MiniCPM-o 2.6 8B

Overview

OpenBMB's omni-modal 8B model adding audio and full-duplex speech streaming on top of vision, scoring 70.2 on OpenCompass and beating GPT-4o on single-image tasks.

When to pick this model

• Voice assistants needing on-prem omni-modal capability
• Real-time speech-to-speech demos and prototypes
• Multimodal chat combining vision, audio, and text in one model
• Replacing GPT-4o omni for privacy-sensitive deployments
• Streaming applications that benefit from full-duplex inference

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

• End-to-end full-duplex speech streaming
• OpenCompass 70.2 across vision-language tasks
• Beats GPT-4o on single-image evaluations
• Unified omni-modal architecture in 8B

Limitations

• Ollama integration is image-only — audio needs native inference
• Speech and audio paths require the official runtime
• Same MiniCPM license registration requirements

Architecture & training

Architecture: Omni 8B · SigLIP + Whisper-medium + ChatTTS + Qwen2.5-7B

Training: End-to-end streaming speech.

Quick start

ollama run openbmb/minicpm-o2.6

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