Minicpm V4 En - Minicpm O

MiniCPM-V 4.0

Archieve at: 2025-08-25

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.
💫 Easy Usage. MiniCPM-V 4.0 can be easily used in various ways including llama.cpp, Ollama, vLLM, SGLang, LLaMA-Factory and local web demo etc. We also open-source iOS App that can run on iPhone and iPad. Get started easily with our well-structured Cookbook, featuring detailed instructions and practical examples.

Evaluation

<details> <summary>Click to view single image results on OpenCompass. </summary> <div align="center"> <table style="margin: 0px auto;"> <thead> <tr> <th nowrap="nowrap" align="left">model</th> <th>Size</th> <th>Opencompass</th> <th>OCRBench</th> <th>MathVista</th> <th>HallusionBench</th> <th>MMMU</th> <th>MMVet</th> <th>MMBench V1.1</th> <th>MMStar</th> <th>AI2D</th> </tr> </thead> <tbody align="center"> <tr> <td colspan="11" align="left"><strong>Proprietary</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4v-20240409</td> <td>-</td> <td>63.5</td> <td>656</td> <td>55.2</td> <td>43.9</td> <td>61.7</td> <td>67.5</td> <td>79.8</td> <td>56.0</td> <td>78.6</td> </tr> <tr> <td nowrap="nowrap" align="left">Gemini-1.5-Pro</td> <td>-</td> <td>64.5</td> <td>754</td> <td>58.3</td> <td>45.6</td> <td>60.6</td> <td>64.0</td> <td>73.9</td> <td>59.1</td> <td>79.1</td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4.1-mini-20250414</td> <td>-</td> <td>68.9</td> <td>840</td> <td>70.9</td> <td>49.3</td> <td>55.0</td> <td>74.3</td> <td>80.9</td> <td>60.9</td> <td>76.0</td> </tr> <tr> <td nowrap="nowrap" align="left">Claude 3.5 Sonnet-20241022</td> <td>-</td> <td>70.6</td> <td>798</td> <td>65.3</td> <td>55.5</td> <td>66.4</td> <td>70.1</td> <td>81.7</td> <td>65.1</td> <td>81.2</td> </tr> <tr> <td colspan="11" align="left"><strong>Open-source</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2.5-VL-3B-Instruct</td> <td>3.8B</td> <td>64.5</td> <td>828</td> <td>61.2</td> <td>46.6</td> <td>51.2</td> <td>60.0</td> <td>76.8</td> <td>56.3</td> <td>81.4</td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2.5-4B</td> <td>3.7B</td> <td>65.1</td> <td>820</td> <td>60.8</td> <td>46.6</td> <td>51.8</td> <td>61.5</td> <td>78.2</td> <td>58.7</td> <td>81.4</td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2.5-VL-7B-Instruct</td> <td>8.3B</td> <td>70.9</td> <td>888</td> <td>68.1</td> <td>51.9</td> <td>58.0</td> <td>69.7</td> <td>82.2</td> <td>64.1</td> <td>84.3</td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2.5-8B</td> <td>8.1B</td> <td>68.1</td> <td>821</td> <td>64.5</td> <td>49.0</td> <td>56.2</td> <td>62.8</td> <td>82.5</td> <td>63.2</td> <td>84.6</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V-2.6</td> <td>8.1B</td> <td>65.2</td> <td>852</td> <td>60.8</td> <td>48.1</td> <td>49.8</td> <td>60.0</td> <td>78.0</td> <td>57.5</td> <td>82.1</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o-2.6</td> <td>8.7B</td> <td>70.2</td> <td>889</td> <td>73.3</td> <td>51.1</td> <td>50.9</td> <td>67.2</td> <td>80.6</td> <td>63.3</td> <td>86.1</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V-4.0</td> <td>4.1B</td> <td>69.0</td> <td>894</td> <td>66.9</td> <td>50.8</td> <td>51.2</td> <td>68.0</td> <td>79.7</td> <td>62.8</td> <td>82.9</td> </tr> </tbody> </table> </div> </details> <details> <summary>Click to view single image results on ChartQA, MME, RealWorldQA, TextVQA, DocVQA, MathVision, DynaMath, WeMath, Object HalBench and MM Halbench. </summary> <div align="center"> <table style="margin: 0px auto;"> <thead> <tr> <th nowrap="nowrap" align="left">model</th> <th>Size</th> <th>ChartQA</th> <th>MME</th> <th>RealWorldQA</th> <th>TextVQA</th> <th>DocVQA</th> <th>MathVision</th> <th>DynaMath</th> <th>WeMath</th> <th colspan="2">Obj Hal</th> <th colspan="2">MM Hal</th> </tr> </thead> <tbody> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>CHAIRs↓</td> <td>CHAIRi↓</td> <td nowrap="nowrap">score avg@3↑</td> <td nowrap="nowrap">hall rate avg@3↓</td> </tr> <tbody align="center"> <tr> <td colspan="14" align="left"><strong>Proprietary</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4v-20240409</td> <td>-</td> <td>78.5</td> <td>1927</td> <td>61.4</td> <td>78.0</td> <td>88.4</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">Gemini-1.5-Pro</td> <td>-</td> <td>87.2</td> <td>-</td> <td>67.5</td> <td>78.8</td> <td>93.1</td> <td>41.0</td> <td>31.5</td> <td>50.5</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4.1-mini-20250414</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>45.3</td> <td>47.7</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">Claude 3.5 Sonnet-20241022</td> <td>-</td> <td>90.8</td> <td>-</td> <td>60.1</td> <td>74.1</td> <td>95.2</td> <td>35.6</td> <td>35.7</td> <td>44.0</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td colspan="14" align="left"><strong>Open-source</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2.5-VL-3B-Instruct</td> <td>3.8B</td> <td>84.0</td> <td>2157</td> <td>65.4</td> <td>79.3</td> <td>93.9</td> <td>21.9</td> <td>13.2</td> <td>22.9</td> <td>18.3</td> <td>10.8</td> <td>3.9 </td> <td>33.3 </td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2.5-4B</td> <td>3.7B</td> <td>84.0</td> <td>2338</td> <td>64.3</td> <td>76.8</td> <td>91.6</td> <td>18.4</td> <td>15.2</td> <td>21.2</td> <td>13.7</td> <td>8.7</td> <td>3.2 </td> <td>46.5 </td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2.5-VL-7B-Instruct</td> <td>8.3B</td> <td>87.3</td> <td>2347</td> <td>68.5</td> <td>84.9</td> <td>95.7</td> <td>25.4</td> <td>21.8</td> <td>36.2</td> <td>13.3</td> <td>7.9</td> <td>4.1 </td> <td>31.6 </td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2.5-8B</td> <td>8.1B</td> <td>84.8</td> <td>2344</td> <td>70.1</td> <td>79.1</td> <td>93.0</td> <td>17.0</td> <td>9.4</td> <td>23.5</td> <td>18.3</td> <td>11.6</td> <td>3.6 </td> <td>37.2</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V-2.6</td> <td>8.1B</td> <td>79.4</td> <td>2348</td> <td>65.0</td> <td>80.1</td> <td>90.8</td> <td>17.5</td> <td>9.0</td> <td>20.4</td> <td>7.3</td> <td>4.7</td> <td>4.0 </td> <td>29.9 </td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o-2.6</td> <td>8.7B</td> <td>86.9</td> <td>2372</td> <td>68.1</td> <td>82.0</td> <td>93.5</td> <td>21.7</td> <td>10.4</td> <td>25.2</td> <td>6.3</td> <td>3.4</td> <td>4.1 </td> <td>31.3 </td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V-4.0</td> <td>4.1B</td> <td>84.4</td> <td>2298</td> <td>68.5</td> <td>80.8</td> <td>92.9</td> <td>20.7</td> <td>14.2</td> <td>32.7</td> <td>6.3</td> <td>3.5</td> <td>4.1 </td> <td>29.2 </td> </tr> </tbody> </table> </div> </details> <details> <summary>Click to view multi-image and video understanding results on Mantis, Blink and Video-MME. </summary> <div align="center"> <table style="margin: 0px auto;"> <thead> <tr> <th nowrap="nowrap" align="left">model</th> <th>Size</th> <th>Mantis</th> <th>Blink</th> <th nowrap="nowrap" colspan="2" >Video-MME</th> </tr> </thead> <tbody> <tr> <td></td> <td></td> <td></td> <td></td> <td>wo subs</td> <td>w subs</td> </tr> <tbody align="center"> <tr> <td colspan="6" align="left"><strong>Proprietary</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4v-20240409</td> <td>-</td> <td>62.7</td> <td>54.6</td> <td>59.9</td> <td>63.3</td> </tr> <tr> <td nowrap="nowrap" align="left">Gemini-1.5-Pro</td> <td>-</td> <td>-</td> <td>59.1</td> <td>75.0</td> <td>81.3</td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4o-20240513</td> <td>-</td> <td>-</td> <td>68.0</td> <td>71.9</td> <td>77.2</td> </tr> <tr> <td colspan="6" align="left"><strong>Open-source</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2.5-VL-3B-Instruct</td> <td>3.8B</td> <td>-</td> <td>47.6</td> <td>61.5</td> <td>67.6</td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2.5-4B</td> <td>3.7B</td> <td>62.7</td> <td>50.8</td> <td>62.3</td> <td>63.6</td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2.5-VL-7B-Instruct</td> <td>8.3B</td> <td>-</td> <td>56.4</td> <td>65.1</td> <td>71.6</td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2.5-8B</td> <td>8.1B</td> <td>67.7</td> <td>54.8</td> <td>64.2</td> <td>66.9</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V-2.6</td> <td>8.1B</td> <td>69.1</td> <td>53.0</td> <td>60.9</td> <td>63.6</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o-2.6</td> <td>8.7B</td> <td>71.9</td> <td>56.7</td> <td>63.9</td> <td>69.6</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V-4.0</td> <td>4.1B</td> <td>71.4</td> <td>54.0</td> <td>61.2</td> <td>65.8</td> </tr> </tbody> </table> </div> </details>

Examples

We deploy MiniCPM-V 4.0 on iPhone 16 Pro Max with iOS demo. The demo video is the raw screen recording without edition.

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