MiniCPM-V-4.6

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What MiniCPM-V-4.6 is

A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone GitHub | MiniCPM Wiki(Chinese) | CookBook | Demo | Feishu (Lark) News [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a public free API key together! Try it now. MiniCPM-V 4.6 MiniCPM-V 4.6 is our most edge-deployment-friendly model to date. The model is built based on SigLIP2-400M and the Qwen3.5-0.8B LLM. It inherits the strong single-image, multi-image, and video understanding capabilities of MiniCPM-V family, while significantly improving computation efficiency. It also introduces mixed 4x/16x visual token compression. Notable features of MiniCPM-V 4.6 include: - 🔥 Leading Foundation Capability. MiniCPM-V 4.6 scores 13 on the Artificial Analysis Intelligence Index benchmark, outperforming Qwen3.5-0.8B's score of 10 with 19x fewer token cost, and Qwen3.5-0.8B-Thinking's score of 11 with 43x fewer token cost. It also surpasses the larger Ministral 3 3B (score of 11). - 💪 Strong Multimodal Capability. MiniCPM-V 4.6 outperforms Qwen3.5-0.8B on most vision-language understanding tasks, and reaches Qwen3.5 2B-level capability on many benchmarks including OpenCompass, RefCOCO, HallusionBench, MUIRBench, and OCRBench. - 🚀 Ultra-Efficient Architecture. Based on the latest technique in LLaVA-UHD v4, MiniCPM-V 4.6 reduces the visual encoding computation FLOPs by more than 50%. It enables MiniCPM-V 4.6 to achieve better efficiency to even smaller models, achieving ~1.5x token throughput compared to Qwen3.5-0.8B. It also supports mixed 4x/16x visual token compression rate, allowing flexible switching between accuracy and speed. - 📱 Broad Mobile Platform Coverage. MiniCPM-V 4.6 can be deployed across all three mainstream mobile platforms — iOS, Android, and HarmonyOS. With every edge adaptation code open-sourced, developers can reproduce the on-device experience in just a few steps. - 🛠️ Developer Friendly. MiniCPM-V 4.6 is adapted to inference frameworks such as vLLM, SGLang, llama.cpp, Ollama, and supports fine-tuning ecosystems such as SWIFT and LLaMA-Factory. Developers can quickly customize models for new domains and tasks on consumer-grade GPUs. We provide multi

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys minicpm-v-4-6 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (minicpm-v-4-6 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"minicpm-v-4-6","messages":[{"role":"user","content":"Hello"}]}'

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