9B multimodal model with vision, speech, and full-duplex streaming for text, image, video, audio
905
MiniCPM-o 4.5 is a state-of-the-art multimodal language model that delivers Gemini 2.5 Flash-level performance for vision, speech, and full-duplex multimodal live streaming capabilities. Built on an end-to-end architecture combining SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B, the model totals 9B parameters while surpassing widely used proprietary models like GPT-4o and Gemini 2.0 Pro in vision-language tasks.
The model excels across multiple modalities with leading visual understanding capabilities, achieving an average OpenCompass score of 77.6 across 8 popular benchmarks. It supports both instruct and thinking modes in a single model, offering flexibility for different performance and efficiency trade-offs. MiniCPM-o 4.5 features bilingual real-time speech conversation in English and Chinese with configurable voices, voice cloning, and role-play capabilities. A standout feature is its full-duplex multimodal live streaming, allowing the model to simultaneously process continuous video and audio input streams while generating concurrent text and speech outputs without mutual blocking.
The model demonstrates strong OCR capabilities with state-of-the-art performance for end-to-end English document parsing on OmniDocBench, surpassing specialized tools and proprietary models. It efficiently processes high-resolution images up to 1.8 million pixels and high-FPS videos up to 10fps in any aspect ratio, while supporting multilingual capabilities across more than 30 languages.

| Attribute | Value |
|---|---|
| Provider | OpenBMB |
| Architecture | Qwen3 (SigLip2 + Whisper-medium + CosyVoice2 + Qwen3-8B) |
| Parameters | 9B |
| Context Length | 40,960 tokens |
| Languages | 30+ languages (with bilingual speech support for English and Chinese) |
| Input modalities | Text, Image, Video, Audio |
| Output modalities | Text, Speech |
| License | Apache 2.0 |
docker model run minicpm-v-4_5
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| Model | OpenCompass | MMBench EN | MMBench CN | MathVista | MMVet | MMMU | MMStar | HallusionBench |
|---|---|---|---|---|---|---|---|---|
| MiniCPM-o 4.5-Instruct | 77.6 | 87.6 | 87.2 | 80.1 | 74.4 | 67.6 | 73.1 | 63.2 |
| Gemini 2.5 Flash | 78.5 | 86.6 | 86.0 | 75.3 | 81.4 | 76.3 | 75.8 | 59.1 |
| InternVL-3.5-8B | 75.8 | 79.5 | 80.0 | 78.4 | 83.1 | 73.4 | 69.3 | 54.5 |
| Qwen3-VL-8B-Instruct | 76.5 | 84.5 | 84.7 | 77.2 | 73.7 | 69.6 | 70.9 | 61.1 |
| Model | Video-MME (w/o subs) | LVBench | MLVU (M-Avg) | LongVideoBench | MotionBench |
|---|---|---|---|---|---|
| MiniCPM-o 4.5-Instruct | 70.4 | 50.9 | 76.5 | 66.0 | 61.4 |
| Gemini 2.5 Flash | 75.6 | 62.2 | 77.8 | - | - |
| Qwen3-Omni-30B-A3B | 70.5 | 50.2 | 75.2 | 66.9 | 61.7 |
| InternVL-3.5-8B | 66.0 | - | 70.2 | 62.1 | 62.3 |
| Model | Overall Edit (EN) | Text Edit (EN) | Formula Edit (EN) | Table TEDS (EN) | Read Order Edit (EN) |
|---|---|---|---|---|---|
| MiniCPM-o 4.5-Instruct | 0.109 | 0.046 | 0.257 | 86.8 | 0.037 |
| PaddleOCR-VL | 0.105 | 0.041 | 0.241 | 88.0 | 0.045 |
| Gemini 3 Flash | 0.155 | 0.138 | 0.297 | 86.4 | 0.072 |
| DeepSeek-OCR 2 | 0.119 | 0.041 | 0.256 | 82.6 | 0.055 |
Note: Lower Edit scores are better; higher TEDS scores are better.
| Model | Daily-Omni | WorldSense | Video-Holmes | JointAVBench | AVUT-Human | FutureOmni | Avg |
|---|---|---|---|---|---|---|---|
| MiniCPM-o 4.5-Instruct | 80.2 | 55.7 | 64.3 | 60.0 | 78.6 | 56.1 | 68.5 |
| Qwen3-Omni-30B-A3B | 70.7 | 54.0 | 50.4 | 53.1 | 74.2 | 62.1 | 63.7 |
| Gemini 2.5 Flash | 79.3 | 52.6 | 51.3 | 55.6 | 65.4 | 55.6 | 63.6 |
| Model | IFEval-PLS | BBH | CMMLU | MMLU | HumanEval | MBPP | Math500 | GSM8K | Avg |
|---|---|---|---|---|---|---|---|---|---|
| MiniCPM-o 4.5-Instruct | 84.7 | 81.1 | 79.5 | 77.0 | 86.6 | 76.7 | 77.0 | 94.5 | 82.1 |
| Qwen3-8B-Instruct | 83.0 | 69.4 | 78.7 | 81.7 | 86.6 | 75.9 | 84.0 | 93.4 | 81.6 |
| Model | LiveSports-3K-CC (Win Rate vs GPT-4o) |
|---|---|
| MiniCPM-o 4.5-Instruct | 54.4 |
| StreamingVLM | 45.6 |
| LiveCC-7B-Instruct | 41.5 |
This model card was automatically generated using cagent-action. Want to learn more about Docker Model Runner? Check out the project repository: https://github.com/docker/model-runner.
Content type
Model
Digest
sha256:3df6a19bd…
Size
5.7 GB
Last updated
5 months ago
docker model pull aistaging/minicpm-v-4_5:9B-Q4_K_M