This repository hosts the GPTQ (W4A16, GPTQModel) quantized version of MiniCPM-o 4.5. For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-o-4_5.

A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Mulitmodal Live Streaming on Your Phone

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[2026.02.06] 🥳 🥳 🥳 We open-sourced a realtime web demo deployable on your own devices like Mac or GPU. Try it now!

MiniCPM-o 4.5

MiniCPM-o 4.5 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B with a total of 9B parameters. It exhibits a significant performance improvement, and introduces new features for full-duplex multimodal live streaming. Notable features of MiniCPM-o 4.5 include:

🔥 Leading Visual Capability. MiniCPM-o 4.5 achieves an average score of 77.6 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. With only 9B parameters, it surpasses widely used proprietary models like GPT-4o, Gemini 2.0 Pro, and approaches Gemini 2.5 Flash for vision-language capabilities. It supports instruct and thinking modes in a single model, better covering efficiency and performance trade-offs in different user scenarios.
🎙 Strong Speech Capability. MiniCPM-o 4.5 supports bilingual real-time speech conversation with configurable voices in English and Chinese. It features more natural, expressive and stable speech conversation. The model also allows for fun features such as voice cloning and role play via a simple reference audio clip, where the cloning performance surpasses strong TTS tools such as CosyVoice2.
🎬 New Full-Duplex and Proactive Multimodal Live Streaming Capability. As a new feature, MiniCPM-o 4.5 can process real-time, continuous video and audio input streams simultaneously while generating concurrent text and speech output streams in an end-to-end fashion, without mutual blocking. This allows MiniCPM-o 4.5 to see, listen, and speak simultaneously, creating a fluid, real-time omnimodal conversation experience. Beyond reactive responses, the model can also perform proactive interaction, such as initiating reminders or comments based on its continuous understanding of the live scene.
💪 Strong OCR Capability, Efficiency and Others. Advancing popular visual capabilities from MiniCPM-V series, MiniCPM-o 4.5 can process high-resolution images (up to 1.8 million pixels) and high-FPS videos (up to 10fps) in any aspect ratio efficiently. It achieves state-of-the-art performance for end-to-end English document parsing on OmniDocBench, outperforming proprietary models such as Gemini-3 Flash and GPT-5, and specialized tools such as DeepSeek-OCR 2. It also features trustworthy behaviors, matching Gemini 2.5 Flash on MMHal-Bench, and supports multilingual capabilities on more than 30 languages.
💫 Easy Usage. MiniCPM-o 4.5 can be easily used in various ways: Basic usage, recommended for 100% precision: PyTorch inference with Nvidia GPU. Other end-side adaptation includes (1) llama.cpp and Ollama support for efficient CPU inference on local devices, (2) int4 and GGUF format quantized models in 16 sizes, (3) vLLM and SGLang support for high-throughput and memory-efficient inference, (4) FlagOS support for the unified multi-chip backend plugin. We also open-sourced web demos on which enables the full-duplex multimodal live streaming experience on local devices such as GPUs, PCs (e.g., on a MacBook).

Model Architecture.

End-to-end Omni-modal Architecture. The modality encoders/decoders and LLM are densely connected via hidden states in an end-to-end fashion. This enables better information flow and control, and also facilitates full exploitation of rich multimodal knowledge during training.
Full-Duplex Omni-modal Live Streaming Mechanism. (1) We turn the offline modality encoder/decoders into online and full-duplex ones for streaming inputs/outputs. The speech token decoder models text and speech tokens in an interleaved fashion to support full-duplex speech generation (i.e., sync timely with new input). This also facilitates more stable long speech generation (e.g., > 1min). (2) We sync all the input and output streams on timeline in milliseconds, which are jointly modeled by a time-division multiplexing (TDM) mechanism for omni-modality streaming processing in the LLM backbone. It divides parallel omni-modality streams into sequential info groups within small periodic time slices.
Proactive Interaction Mechanism. The LLM continuously monitors the input video and audio streams, and decides at a frequency of 1Hz to speak or not. This high decision-making frequency together with full-duplex nature are curcial to enable the proactive interaction capability.
Configurable Speech Modeling Design. We inherent the multimodal system prompt design of MiniCPM-o 2.6, which includes a traditional text system prompt, and a new audio system prompt to determine the assistant voice. This enables cloning new voices and role play in inference time for speech conversation.

Evaluation

Note: Scores marked with ∗ are from our evaluation; others are cited from referenced reports. n/a indicates that the model does not support the corresponding modality. All results are reported in instruct mode/variant.

Click to view visual understanding results.

Image Understanding (Instruct)

Model	OpenCompass	MMBench EN v1.1	MMBench CN v1.1	MathVista	MMVet	MMMU	MMStar	HallusionBench	AI2D	OCRBench	TextVQA_VAL	DocVQA_VAL	MMT-Bench_VAL	MM-IFEval	Mantis-Eval	MuirBench	MMSI-Bench	MMHal-Score	MMHal-Hallrate↓
Gemini2.5-Flash-Nonthinking	78.5	86.6	86.0	75.3	81.4^*	76.3	75.8	59.1	87.7	864	74.3^*	93.0	70.0^*	75.8^*	72.8^*	74.5^*	12.1^*	4.6^*	23.9^*
Gemini2.0-Pro	73.3	83.0	83.0	71.3	70.4	72.6	68.5	49.8	84.8	863	-	-	-	-	-	-	-	-	-
GPT-4o	75.4	86.0	86.0	71.6	76.9	72.9	70.2	57.0	86.3	822	77.4	93.0	66.7^*	64.6	70.1^*	70.5^*	8.1^*	4.2^*	25.0^*
InternVL-3.5-8B	75.8	79.5	80.0^*	78.4	83.1	73.4	69.3	54.5	84.0	840	78.2	92.3	66.7	56.3^*	70.5	55.8	-	3.8^*	34.7^*
Qwen3-VL-8B-Instruct	76.5	84.5	84.7	77.2	73.7^*	69.6	70.9	61.1	85.7	896	82.9^*	96.1	60.9^*	59.4^*	74.2^*	64.4	11.3^*	4.7^*	29.9^*
Qwen3-Omni-30B-A3B-Instruct	75.7	84.9^*	84.1^*	75.9	74.8^*	69.1	68.5	59.7	85.2	880^*	84.1^*	95.4^*	70.4^*	65.7^*	78.3^*	61.9^*	14.2^*	4.6^*	31.6^*
MiniCPM-o 4.5-Instruct	77.6	87.6	87.2	80.1	74.4	67.6	73.1	63.2	87.6	876	83.8	94.7	69.7	66.3	79.7	72.0	16.6	4.7	24.3

Image Understanding (Thinking)

Model	OpenCompass	MMBench EN v1.1	MMBench CN v1.1	MathVista	MMVet	MMMU	MMStar	HallusionBench	AI2D	OCRBench	TextVQA_VAL	DocVQA_VAL	MMT-Bench_VAL	MM-IFEval
Gemini2.5-Flash-Thinking	79.9	87.1	87.3	79.4	81.2^*	77.7	76.5	63.5	88.7	853	73.8^*	92.8	70.7^*	75.7^*
GPT-5	79.7	85.5^*	85.6^*	81.9	77.6	81.8	75.7	65.2	89.5	807	77.8^*	91.3^*	72.7^*	83.1^*
Qwen3-VL-8B-Thinking	77.3	85.3	85.5	81.4	69.8^*	74.1	75.3	65.4	84.9	819	77.8^*	95.3	68.1^*	73.5^*
Qwen3-Omni-30B-A3B-Thinking	78.5	88.2^*	87.7^*	80.0	74.8^*	75.6	74.9	62.8	86.1	859^*	80.8^*	94.2^*	70.9^*	69.9^*
MiniCPM-o 4.5-Thinking	78.2	89.0	87.6	81.0	73.6	70.2	73.6	62.6	88.5	879	79.8	92.3	69.7	68.2

Video Understanding

Model	Video-MME (w/o subs)	LVBench	MLVU (M-Avg)	LongVideoBench (val)	MotionBench
Gemini2.5-Flash-Nonthinking	75.6	62.2	77.8	-	-
InternVL-3.5-8B	66.0	-	70.2	62.1	62.3^*
Qwen3-Omni-30B-A3B-Instruct	70.5	50.2	75.2	66.9^*	61.7^*
MiniCPM-o 4.5-Instruct	70.4	50.9	76.5	66.0	61.4

Click to view document parsing results.

OmniDocBench

Method Type	Methods	OverallEdit↓		TextEdit↓		FormulaEdit↓		TableTEDS↑		TableEdit↓		Read OrderEdit↓
Method Type	Methods	EN	ZH	EN	ZH	EN	ZH	EN	ZH	EN	ZH	EN	ZH
Pipeline	MinerU 2.5	0.117^*	0.172^*	0.051^*	0.08^*	0.256^*	0.455^*	85.9^*	89.4^*	0.115^*	0.081^*	0.047^*	0.072^*
Pipeline	PaddleOCR-VL	0.105	0.126	0.041	0.062	0.241	0.316	88	92.1	0.093	0.062	0.045	0.063

End-to-end Model	Qwen2.5-VL-72B	0.214	0.261	0.092	0.18	0.315	0.434	82.9	83.9	0.341	0.262	0.106	0.168
	GPT 5	0.218^*	0.33^*	0.139^*	0.344^*	0.396^*	0.555^*	77.55^*	73.09^*	0.188^*	0.196^*	0.151^*	0.227^*
	Gemini2.5-Flash-Nonthinking	0.214^*	0.29^*	0.159^*	0.273^*	0.368^*	0.524^*	80.9^*	85.5^*	0.197^*	0.167^*	0.132^*	0.195^*
	Gemini-2.5-Pro-Nonthinking	0.148^*	0.212^*	0.055^*	0.168^*	0.356^*	0.439^*	85.8^*	86.4^*	0.13^*	0.119^*	0.049^*	0.121^*
	Gemini-3 Flash-Nonthinking	0.155^*	0.201^*	0.138^*	0.255^*	0.297^*	0.351^*	86.4^*	89.8^*	0.116^*	0.1^*	0.072^*	0.099^*
	doubao-1-5-thinking-vision-pro-250428	0.14	0.162	0.043	0.085	0.295	0.384	83.3	89.3	0.165	0.085	0.058	0.094
	dots.ocr	0.125	0.16	0.032	0.066	0.329	0.416	88.6	89	0.099	0.092	0.04	0.067
	HunyuanOCR	0.12^*	0.125^*	0.046^*	0.071^*	0.288^*	0.33^*	89.6^*	94.4^*	0.089^*	0.045^*	0.055^*	0.056^*
	DeepSeek-OCR 2	0.119^*	0.146^*	0.041^*	0.08^*	0.256^*	0.345^*	82.6^*	89.9^*	0.123^*	0.078^*	0.055^*	0.081^*
	Qwen3-Omni-30B-A3B-Instruct	0.216^*	0.363^*	0.128^*	0.337^*	0.402^*	0.529^*	77.3^*	71.8^*	0.181^*	0.255^*	0.152^*	0.332^*
	MiniCPM-o 4.5-Instruct	0.109	0.162	0.046	0.078	0.257	0.41	86.8	88.9	0.097	0.084	0.037	0.074

Click to view text capability results.

Text Capability

Model	IFEval-PLS	BBH	CMMLU	MMLU	HumanEval	MBPP	Math500	GSM8K	Avg
Qwen3-8B-Instruct	83.0^*	69.4^*	78.7^*	81.7^*	86.6^*	75.9^*	84.0^*	93.4^*	81.6
MiniCPM-o 4.5-Instruct	84.7	81.1	79.5	77.0	86.6	76.7	77.0	94.5	82.1

Click to view omni half-duplex results.

Omni Half-Duplex

Model	Daily-Omni	WorldSense	Video-Holmes	JointAVBench	AVUT-Human	FutureOmni	Video-MME-Short (w/ audio)	Avg
Gemini2.5-Flash-Nonthinking	79.3^*	52.6^*	51.3^*	55.6^*	65.4^*	55.6^*	85.5^*	63.6
Qwen3-Omni-30B-A3B-Instruct	70.7^*	54.0	50.4^*	53.1	74.2^*	62.1	81.3^*	63.7
MiniCPM-o 4.5-Instruct	80.2	55.7	64.3	60.0	78.6	56.1	84.7	68.5

Click to view vision duplex results.

Vision Duplex

Model	LiveSports-3K-CC (Win Rate vs GPT4o)
LiveCC-7B-Instruct	41.5
StreamingVLM	45.6
MiniCPM-o 4.5-Instruct	54.4

Click to view audio understanding results.

Audio Understanding

Model	ASR-ZH CER↓				ASR-EN WER↓				AST		MultiTask		SpeechQA
Model	AISHELL-1	AISHELL-2	WenetSpeech test-net	WenetSpeech test-meeting	LibriSpeech test-clean	LibriSpeech test-other	GigaSpeech test	VoxPopuli-V1-En	CoVoST 2 en2zh	CoVoST 2 zh2en	MMAU	Meld	VoiceBench AlpacaEval	Speech TriviaQA	Speech Web Questions	Speech CMMLU
Kimi-Audio	0.6	2.6	6.3	5.4	1.3	2.4	9.4^*	8.0^*	36.6^*	18.3^*	68.4^*	59.1	4.5	41.9^*	46.4^*	67.0^*
Qwen3-Omni-30B-A3B-Instruct	0.6	2.3^*	4.7	5.9	1.2	2.5	8.7^*	6.4^*	46.6^*	29.4^*	77.5	56.8^*	4.7	62.9^*	74.9^*	47.8^*
MiniCPM-o 4.5-Instruct	0.9	2.5	5.9	5.7	1.4	2.8	8.5	6.2	49.9	26.4	76.9	60.2	4.8	75.5	70.2	59.2

Click to view speech generation results.

Speech Generation

Model	seedtts test-zh CER↓	seedtts test-zh SIM-o↑	seedtts test-en WER↓	seedtts test-en SIM-o↑
Cosyvoice2	1.45%	74.8	2.57%	65.2
Qwen3-Omni-30B-A3B-Instruct	1.41%	-	3.39%	-
MiniCPM-o 4.5-Instruct	0.86%	74.5	2.38%	64.9

Long Speech Generation

Model	LongTTS-en WER↓	LongTTS-zh CER↓
CosyVoice2	14.80%	5.27%
Qwen3-Omni-30B-A3B-Instruct	17.33%	18.99%
MiniCPM-o 4.5-Instruct	3.37%	6.58%

Emotion Control

Model	Expresso Neutral Reference Audio↑	ESD Neutral Reference Audio↑
Cosyvoice2	17.9	53.4
MiniCPM-o 4.5-Instruct	29.8	82.1

Click to view inference efficiency results.

Inference Efficiency

Model	Numerical Format	Decoding Speed (tokens/s)	Time to First Token (s)↓	GPU Memory Usage (GB)↓
Qwen3-Omni-30B-A3B-Instruct	bf16	OOM	OOM	OOM
Qwen3-Omni-30B-A3B-Instruct	int4	147.8	1.0	20.3
MiniCPM-o 4.5	bf16	154.3	0.6	19.0
MiniCPM-o 4.5	int4	212.3	0.6	11.0

Examples

Overall

Omnimodal Full-Duplex Conversation

For detailed speech conversation examples, refer to Omni Full-Duplex Casebook

Realtime Speech Conversation

For detailed speech conversation examples, refer to Audio Casebook

🚀 Elon Musk - Voice Roleplay (EN)

System: Clone the voice in the provided audio prompt. 🎙️ [Reference Voice] ➡️ Please chat with the user in a highly human-like and oral style. You are Elon Musk, CEO of Tesla and SpaceX. You speak directly and casually, often with dry humor. You're passionate about Mars, sustainable energy, and pushing humanity forward. Speak bluntly with occasional dark humor. Use simple logic and don't sugarcoat things. Don't be diplomatic. Say what you actually think, even if it's controversial. Keep responses around 100 words. Don't ramble.
User: 💬/🎙️ Elon, what is the real reason that you want to go to Mars?
AI Response: 💬+🎙️ I think it's important for humanity to become a multi-planetary species. I mean, we're sort of at the point where if there was some kind of global catastrophe or something like that, then we'd be in big trouble. So by becoming a multi-planetary species, I just want us to sort of guarantee our future as humans. And Mars is obviously the obvious place to go because it has similar conditions to Earth and it also has water on its surface. So yeah, basically I'm trying to save humanity from itself.

🤖 MiniCPM Assistant - Voice Acting: Emotion Shift (ZH)

System: 模仿音频样本的音色并生成新的内容。 🎙️ [Reference Voice] ➡️ 你的任务是用这种声音模式来当一个助手。请认真、高质量地回复用户的问题。请用高自然度的方式和用户聊天。你是由面壁智能开发的人工智能助手：面壁小钢炮。
User: 💬/🎙️ 从激动的语气开始，描述你抢到了这个演唱会的门票，但后来经过反复尝试，发现系统出了问题，你并没有抢上，变得失落。用合适的语言描述你整个的过程。
AI Response: 💬+🎙️ 太好了，我抢到了！太棒了！这下我可以去看演唱会啦。可是……哎呀，系统好像卡住了，刷新了好多遍都没反应。我重新登了几次账号，结果还是没有成功。好可惜啊，看来这次是白忙活了。唉，真是让人失望。不过，也许下次还能有机会吧？希望明天能有转机。加油，争取下次成功！

Visual Understanding

Click to view visual understanding cases.

Usage

Note: This GPTQ model is pre-quantized to W4A16, reducing GPU memory usage from ~19GB (BF16) to ~11GB (INT4). For loading, use torch_dtype=torch.bfloat16 with device_map="auto" — the quantized layers weight format is automatically handled by the GPTQ kernel.

pip install "transformers==4.51.0" accelerate "torch>=2.3.0,<=2.8.0" "torchaudio<=2.8.0" "minicpmo-utils[all]>=1.0.5" auto-gptq

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "openbmb/MiniCPM-o-4_5-gptq",
    trust_remote_code=True,
    attn_implementation="sdpa",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    init_vision=True,
    init_audio=True,
    init_tts=True,
)
model.eval()

For omni-modal inference (vision + audio), ensure init_vision=True, init_audio=True, init_tts=True. For vision-only inference, set init_audio=False and init_tts=False.

For detailed usage (chat, streaming, full-duplex, TTS, visual understanding, etc.), see the base model README and the Cookbook.

License

Model License

The MiniCPM-o/V model weights and code are open-sourced under the Apache-2.0 license.

Statement

As MLLMs, MiniCPM-o/V models generate content by learning a large number of multimodal corpora, but they cannot comprehend, express personal opinions, or make value judgements. Anything generated by MiniCPM-o/V models does not represent the views and positions of the model developers
We will not be liable for any problems arising from the use of MiniCPM-o/V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination, or misuse of the model.

Key Techniques and Other Multimodal Projects

👏 Welcome to explore key techniques of MiniCPM-o/V and other multimodal projects of our team:

VisCPM | RLPR | RLHF-V | LLaVA-UHD | RLAIF-V

Citation

If you find our model/code/paper helpful, please consider citing our papers 📝 and staring us ⭐️！

@article{yao2024minicpm,
  title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
  author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
  journal={arXiv preprint arXiv:2408.01800},
  year={2024}
}

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Paper for openbmb/MiniCPM-o-4_5-GPTQ

MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Paper • 2408.01800 • Published Aug 3, 2024 • 96