Mistral Medium 3.5 is a flagship model designed for instruction-following, reasoning, and coding tasks. It operates as a dense 128B model with a 256k context window, enabling efficient performance in real-world applications.
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Links and model details
Generate marketing copy, blog posts, product descriptions, and creative content
Example
Write email campaigns, social media posts, landing page copy with consistent brand voice
Produce high-quality content 10x faster, maintain consistent tone across channels
Condense long documents, extract key insights, generate executive summaries
Example
Summarize 50-page reports into 2-page executive briefs, extract action items from meeting transcripts
Save hours on document review, never miss important details in lengthy content
Answer questions based on context, retrieve information from knowledge bases
Example
Build FAQ systems, customer support bots, internal knowledge assistants
Mistral Medium 3.5 is our first flagship merged model, available in public preview. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning, and coding in a single set of weights. It performs strongly in real-world use, with self-hosting possible on as few as four GPUs. Reasoning effort is now configurable per request, so the same model can answer a quick chat reply or work through a complex agentic run. We trained the vision encoder from scratch to handle variable image sizes and aspect ratios. Mistral Medium 3.5 scores 77.6% on SWE-Bench Verified, ahead of Devstral 2 and models like Qwen3.5 397B A17B. It also has strong agentic capabilities and scores 91.4 on τ³-Telecom.
Mistral Medium 3.5 is in the explainx.ai LLM directory. Mistral Medium 3.5 is a flagship model designed for instruction-following, reasoning, and coding tasks. It operates as a dense 128B model with a 256k context window, enabling efficient performance in real-world applications.. It is labeled open-weights / public artifacts, with publisher field Mistral AI and license Modified MIT. Structured FAQs below clarify source, weights, and benchmark data. Canonical URL: /llms/mistral-medium-3-5.
Listing on explainx.ai. Information may change; verify with the publisher.
Reduce support ticket volume by 40-60%, provide instant answers 24/7
Rewrite content, change tone/style, translate, simplify complex text
Example
Convert technical documentation to user-friendly guides, adapt content for different audiences
Repurpose content efficiently, reach broader audiences without rewriting from scratch
Prerequisites
Time Estimate
30-60 minutes for basic integration, 1-2 days for production-ready implementation
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Transformer-based neural networks trained on massive text corpora, using self-attention mechanisms to understand and generate human-like text.
✓ Use when
Use for content generation, summarization, Q&A, text transformation, creative writing, and any task involving understanding and generating natural language. Best for non-critical applications where occasional errors are acceptable.
✗ Avoid when
Avoid for: mission-critical decisions without human oversight, medical/legal advice without expert review, real-time information (news, stock prices), exact calculations (use code instead), or when perfect factual accuracy is required.
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