
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mistral.aiPros 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.
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