Mistral Medium 3.5 128B

Overview

Mistral AI's first merged flagship — a dense 128B with vision, 256k context, and configurable reasoning. Hits 77.6% on SWE-Bench Verified, consolidating Medium 3.1, Magistral, and Devstral 2 into one model.

When to pick this model

• Agentic coding workflows demanding state-of-the-art SWE-Bench performance
• Customer-support automation needing top τ³-Telecom scores
• Long-document analysis up to 256k tokens
• Multilingual vision tasks across 24 languages
• Production deployments wanting reasoning toggleable per request

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	74 GB
Q5_K_M	91 GB
Q8_0	137 GB
FP16 (no quantization)	256 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Benchmark	Score
SWE-Bench Verified	77.6
τ³-Telecom	91.4

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• SWE-Bench Verified 77.6% — best-in-class for open weights
• τ³-Telecom 91.4% for tool-using agents
• 256k context with strong long-context retention
• Vision-enabled and multilingual across 24 languages
• Modified MIT — permissive for most commercial use

Limitations

• ~74GB at Q4 — needs a 4-GPU box for comfortable serving
• Revenue clause kicks in for large enterprises
• Single-model consolidation means no separate specialized variants

Architecture & training

Architecture: Dense 128B · vision encoder · 256k ctx · configurable reasoning · integrated EAGLE draft head

Training: First merged Mistral flagship: replaces Medium 3.1, Magistral and Devstral 2 in Le Chat / Vibe.

Quick start

ollama run mistral-medium-3.5:128b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.