MiniCPM-V 2.6 8B

Overview

OpenBMB's 8B vision-language model pairing SigLIP and Qwen2, scoring 65.2 on OpenCompass and beating GPT-4o on OCRBench among sub-25B models.

When to pick this model

• OCR and document extraction at high resolution
• Multi-image and video understanding on a single GPU
• VLM workloads needing 32k context
• Replacing GPT-4V for screenshot and form parsing
• Mobile and consumer-grade inference of multimodal apps

VRAM requirements by quantization

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

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

Strengths

• Beats GPT-4o on OCRBench in the sub-25B class
• OpenCompass 65.2 matches much larger VLMs
• Handles 1.8MP inputs without aggressive downsampling
• Native multi-image and video reasoning
• Free aspect-ratio handling avoids letterboxing artifacts

Limitations

• MiniCPM Model License requires registration for commercial use
• Smaller community than Qwen2-VL or Llama-class VLMs
• Tooling support varies across inference backends

Architecture & training

Architecture: VLM 8B · SigLIP-400M + Qwen2-7B

Training: Multi-image, video, free aspect ratio.

Quick start

ollama run minicpm-v:8b

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