https://openbmb.cn/assets/js/ModelDetailPage-Ck6Mop3r.js

1import{r as j,j as e}from"./react-vendor-Dg-XgdGg.js";import{i as v,u as A,f as We,p as U,S as ke,a as w,o as se,b as be}from"./main-DMog5HPc.js";import{b as ve,H as ye,c as Pe,V as ze,M as Re,a as Ve,S as P,C as J}from"./ScrollReveal-DSln2zvj.js";import{m as y,b as ie,c as X,d as Q}from"./vendor-IEMZZwl2.js";import{d as De,e as Ge,N as Oe}from"./router-vendor-C9uBNE_R.js";const O={id:"minicpm-o-4-5",name:"MiniCPM-o 4.5",category:"全模态模型",overview:"MiniCPM-o 4.5 是 MiniCPM-o 系列最新、能力最强的全模态模型。模型采用端到端架构，基于 SigLip2、Whisper-medium、CosyVoice2 和 Qwen3-8B 构建，总计 90 亿参数。它在多项能力上实现显著提升，并引入了全新的全双工多模态实时流式交互特性。",timeline:[{id:"minicpm-3o-pre",name:"MiniCPM-3o",position:.08},{id:"minicpm-3o",name:"MiniCPM-3o",position:.3},{id:"minicpm-o-4-5",name:"MiniCPM-o 4.5",parameters:"9B",position:.5,current:!0},{id:"minicpm-o-future-1",name:"MiniCPM-o 4.5",position:.72},{id:"minicpm-o-future-2",name:"MiniCPM-o 4.5",position:.94}],highlights:[{title:"领先的视觉能力",description:"超越 GPT-4o、Gemini 2.0 Pro 等商用模型，接近 Gemini 2.5 Flash。",bigNumber:"77.6",bigNumberLabel:`综合评测平均分
2OpenCompass`,variant:"spotlight",span:1},{title:"强大语音能力",description:"支持中英文双语实时语音对话，可配置音色，并支持声音克隆与角色扮演。",variant:"standard"},{title:"全双工与主动交互",description:"实现实时视频/音频输入与文本/语音输出的并行无阻塞处理，模型可主动发起交互。",variant:"standard"}],simpleUsage:{label:"极简使用",description:"支持 PyTorch、llama.cpp、vLLM、SGLang 等多种推理框架，并提供全双工体验的本地 Web 演示。"},performance:{note:"* 数据来源于真实模型评测",tabs:[{id:"visual",label:"视觉推理与感知",description:"在视觉推理与感知评测中，MiniCPM-o 4.5-Thinking 在多项核心数据集上保持领先或接近领先表现。",columns:["OpenCompass","MMBench EN v1.1","MathVista","OCRBench"],rows:[{metric:"MiniCPM-o 4.5-Thinking",values:["78.2","89","81","879"]},{metric:"Gemini2.5-Flash-Thinking",values:["79.9","87.1","79.4","853"]},{metric:"Qwen3-Omni-30B-A3B-Thinking",values:["78.5","88.2","80","859"]}]},{id:"voice",label:"语音交互能力",description:"语音交互能力覆盖中英文语音生成和长文本语音生成，MiniCPM-o 4.5-Instruct 在多项错误率指标上保持较低水平。",columns:[`seedtts test-zh
3CER↓`,`seedtts test-en
4WER↓`,`LongTTS-en
5WER↓`],rows:[{metric:"MiniCPM-o 4.5-Instruct",values:["0.86%","2.38%","3.37%"]},{metric:"CosyVoice2",values:["1.45%","2.57%","14.80%"]},{metric:"Qwen3-Omni-30B-A3B-Instruct",values:["1.41%","3.39%","17.33%"]}]},{id:"duplex",label:"全双工实时流式交互",description:"全双工实时流式交互评测覆盖日常实时交互、世界感知和视频推理能力。",columns:["Daily-Omni","WorldSense","Video-Holmes"],rows:[{metric:"MiniCPM-o 4.5-Instruct",values:["80.2","55.7","64.3"]},{metric:"Gemini2.5-Flash-Nonthinking",values:["79.3","52.6","51.3"]},{metric:"Qwen3-Omni-30B-A3B-Instruct",values:["70.7","54","50.4"]}]},{id:"ocr",label:"文档解析",description:"文档解析评测基于 OmniDocBench 中英文子集的整体编辑距离，数值越低表示解析质量越好。",columns:["OmniDocBench EN ↓","OmniDocBench ZH ↓"],rows:[{metric:"MiniCPM-o 4.5-Instruct",values:["0.109","0.162"]},{metric:"Gemini2.5-Flash-Nonthinking",values:["0.214","0.29"]},{metric:"Qwen3-Omni-30B-A3B-Instruct",values:["0.216","0.363"]}]},{id:"multilang",label:"语音生成能力",description:"语音生成能力评测覆盖中文 ASR、英文 ASR 与英中语音翻译，反映语音识别与生成的综合表现。",columns:["AISHELL-1↓","LibriSpeech test-clean↓","CoVoST 2 en2zh"],rows:[{metric:"MiniCPM-o 4.5-Instruct",values:["0.9","1.4","49.9"]},{metric:"Kimi-Audio",values:["0.6","1.3","36.6"]},{metric:"Qwen3-Omni-30B-A3B-Instruct",values:["0.6","1.2","46.6"]}]}]},techHighlights:[{label:"端到端全模态架构",items:[{label:"端到端全模态架构",description:"视觉、语音编码器/解码器与 LLM 通过隐藏状态稠密è¿

5žæŽ¥ï¼Œå®žçŽ°æ›´ä¼˜çš„信息流动与控制，充分挖掘多模态知识。"}]},{label:"全双工实时流式机制",items:[{label:"全双工实时流式机制",description:`将离线编码器/解码器改造为在线全双工模式，支持流式输入输出。
6语音 token 解码器以交错方式生成文本和语音 token，实现全双工语音生成（如超过 1 分钟的稳定长语音）。
7通过时分复用（TDM）机制在 LLM 主干中同步多模态流，以毫秒级时间片划分并行流为顺序信息组。`}]},{label:"主动交互机制",items:[{label:"主动交互机制",description:"LLM 以 1Hz 频率持续监控输入视频和音频流，自主决策是否发言，高决策频率结合全双工特性实现主动交互。"}]}],values:[{title:"使用价值",index:"01",items:[{label:"更强的性能",description:"以 9B 参数达到超越商用模型的视觉语言能力。"},{label:"更类人的人机交互",description:"全双工 + 主动交互，带来流畅、自然的实时对话体验。"}]},{title:"科学价值",index:"01",items:[{label:"数据空间拓展",description:"文本预训练数据趋于耗尽，多模态数据仍有巨大挖掘潜力。"},{label:"训练方式创新",description:"让模型像人一样学习互联网视频数据。"},{label:"机制探索",description:"全模态、流式、全双工设计，模拟类人感知与交互机制。"},{label:"自主交互",description:"摆脱外部工具束缚，孕育更多能力增长可能性。"}]}]},Z={id:"minicpm-o-2-6",name:"MiniCPM-o 2.6",category:"全模态模型",variant:"general",modelTag:"多模态",heroImage:v("/images/general-model-hero-bg.png"),overview:"MiniCPM-o 2.6 是 MiniCPM-o 系列功能强大的全模态模型。模型基于 SigLip-400M、Whisper-medium-300M、ChatTTS-200M 和 Qwen2.5-7B 构建，总计 80 亿参数，支持实时语音对话和多模态直播能力。",generalIntro:{name:"MiniCPM-o 2.6",tag:"多模态",description:"MiniCPM-o 2.6 是 MiniCPM-o 系列中最新且功能最强大的模型。该模型基于 SigLip-400M、Whisper-medium-300M、ChatTTS-200M 和 Qwen2.5-7B 构建，总共包含 80 亿参数。它比 MiniCPM-V 2.6 有显著的性能提升，并引入了实时语音对话和多模态直播的新功能。"},generalHighlights:[{index:"01",title:"领先的视觉能力",description:"MiniCPM-o 2.6 在 OpenCompass 上的平均得分为 70.2，这是一个覆盖 8 个流行基准的综合评估。仅用 80 亿参数，它就超越了广泛使用的专有模型如 GPT-4o-202405、Gemini 1.5 Pro 和 Claude 3.5 Sonnet 在单图像理解方面。它还在多图像和视频理解方面优于 GPT-4V 和 Claude 3.5 Sonnet，并展示了良好的上下文学习能力。"},{index:"02",title:"最先进的语音能力",description:"MiniCPM-o 2.6 支持英语和中文双语实时语音对话，可配置声音。它在音频理解任务（如 ASR 和 STT 翻译）上优于 GPT-4o-realtime，并在开源社区的语义和声学评估中表现出色。它还允许有趣的功能，如情绪/

7速度/风格控制、端到端语音克隆、角色扮演等。"},{index:"03",title:"强大的多模态直播能力",description:"作为新功能，MiniCPM-o 2.6 可以接受与用户查询无关的连续视频和音频流，并支持实时语音交互。它在 StreamingBench 上优于 GPT-4o-202408 和 Claude 3.5 Sonnet，在开源社区中展示出了最先进的性能，这是一个针对实时视频理解、全源（视频和音频）理解和多模态上下文理解的全面基准。"},{index:"04",title:"强大的 OCR 能力和其他功能",description:"继承自 MiniCPM-V 系列流行的视觉能力，MiniCPM-o 2.6 可以处理任意纵横比和高达 180 万像素（例如 1344x1344）的图像。它在 OCRBench 上对于低于 250 亿参数的模型达到了最先进的性能，超过了 GPT-4o-202405 等专有模型。基于最新的 RLAIF-V 和 VisCPM 技术，它具有可信行为，在 MMHal-Bench 上优于 GPT-4o 和 Claude 3.5 Sonnet，并支持超过 30 种语言的多语言能力。"},{index:"05",title:"卓越的效率",description:"除了其友好的大小外，MiniCPM-o 2.6 还展示了最先进的 token 密度（即每个视觉 token 编码的像素数）。处理 180 万像素图像时只生成 640 个 token，比大多数模型少 75%。这直接提高了推理速度、首 token 延迟、内存使用和功耗。因此，MiniCPM-o 2.6 可以在 iPad 等终端设备上高效支持多模态直播。"},{index:"06",title:"易于使用",description:"MiniCPM-o 2.6 可以通过多种方式轻松使用：(1) llama.cpp 支持本地设备上的高效 CPU 推理，(2) int4 和 GGUF 格式的量化模型，共 16 种尺寸，(3) vLLM 支持高吞吐量和内存高效的推理，(4) 使用 LLaMA-Factory 进行新领域和任务的微调，(5) 使用 Gradio 快速设置本地 WebUI 演示，以及(6) 服务器上的在线网络演示。"}],timeline:[{id:"minicpm-3o-pre",name:"MiniCPM-3o",position:.08},{id:"minicpm-3o",name:"MiniCPM-3o",position:.3},{id:"minicpm-o-2-6",name:"9B",parameters:"9B",position:.5,current:!0},{id:"minicpm-o-4-5-tl",name:"MiniCPM-o 4.5",position:.72},{id:"minicpm-o-4-5-tl2",name:"MiniCPM-o 4.5",position:.94}],highlights:[],performance:{note:"* 数据来源于真实模型评测",tabs:[{id:"visual",label:"视觉推理与感知",description:"",tableLayout:"single",highlightColumnIndex:0,columns:["MiniCPM-o 2.6","MiniCPM-V 2.6","GPT-4o-20240513","GPT4V","LLaVA-NeXT-Interleave 14B","LLaVA-OneVision-72B","MANTIS 8B","Qwen2-VL-7B","InternVL2.5-8B"],rows:[{metric:"BLINK val",values:["56.7","53","68","54.6","52.6","55.4","49.1","53.2","54.8"]},{metric:"Mantis Eval",values:["71.9","69.1","-","62.7","66.4","77.6","59.5","69.6*","67.7"]},{metric:"MIRB",values:["58.6","53.8","-","53.1","30.2","-","34.8","67.6*","52.5"]},{metric:"Video-MME (wo / w subs)",values:["63.9/67.9","60.9/63.6","71.9/77.2","59.9/63.3","-","66.2/69.5","-","63.3/69.0","64.2/66.9"]}]}],barGroups:[{label:"BLINK val",values:[{label:"68",value:68,model:"GPT-4o-20240513"},{label:"54.6",value:54.6,model:"GPT4V"},{label:"52.6",value:52.6,model:"LLaVA-NeXT-Interleave 14B"},{label:"55.4",value:55.4,model:"LLaVA-OneVision-72B"},{label:"49.1",value:49.1,model:"MANTIS 8B"},{label:"53.2",value:53.2,model:"Qwen2-VL-7B"},{label:"54.8",value:54.8,model:"InternVL2.5-8B"},{label:"53",value:53,model:"MiniCPM-V 2.6"},{label:"56.7",value:56.7,model:"MiniCPM-o 2.6"}]},{label:"Mantis Eval",values:[{label:"62.7",value:62.7,model:"GPT4V"},{label:"66.4",value:66.4,model:"LLaVA-NeXT-Interleave 14B"},{label:"77.6",value:77.6,model:"LLaVA-OneVision-72B"},{label:"59.5",value:59.5,model:"MANTIS 8B"},{label:"69.6*",value:69.6,model:"Qwen2-VL-7B"},{label:"67.7",value:67.7,model:"InternVL2.5-8B"},{label:"69.1",value:69.1,model:"MiniCPM-V 2.6"},{label:"71.9",value:71.9,model:"MiniCPM-o 2.6"}]},{label:"MIRB",values:[{label:"53.1",value:53.1,model:"GPT4V"},{label:"30.2",value:30.2,model:"LLaVA-NeXT-Interleave 14B"},{label:"34.8",value:34.8,model:"MANTIS 8B"},{label:"67.6*",value:67.6,model:"Qwen2-VL-7B"},{label:"52.5",value:52.5,model:"InternVL2.5-8B"},{label:"53.8",value:53.8,model:"MiniCPM-V 2.6"},{label:"58.6",value:58.6,model:"MiniCPM-o 2.6"}]},{label:"Video-MME",values:[{label:"71.9/77.2",value:71.9,model:"GPT-4o-20240513"},{label:"59.9/63.3",value:59.9,model:"GPT4V"},{label:"66.2/69.5",value:66.2,model:"LLaVA-OneVision-72B"},{label:"63.3/69.0",value:63.3,model:"Qwen2-VL-7B"},{label:"64.2/66.9",value:64.2,model:"InternVL2.5-8B"},{label:"60.9/63.6",value:60.9,model:"MiniCPM-V 2.6"},{label:"63.9/67.9",value:63.9,model:"MiniCPM-o 2.6"}]}]},techHighlights:[{label:"端到端全模态架构",items:[{label:"端到端全模态架构",description:"不同模态的编码器/解码器以端到端的方式连接和训练，以充分利用丰富的多模态知识。"}]},{label:"全模态流媒体机制",items:[{label:"全模态流媒体机制",description:"我们将离线模态编码器/解码器改为在线版本，以处理流式输入/输出。我们设计了一种时分复用（TDM）机制，用于在 LLM 主干中进行全模态流处理。它将并行的全模态流在小的时间片段内分割成顺序信息。"}]},{label:"可配置的语音建模设计",items:[{label:"可配置的语音建模设计",description:"我们设计了一个多模态系统提示，包括传统的文本系统提示，以及一个新的音频系统提示来确定助手的声音ã€

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