Minicpm O2dot6 En - Minicpm O

MiniCPM-o 2.6

Archieve at: 2026-02-02

MiniCPM-o 2.6 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for real-time speech conversation and multimodal live streaming. Notable features of MiniCPM-o 2.6 include:

🔥 Leading Visual Capability. MiniCPM-o 2.6 achieves an average score of 70.2 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-202405, Gemini 1.5 Pro, and Claude 3.5 Sonnet for single image understanding. It also outperforms GPT-4V and Claude 3.5 Sonnet in multi-image and video understanding, and shows promising in-context learning capability.
🎙 State-of-the-art Speech Capability. MiniCPM-o 2.6 supports bilingual real-time speech conversation with configurable voices in English and Chinese. It outperforms GPT-4o-realtime on audio understanding tasks such as ASR and STT translation, and shows state-of-the-art performance on speech conversation in both semantic and acoustic evaluations in the open-source community. It also allows for fun features such as emotion/speed/style control, end-to-end voice cloning, role play, etc.
🎬 Strong Multimodal Live Streaming Capability. As a new feature, MiniCPM-o 2.6 can accept continuous video and audio streams independent of user queries, and support real-time speech interaction. It outperforms GPT-4o-202408 and Claude 3.5 Sonnet and shows state-of-the-art performance in the open-source community on StreamingBench, a comprehensive benchmark for real-time video understanding, omni-source (video & audio) understanding, and multimodal contextual understanding.
💪 Strong OCR Capability and Others. Advancing popular visual capabilities from MiniCPM-V series, MiniCPM-o 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves state-of-the-art performance on OCRBench for models under 25B, surpassing proprietary models such as GPT-4o-202405. Based on the latest RLAIF-V and VisCPM techniques, it features trustworthy behaviors, outperforming GPT-4o and Claude 3.5 Sonnet on MMHal-Bench, and supports multilingual capabilities on more than 30 languages.
🚀 Superior Efficiency. In addition to its friendly size, MiniCPM-o 2.6 also shows state-of-the-art token density (i.e., the number of pixels encoded into each visual token). It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-o 2.6 can efficiently support multimodal live streaming on end-side devices such as iPads.
💫 Easy Usage. MiniCPM-o 2.6 can be easily used in various ways: (1) llama.cpp support for efficient CPU inference on local devices, (2) int4 and GGUF format quantized models in 16 sizes, (3) vLLM support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with LLaMA-Factory, (5) quick local WebUI demo, and (6) online web demo on server.

Model Architecture.

End-to-end Omni-modal Architecture. Different modality encoders/decoders are connected and trained in an end-to-end fashion to fully exploit rich multimodal knowledge. The model is trained in a fully end-to-end manner with only CE loss.
Omni-modal Live Streaming Mechanism. (1) We change the offline modality encoder/decoders into online ones for streaming inputs/outputs. (2) We devise a time-division multiplexing (TDM) mechanism for omni-modality streaming processing in the LLM backbone. It divides parallel omni-modality streams into sequential info within small periodic time slices.
Configurable Speech Modeling Design. We devise a multimodal system prompt, including traditional text system prompt, and a new audio system prompt to determine the assistant voice. This enables flexible voice configurations in inference time, and also facilitates end-to-end voice cloning and description-based voice creation.

Evaluation

<div align="center"> </div> <details> <summary>Click to view visual understanding results.</summary>

Image Understanding

<div align="center"> <table style="margin: 0px auto;"> <thead> <tr> <th align="left">Model</th> <th>Size</th> <th>Token Density<sup>+</sup></th> <th>OpenCompass</th> <th>OCRBench</th> <th>MathVista mini</th> <th>ChartQA</th> <th>MMVet</th> <th>MMStar</th> <th>MME</th> <th>MMB1.1 test</th> <th>AI2D</th> <th>MMMU val</th> <th>HallusionBench</th> <th>TextVQA val</th> <th>DocVQA test</th> <th>MathVerse mini</th> <th>MathVision</th> <th>MMHal Score</th> </tr> </thead> <tbody align="center"> <tr> <td colspan="19" align="left"><strong>Proprietary</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4o-20240513</td> <td>-</td> <td>1088</td> <td><u>69.9</u></td> <td>736</td> <td>61.3</td> <td>85.7</td> <td><strong>69.1</strong></td> <td>63.9</td> <td>2328.7</td> <td>82.2</td> <td>84.6</td> <td><strong>69.2</strong></td> <td><strong>55.0</strong></td> <td>-</td> <td>92.8</td> <td><strong>50.2</strong></td> <td><strong>30.4</strong></td> <td><u>3.6</u></td> </tr> <tr> <td nowrap="nowrap" align="left">Claude3.5-Sonnet</td> <td>-</td> <td>750</td> <td>67.9</td> <td>788</td> <td>61.6</td> <td><strong>90.8</strong></td> <td>66.0</td> <td>62.2</td> <td>1920.0</td> <td>78.5</td> <td>80.2</td> <td><u>65.9</u></td> <td>49.9</td> <td>-</td> <td><strong>95.2</strong></td> <td>-</td> <td>-</td> <td>3.4</td> </tr> <tr> <td nowrap="nowrap" align="left">Gemini 1.5 Pro</td> <td>-</td> <td>-</td> <td>64.4</td> <td>754</td> <td>57.7</td> <td>81.3</td> <td>64.0</td> <td>59.1</td> <td>2110.6</td> <td>73.9</td> <td>79.1</td> <td>60.6</td> <td>45.6</td> <td>73.5</td> <td>86.5</td> <td>-</td> <td>19.2</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4o-mini-20240718</td> <td>-</td> <td>1088</td> <td>64.1</td> <td>785</td> <td>52.4</td> <td>-</td> <td>66.9</td> <td>54.8</td> <td>2003.4</td> <td>76.0</td> <td>77.8</td> <td>60.0</td> <td>46.1</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>3.3</td> </tr> <tr> <td colspan="19" align="left"><strong>Open Source</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">Cambrian-34B</td> <td>34B</td> <td><u>1820</u></td> <td>58.3</td> <td>591</td> <td>50.3</td> <td>75.6</td> <td>53.2</td> <td>54.2</td> <td>2049.9</td> <td>77.8</td> <td>79.5</td> <td>50.4</td> <td>41.6</td> <td>76.7</td> <td>75.5</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">GLM-4V-9B</td> <td>13B</td> <td>784</td> <td>59.1</td> <td>776</td> <td>51.1</td> <td>-</td> <td>58.0</td> <td>54.8</td> <td>2018.8</td> <td>67.9</td> <td>71.2</td> <td>46.9</td> <td>45.0</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">Pixtral-12B</td> <td>12B</td> <td>256</td> <td>61.0</td> <td>685</td> <td>56.9</td> <td>81.8</td> <td>58.5</td> <td>54.5</td> <td>-</td> <td>72.7</td> <td>79.0</td> <td>51.1</td> <td>47.0</td> <td>75.7</td> <td>90.7</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">VITA-1.5</td> <td>8B</td> <td>784</td> <td>63.3</td> <td>741</td> <td>66.2</td> <td>-</td> <td>52.7</td> <td>60.2</td> <td>2328.1</td> <td>76.8</td> <td>79.2</td> <td>52.6</td> <td>44.6</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">DeepSeek-VL2-27B (4B)</td> <td>27B</td> <td>672</td> <td>66.4</td> <td>809</td> <td>63.9</td> <td>86.0</td> <td>60.0</td> <td>61.9</td> <td>2253.0</td> <td>81.2</td> <td>83.8</td> <td>54.0</td> <td>45.3</td> <td><u>84.2</u></td> <td>93.3</td> <td>-</td> <td>-</td> <td>3.0</td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2-VL-7B</td> <td>8B</td> <td>784</td> <td>67.1</td> <td><u>866</u></td> <td>58.2</td> <td>83.0</td> <td>62.0</td> <td>60.7</td> <td>2326.0</td> <td>81.8</td> <td>83.0</td> <td>54.1</td> <td>50.6</td> <td><strong>84.3</strong></td> <td><u>94.5</u></td> <td>31.9</td> <td>16.3</td> <td>3.2</td> </tr> <tr> <td nowrap="nowrap" align="left">LLaVA-OneVision-72B</td> <td>72B</td> <td>182</td> <td>68.1</td> <td>741</td> <td>67.5</td> <td>83.7</td> <td>60.6</td> <td><strong>65.8</strong></td> <td>2261.0</td> <td><strong>85.0</strong></td> <td><u>85.6</u></td> <td>56.8</td> <td>49.0</td> <td>80.5</td> <td>91.3</td> <td>39.1</td> <td>-</td> <td>3.5</td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2.5-8B</td> <td>8B</td> <td>706</td> <td>68.3</td> <td>822</td> <td><u>64.4</u></td> <td>84.8</td> <td>62.8</td> <td>62.8</td> <td>2344.0</td> <td><u>83.6</u></td> <td>84.5</td> <td>56.0</td> <td>50.1</td> <td>79.1</td> <td>93.0</td> <td>39.5</td> <td>19.7</td> <td>3.4</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td> <td>8B</td> <td><strong>2822</strong></td> <td>65.2</td> <td>852*</td> <td>60.6</td> <td>79.4</td> <td>60.0</td> <td>57.5</td> <td><u>2348.4*</u></td> <td>78.0</td> <td>82.1</td> <td>49.8*</td> <td>48.1*</td> <td>80.1</td> <td>90.8</td> <td>25.7</td> <td>18.3</td> <td>3.6</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td> <td>8B</td> <td><strong>2822</strong></td> <td><strong>70.2</strong></td> <td><strong>897*</strong></td> <td><strong>71.9*</strong></td> <td><u>86.9*</u></td> <td><u>67.5</u></td> <td><u>64.0</u></td> <td><strong>2372.0*</strong></td> <td>80.5</td> <td><strong>85.8</strong></td> <td>50.4*</td> <td><u>51.9</u></td> <td>82.0</td> <td>93.5</td> <td><u>41.4*</u></td> <td><u>23.1*</u></td> <td><strong>3.8</strong></td> </tr> </tbody> </table> </div> * We evaluate this benchmark using chain-of-thought prompting. Specifically, for MME, we used this technique only for the Cognition set.

<sup>+</sup> Token Density: number of pixels encoded into each visual token at maximum resolution, i.e., # pixels at maximum resolution / # visual tokens.

Note: For proprietary models, we calculate token density based on the image encoding charging strategy defined in the official API documentation, which provides an upper-bound estimation.

Multi-image and Video Understanding

<div align="center"> <table style="margin: 0px auto;"> <thead> <tr> <th align="left">Model</th> <th>Size</th> <th>BLINK val</th> <th>Mantis Eval</th> <th>MIRB</th> <th>Video-MME (wo / w subs)</th> </tr> </thead> <tbody align="center"> <tr> <td colspan="6" align="left"><strong>Proprietary</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4o-20240513</td> <td>-</td> <td><strong>68.0</strong></td> <td>-</td> <td>-</td> <td><strong>71.9/77.2<strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT4V</td> <td>-</td> <td>54.6</td> <td>62.7</td> <td>53.1</td> <td>59.9/63.3</td> </tr> <tr> <td colspan="6" align="left"><strong>Open-source</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">VITA-1.5</td> <td>8B</td> <td>45.0</td> <td>-</td> <td>-</td> <td>56.1/58.7</td> </tr> <tr> <td nowrap="nowrap" align="left">LLaVA-NeXT-Interleave 14B</td> <td>14B</td> <td>52.6</td> <td>66.4</td> <td>30.2</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">LLaVA-OneVision-72B</td> <td>72B</td> <td>55.4</td> <td><strong>77.6</strong></td> <td>-</td> <td><u>66.2/69.5</u></td> </tr> <tr> <td nowrap="nowrap" align="left">MANTIS 8B</td> <td>8B</td> <td>49.1</td> <td>59.5</td> <td>34.8</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2-VL-7B</td> <td>8B</td> <td>53.2</td> <td>69.6*</td> <td><strong>67.6*</strong></td> <td>63.3/69.0</td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2.5-8B</td> <td>8B</td> <td>54.8</td> <td>67.7</td> <td>52.5</td> <td>64.2/66.9</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td> <td>8B</td> <td>53.0</td> <td>69.1</td> <td>53.8</td> <td>60.9/63.6</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td> <td>8B</td> <td><u>56.7</u></td> <td><u>71.9</u></td> <td><u>58.6</u></td> <td>63.9/67.9</td> </tr> </tbody> </table> </div> * We evaluate officially released checkpoints by ourselves. </details> <details> <summary>Click to view audio understanding and speech conversation results.</summary>

Audio Understanding

<div align="center"> <table style="margin: 0px auto;"> <thead> <tr> <th align="left">Task</th> <th>Size</th> <th colspan="3">ASR (zh)</th> <th colspan="3">ASR (en)</th> <th colspan="2">AST</th> <th>Emotion</th> </tr> <tr> <th align="left">Metric</th> <td></td> <th colspan="3">CER↓</th> <th colspan="3">WER↓</th> <th colspan="2">BLEU↑</th> <th>ACC↑</th> </tr> <tr> <th align="left">Dataset</th> <td></td> <th>AISHELL-1</th> <th>Fleurs zh</th> <th>WenetSpeech test-net</th> <th>LibriSpeech test-clean</th> <th>GigaSpeech</th> <th>TED-LIUM</th> <th>CoVoST en2zh</th> <th>CoVoST zh2en</th> <th>MELD emotion</th> </tr> </thead> <tbody align="center"> <tr> <td colspan="11" align="left"><strong>Proprietary</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4o-Realtime</td> <td>-</td> <td>7.3*</td> <td><u>5.4*</u></td> <td>28.9*</td> <td>2.6*</td> <td>12.9*</td> <td>4.8*</td> <td>37.1*</td> <td>15.7*</td> <td>33.2*</td> </tr> <tr> <td nowrap="nowrap" align="left">Gemini 1.5 Pro</td> <td>-</td> <td>4.5*</td> <td>5.9*</td> <td>14.3*</td> <td>2.9*</td> <td>10.6*</td> <td><strong>3.0*</strong></td> <td><u>47.3*</u></td> <td>22.6*</td> <td>48.4*</td> </tr> <tr> <td colspan="11" align="left"><strong>Open-Source</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2-Audio-7B</td> <td>8B</td> <td>-</td> <td>7.5</td> <td>-</td> <td><strong>1.6</strong></td> <td>-</td> <td>-</td> <td>45.2</td> <td><u>24.4</u></td> <td><strong>55.3</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2-Audio-7B-Instruct</td> <td>8B</td> <td>2.6*</td> <td>6.9*</td> <td><u>10.3*</u></td> <td>3.1*</td> <td><u>9.7</u>*</td> <td>5.9*</td> <td>39.5*</td> <td>22.9*</td> <td>17.4*</td> </tr> <tr> <td nowrap="nowrap" align="left">VITA-1.5</td> <td>8B</td> <td>2.16</td> <td>-</td> <td>8.4</td> <td>3.4</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">GLM-4-Voice-Base</td> <td>9B</td> <td><u>2.5</u></td> <td>-</td> <td>-</td> <td>2.8</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td> <td>8B</td> <td><strong>1.6</strong></td> <td><strong>4.4</strong></td> <td><strong>6.9</strong></td> <td><u>1.7</u></td> <td><strong>8.7</strong></td> <td><strong>3.0</strong></td> <td><strong>48.2</strong></td> <td><strong>27.2</strong></td> <td><u>52.4</u></td> </tr> </tbody> </table> </div> * We evaluate officially released checkpoints by ourselves.

Speech Generation

<div align="center"> <table style="margin: 0px auto;"> <thead> <tr> <th align="left">Task</th> <th>Size</th> <th colspan="9">SpeechQA</th> </tr> <tr> <th align="left">Metric</th> <th></th> <th colspan="3">ACC↑</th> <th>G-Eval (10 point)↑</th> <th>Semantic ELO score↑</th> <th>Acoustic ELO score↑</th> <th>Overall ELO score↑</th> <th>UTMOS↑</th> <th>ASR-WER↓</th> </tr> <tr> <th align="left">Dataset</th> <th></th> <th>Speech Llama Q.</th> <th>Speech Web Q.</th> <th>Speech Trivia QA</th> <th>Speech AlpacaEval</th> <th colspan="5">AudioArena</th> </tr> </thead> <tbody align="center"> <tr> <td colspan="11" align="left"><strong>Proprietary</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4o-Realtime</td> <td></td> <td><strong>71.7</strong></td> <td><strong>51.6</strong></td> <td><strong>69.7</strong></td> <td><strong>7.4</strong></td> <td><strong>1157</strong></td> <td><strong>1203</strong></td> <td><strong>1200</strong></td> <td><strong>4.2</strong></td> <td><strong>2.3</strong></td> </tr> <tr> <td colspan="11" align="left"><strong>Open-Source</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GLM-4-Voice</td> <td>9B</td> <td>50.0</td> <td>32.0</td> <td>36.4</td> <td><u>5.1</u></td> <td>999</td> <td>1147</td> <td>1035</td> <td><u>4.1</u></td> <td><u>11.7</u></td> </tr> <tr> <td nowrap="nowrap" align="left">Llama-Omni</td> <td>8B</td> <td>45.3</td> <td>22.9</td> <td>10.7</td> <td>3.9</td> <td>960</td> <td>878</td> <td>897</td> <td>3.2</td> <td>24.3</td> </tr> <tr> <td nowrap="nowrap" align="left">VITA-1.5</td> <td>8B</td> <td>46.7</td> <td>28.1</td> <td>23.3</td> <td>2.0</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td nowrap="nowrap" align="left">Moshi</td> <td>7B</td> <td>43.7</td> <td>23.8</td> <td>16.7</td> <td>2.4</td> <td>871</td> <td>808</td> <td>875</td> <td>2.8</td> <td>8.2</td> </tr> <tr> <td nowrap="nowrap" align="left">Mini-Omni</td> <td>1B</td> <td>22.0</td> <td>12.8</td> <td>6.9</td> <td>2.5</td> <td>926</td> <td>803</td> <td>865</td> <td>3.4</td> <td>10.0</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td> <td>8B</td> <td><u>61.0</u></td> <td><u>40.0</u></td> <td><u>40.2</u></td> <td><u>5.1</u></td> <td><u>1088</u></td> <td><u>1163</u></td> <td><u>1131</u></td> <td><strong>4.2</strong></td> <td>9.8</td> </tr> </tbody> </table> </div> All results are from AudioEvals, and the evaluation methods along with further details can be found in <a href="https://github.com/OpenBMB/UltraEval-Audio" target="_blank">AudioEvals</a>.

End-to-end Voice Cloning

<div align="center"> <table style="margin: 0px auto;"> <thead> <tr> <th align="left">Task</th> <th colspan="2">Voice cloning</th> </tr> <tr> <th align="left">Metric</th> <th>SIMO↑</th> <th>SIMO↑</th> </tr> <tr> <th align="left">Dataset</th> <th>Seed-TTS test-zh</th> <th>Seed-TTS test-en</th> </tr> </thead> <tbody align="center"> <tr> <td nowrap="nowrap" align="left">F5-TTS</td> <td><strong>76</strong></td> <td><strong>67</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">CosyVoice</td> <td><u>75</u></td> <td><u>64</u></td> </tr> <tr> <td nowrap="nowrap" align="left">FireRedTTS</td> <td>63</td> <td>46</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td> <td>57</td> <td>47</td> </tr> </tbody> </table> </div> </details> <details> <summary>Click to view multimodal live streaming results.</summary>

Multimodal Live Streaming: results on StreamingBench

<table style="margin: 0px auto;"> <thead> <tr> <th align="left">Model</th> <th>Size</th> <th>Real-Time Video Understanding</th> <th>Omni-Source Understanding</th> <th>Contextual Understanding</th> <th>Overall</th> </tr> </thead> <tbody align="center"> <tr> <td colspan="7" align="left"><strong>Proprietary</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">Gemini 1.5 Pro</td> <td>-</td> <td><u>77.4</u></td> <td><strong>67.8</strong></td> <td><strong>51.1</strong></td> <td><strong>70.3</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">GPT-4o-202408</td> <td>-</td> <td>74.5</td> <td>51.0</td> <td><u>48.0</u></td> <td>64.1</td> </tr> <tr> <td nowrap="nowrap" align="left">Claude-3.5-Sonnet</td> <td>-</td> <td>74.0</td> <td>41.4</td> <td>37.8</td> <td>59.7</td> </tr> <tr> <td colspan="9" align="left"><strong>Open-source</strong></td> </tr> <tr> <td nowrap="nowrap" align="left">VILA-1.5</td> <td>8B</td> <td>61.5</td> <td>37.5</td> <td>26.7</td> <td>49.5</td> </tr> <tr> <td nowrap="nowrap" align="left">LongVA</td> <td>7B</td> <td>63.1</td> <td>35.9</td> <td>30.2</td> <td>50.7</td> </tr> <tr> <td nowrap="nowrap" align="left">LLaVA-Next-Video-34B</td> <td>34B</td> <td>69.8</td> <td>41.7</td> <td>34.3</td> <td>56.7</td> </tr> <tr> <td nowrap="nowrap" align="left">Qwen2-VL-7B</td> <td>8B</td> <td>71.2</td> <td>40.7</td> <td>33.1</td> <td>57.0</td> </tr> <tr> <td nowrap="nowrap" align="left">InternVL2-8B</td> <td>8B</td> <td>70.1</td> <td>42.7</td> <td>34.1</td> <td>57.0</td> </tr> <tr> <td nowrap="nowrap" align="left">VITA-1.5</td> <td>8B</td> <td>70.9</td> <td>40.8</td> <td>35.8</td> <td>57.4</td> </tr> <tr> <td nowrap="nowrap" align="left">LLaVA-OneVision-7B</td> <td>8B</td> <td>74.3</td> <td>40.8</td> <td>31.0</td> <td>58.4</td> </tr> <tr> <td nowrap="nowrap" align="left">InternLM-XC2.5-OL-7B</td> <td>8B</td> <td>75.4</td> <td>46.2</td> <td>33.6</td> <td>60.8</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td> <td>8B</td> <td>72.4</td> <td>40.2</td> <td>33.4</td> <td>57.7</td> </tr> <tr> <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td> <td>8B</td> <td><strong>79.9</strong></td> <td><u>53.4</u></td> <td>38.5</td> <td><u>66.0</u></td> </tr> </tbody> </table> </details>

Examples

We deploy MiniCPM-o 2.6 on end devices. The demo video is the raw-speed recording on an iPad Pro and a Web demo.