**MiniCPM-V 2.8B** is a strong multimodal large language model for efficient end-side deployment. The model is built based on SigLip-400M and MiniCPM-2.4B, connected by a perceiver resampler. Our latest version, **MiniCPM-V 2.0** has several notable features. MiniCPM-V 2.0 achieves **state-of-the-art performance** on multiple benchmarks (including OCRBench, TextVQA, MME, MMB, MathVista, etc) among models under 7B parameters. It even **outperforms strong Qwen-VL-Chat 9.6B, CogVLM-Chat 17.4B, and Yi-VL 34B on OpenCompass, a comprehensive evaluation over 11 popular benchmarks**. Notably, MiniCPM-V 2.0 shows **strong OCR capability**, achieving **comparable performance to Gemini Pro in scene-text understanding**, and **state-of-the-art performance on OCRBench** among open-source models. LMMs are known for suffering from hallucination, often generating text not factually grounded in images. MiniCPM-V 2.0 is **the first end-side LMM aligned via multimodal RLHF for trustworthy behavior** (using the recent RLHF-V [CVPR'24] series technique). This allows the model to **match GPT-4V in preventing hallucinations** on Object HalBench.
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**MiniCPM-V 4.0** is the latest efficient model in the MiniCPM-V series. The model is built based on SigLIP2-400M and MiniCPM4-3B with a total of 4.1B parameters. It inherits the strong single-image, multi-image and video understanding performance of MiniCPM-V 2.6 with largely improved efficiency. Notable features of MiniCPM-V 4.0 include: - 🔥 **Leading Visual Capability.** With only 4.1B parameters, MiniCPM-V 4.0 achieves an average score of 69.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks, **outperforming GPT-4.1-mini-20250414, MiniCPM-V 2.6 (8.1B params, OpenCompass 65.2) and Qwen2.5-VL-3B-Instruct (3.8B params, OpenCompass 64.5)**. It also shows good performance in multi-image understanding and video understanding. - 🚀 **Superior Efficiency.** Designed for on-device deployment, MiniCPM-V 4.0 runs smoothly on end devices. For example, it devlivers **less than 2s first token delay and more than 17 token/s decoding on iPhone 16 Pro Max**, without heating problems. It also shows superior throughput under concurrent requests.
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A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone **MiniCPM-V 4.5** is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include: - 🔥 **State-of-the-art Vision-Language Capability.** MiniCPM-V 4.5 achieves an average score of 77.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B** for vision-language capabilities, making it the most performant MLLM under 30B parameters.
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**MiniCPM-V 4.5** is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include: We deploy MiniCPM-V 4.5 on iPad M4 with iOS demo. The demo video is the raw screen recording without edition.
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A GPT-4o Level MLLM for Single Image, Multi Image and Video Understanding on Your Phone **MiniCPM-V 4.5** is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include: - 🔥 **State-of-the-art Vision-Language Capability.** MiniCPM-V 4.5 achieves an average score of 77.2 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B** for vision-language capabilities, making it the most performant MLLM under 30B parameters.
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**MiniCPM-V 4.5** is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include: Both Video-MME and OpenCompass were evaluated using 8×A100 GPUs for inference. The reported inference time of Video-MME includes full model-side computation, and excludes the external cost of video frame extraction (dependent on specific frame extraction tools) for fair comparison. We deploy MiniCPM-V 4.5 on iPad M4 with iOS demo. The demo video is the raw screen recording without editing.
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A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [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** 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:
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> **This repository hosts the AWQ (W4A16, AutoAWQ) quantized version of MiniCPM-V 4.6.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6. A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now.
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> **This repository hosts the bitsandbytes (NF4, 4-bit) quantized version of MiniCPM-V 4.6.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6. A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now.
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> **This repository hosts the GGUF (llama.cpp) quantized version of MiniCPM-V 4.6.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6. A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now.
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> **This repository hosts the GPTQ (W4A16, GPTQModel) quantized version of MiniCPM-V 4.6.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6. A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now.
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A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [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 Thinking** is the long chain-of-thought reasoning variant of MiniCPM-V 4.6. It generates an explicit reasoning trace before producing the final answer, substantially boosting performance on complex multimodal reasoning, math, and OCR-heavy tasks, while keeping the same edge-friendly architecture (SigLIP2-400M vision encoder + Qwen3.5-0.8B LLM) and the mixed 4x/16x visual token compression of MiniCPM-V 4.6.
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> **This repository hosts the AWQ (W4A16, AutoAWQ) quantized version of MiniCPM-V 4.6 Thinking.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6-Thinking. A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now.
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> **This repository hosts the bitsandbytes (NF4, 4-bit) quantized version of MiniCPM-V 4.6 Thinking.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6-Thinking. A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now.
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> **This repository hosts the GGUF (llama.cpp) quantized version of MiniCPM-V 4.6 Thinking.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6-Thinking. **MiniCPM-V 4.6 Thinking** is the long chain-of-thought reasoning variant of MiniCPM-V 4.6. It generates an explicit reasoning trace before producing the final answer, substantially boosting performance on complex multimodal reasoning, math, and OCR-heavy tasks, while keeping the same edge-friendly architecture (SigLIP2-400M vision encoder + Qwen3.5-0.8B LLM) and the mixed 4x/16x visual token compression of MiniCPM-V 4.6. MiniCPM-V 4.6 can be deployed across three mainstream end-side platforms — **iOS, Android and HarmonyOS**. The clips below are raw screen recordings on phone devices without edition.
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> **This repository hosts the GPTQ (W4A16, GPTQModel) quantized version of MiniCPM-V 4.6 Thinking.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6-Thinking. A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now.
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**MiniCPM-V 4.7** is the latest model in the MiniCPM-V series, available in a **1B** variant. It builds on the family's single-image, multi-image, and video understanding capabilities, combining stronger foundation capabilities with efficient visual encoding and on-device inference. Notable features of MiniCPM-V 4.7 include: - 🔥 **A New Pareto Frontier in Performance and Efficiency.** As model capabilities continue to advance, inference efficiency has become an important measure of a model's value. On resource-constrained devices, delivering the strongest possible capabilities within a limited resource budget calls for higher capability density. The **1B** variant of MiniCPM-V 4.7 advances the performance–efficiency Pareto frontier for MLLMs. In Instruct mode, the 1B variant continues to improve foundation capabilities over MiniCPM-V 4.6 and **substantially outperforms Qwen3.5 0.8B in both overall score and inference efficiency**. - 🚀 **Efficient Architecture.** Based on the intra-ViT early compression technique from LLaVA-UHD v4, MiniCPM-V 4.7 compresses visual tokens early within the vision encoder, reducing computation in subsequent encoder layers. It also supports **mixed 4x/16x visual token compression**, allowing developers to balance visual detail and inference cost across tasks. - 💻 **Broad Platform and Chip Support.** MiniCPM-V 4.7 supports **iOS, Android, and HarmonyOS**, together with open-source on-device deployment support. It also provides **day-0 support for chip platforms from Rockchip, Houmo, Intel, AMD, and Arm**, extending deployment options across mobile and local computing devices. - 🛠️ **Developer Friendly.** MiniCPM-V 4.7 supports inference frameworks including SGLang, vLLM, llama.cpp, and Ollama, along with fine-tuning frameworks including SWIFT and LLaMA-Factory. It provides GGUF, BNB, AWQ, and GPTQ quantized variants and adds **FP8 quantization support**. We also release **Self-Replay Fine-Tuning (SRFT)**, a fine-tuning approach that helps developers extend model capabilities at low cost while reducing forgetting of existing capabilities, making further customization easier. Model weights · Inference · Fine-tuning · Live demo · Local deployment · API · Cookbook
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> **This repository hosts the AWQ (W4A16, AutoAWQ) quantized version of MiniCPM-V 4.7 1B.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.7-1B. **MiniCPM-V 4.7** is the latest model in the MiniCPM-V series, available in a **1B** variant. It builds on the family's single-image, multi-image, and video understanding capabilities, combining stronger foundation capabilities with efficient visual encoding and on-device inference. Notable features of MiniCPM-V 4.7 include: - 🔥 **A New Pareto Frontier in Performance and Efficiency.** As model capabilities continue to advance, inference efficiency has become an important measure of a model's value. On resource-constrained devices, delivering the strongest possible capabilities within a limited resource budget calls for higher capability density. The **1B** variant of MiniCPM-V 4.7 advances the performance–efficiency Pareto frontier for MLLMs. In Instruct mode, the 1B variant continues to improve foundation capabilities over MiniCPM-V 4.6 and **substantially outperforms Qwen3.5 0.8B in both overall score and inference efficiency**. - 🚀 **Efficient Architecture.** Based on the intra-ViT early compression technique from LLaVA-UHD v4, MiniCPM-V 4.7 compresses visual tokens early within the vision encoder, reducing computation in subsequent encoder layers. It also supports **mixed 4x/16x visual token compression**, allowing developers to balance visual detail and inference cost across tasks. - 💻 **Broad Platform and Chip Support.** MiniCPM-V 4.7 supports **iOS, Android, and HarmonyOS**, together with open-source on-device deployment support. It also provides **day-0 support for chip platforms from Rockchip, Houmo, Intel, AMD, and Arm**, extending deployment options across mobile and local computing devices. - 🛠️ **Developer Friendly.** MiniCPM-V 4.7 supports inference frameworks including SGLang, vLLM, llama.cpp, and Ollama, along with fine-tuning frameworks including SWIFT and LLaMA-Factory. It provides GGUF, BNB, AWQ, and GPTQ quantized variants and adds **FP8 quantization support**. We also release **Self-Replay Fine-Tuning (SRFT)**, a fine-tuning approach that helps developers extend model capabilities at low cost while reducing forgetting of existing capabilities, making further customization easier.
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`MiniCPM-V-4.7-1B-DSpark` is a DSpark draft checkpoint trained for exact pairing with MiniCPM-V-4.7-1B and its tokenizer. The llama.cpp version please see: MiniCPM-V-4.7-1B-DSpark-gguf Training responses were generated by MiniCPM-V-4.7-1B from a mixture of Vision & Text prompts.
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> **This repository hosts the GGUF (llama.cpp) quantized version of MiniCPM-V-4.7-1B-DSpark.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.7-1B-DSpark. `MiniCPM-V-4.7-1B-DSpark` is a DSpark draft checkpoint trained for exact pairing with MiniCPM-V-4.7-1B and its tokenizer. Acceptance length is defined as the total number of completion tokens divided by the total number of speculative verification steps. Evaluation uses natural EOS termination and `max_new_tokens=8192`.
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