{
  "data": {
    "a": {
      "slug": "agents-a1-4b",
      "name": "Agents-A1-4B",
      "vendor": "InternScience (Shanghai AI Laboratory)",
      "vendorUrl": "https://internscience.github.io/Agents-A1/",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/agents-a1-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json",
      "repo": "https://huggingface.co/InternScience/Agents-A1-4B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q8_0-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-F16-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of InternScience's build repositories for the 4B are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/InternScience/Agents-A1-4B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-07-14",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 27,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
        "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "strengths": [
          "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model",
          "FP8 and three GGUF precisions from the publisher",
          "Apache 2.0 through the chain, with no gate",
          "Trained for tool calling, several calls a turn, and 262,144 tokens of context",
          "An evaluation framework for its agent benchmarks is published on GitHub"
        ],
        "weaknesses": [
          "The base isn't declared, and the technical report doesn't cover the 4B",
          "No safety results, agent guidance or security contact",
          "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14",
          "Tool calling and structured output aren't documented beyond a parser name",
          "InternScience answered one of three discussions, and the history was squashed"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector",
          "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work",
          "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14",
          "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them",
          "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "provenanceScore": 40,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/agents-a1-4b"
      },
      "weights": {
        "trainedBy": "InternScience (Shanghai AI Laboratory)",
        "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B",
        "params": "4.5B",
        "context": 262144,
        "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Agents-A1-4B-Q8_0",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 5.2,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B-Q4_K_M",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 3.4,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "2.7 GB of weights and the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "Agents-A1-4B-FP8",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 7.8,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Agents-A1-4B-F16",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          }
        ],
        "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Agents-A1-4B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "InternScience (Shanghai AI Laboratory)",
        "family": "Agents-A1",
        "url": "https://www.anchorterminal.com/open-weights/#internscience",
        "others": [
          {
            "name": "Agents-A1",
            "url": "https://huggingface.co/InternScience/Agents-A1",
            "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)"
          }
        ],
        "olderCount": 6,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template",
        "domain": "internscience.github.io",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.",
          "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.",
          "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)."
        ],
        "score": 40
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json"
    },
    "answer": "Agents-A1-4B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on documentation and upkeep.",
    "b": {
      "slug": "minicpm-o-4-5",
      "name": "MiniCPM-o 4.5",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-o 4.5 is OpenBMB's open-weight omni-modal model of 9.4B parameters that takes images, video, audio and text and answers in text and speech, including full-duplex live conversation and voice cloning from a reference clip, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-o-4-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json",
      "repo": "https://huggingface.co/openbmb/MiniCPM-o-4_5",
      "license": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-awq"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-GPTQ"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-o4.5"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of OpenBMB's four repositories for the model is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB also serves it as MiniCPM-O-4.5-9B on its own OpenAI-compatible API at api.modelbest.cn, with a key from platform.modelbest.cn, which isn't graded here. Running it yourself costs the hardware, 20.0 GB for the BF16 repository and 5.9 GB for the Q4_0 GGUF with its vision file (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://github.com/OpenSQZ/MiniCPM-V-CookBook",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "omni",
        "vision",
        "speech",
        "voice-cloning",
        "full-duplex",
        "reasoning",
        "apache-2.0",
        "safetensors",
        "gguf",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-10-16",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 35,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 35,
          "reach": 90,
          "risk": 40,
          "upkeep": 66
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.",
        "bestFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.",
        "strengths": [
          "OpenBMB's 4-bit GGUF and Ollama builds fit in about 7 GB",
          "Images, video, audio and text in, text and streaming speech out, with full-duplex live conversation",
          "Plain Apache 2.0, no gate, and OpenBMB's own API serves it too",
          "Released vLLM, llama.cpp and Ollama run it, and OpenBMB publishes GGUF, AWQ and GPTQ builds",
          "Repository changed 5 days ago and earlier versions stay in their own repositories"
        ],
        "weaknesses": [
          "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni",
          "Two pickle files (flow.pt and hift.pt) in the speech stage",
          "No safety results or guidance, though it clones voices from a short clip",
          "Tool calling, structured output and where the reasoning comes back aren't documented",
          "40,960 tokens of context, and OpenBMB answered only half the questions in the discussions"
        ],
        "agentNotes": [
          "Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB",
          "With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does",
          "vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni",
          "In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off",
          "Keep prompts within 40,960 tokens",
          "Don't rely on tool calls or JSON output, neither is documented; validate any you get"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 35,
          "reach": 90,
          "risk": 40,
          "upkeep": 66
        },
        "provenanceScore": 53,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run openbmb/minicpm-o4.5\n# or, with vLLM 0.16.0 or later (text out):\nvllm serve openbmb/MiniCPM-o-4_5 --trust-remote-code --dtype auto \\\n  --max-model-len 4096 --max-num-batched-tokens 2048",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"openbmb/MiniCPM-o-4_5\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/photo.jpg\"}}, {\"type\": \"text\", \"text\": \"What is in this picture?\"}]}], \"stop_token_ids\": [151643, 151645]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-o-4-5"
      },
      "sameCompany": [
        "minicpm5-2b"
      ],
      "weights": {
        "trainedBy": "OpenBMB",
        "builtOn": "Qwen3-8B with SigLIP2, Whisper-medium and CosyVoice2 (named on the card)",
        "params": "9.4B",
        "context": 40960,
        "goodFor": "Omni-modal assistant for images, video, speech and text in, text and speech out, with live full-duplex conversation and voice cloning, small enough for a laptop at 4-bit.",
        "runtimes": [
          "vLLM",
          "llama.cpp",
          "Ollama"
        ],
        "builds": [
          {
            "name": "MiniCPM-o-4_5-gguf Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 6.12,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "5.03 GB language file plus the 1.10 GB vision file, the pair OpenBMB's llama.cpp guide uses. The audio, TTS and token2wav GGUF files (2.75 GB more) are for OpenBMB's llama.cpp-omni"
          },
          {
            "name": "MiniCPM-o-4_5-gguf Q4_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 5.87,
            "memoryGB": 8,
            "format": "gguf",
            "note": "4.77 GB language file plus the 1.10 GB vision file, the smallest 4-bit pair"
          },
          {
            "name": "ollama openbmb/minicpm-o4.5:latest",
            "url": "https://ollama.com/openbmb/minicpm-o4.5/tags",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 6.1,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's, text and image input only, 40K context. q4_0 is 5.9 GB. Size as Ollama's tags page shows it"
          },
          {
            "name": "MiniCPM-o-4_5-gguf Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 9.8,
            "memoryGB": 12,
            "format": "gguf",
            "note": "8.71 GB language file plus the 1.10 GB vision file"
          },
          {
            "name": "MiniCPM-o-4_5-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-GPTQ",
            "who": "publisher",
            "precision": "gptq-int4",
            "sizeGB": 9.77,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "4-bit GPTQ, group size 128. Remote code. vLLM's quantisation table marks GPTQ unsupported on AMD GPUs"
          },
          {
            "name": "MiniCPM-o-4_5-awq",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-awq",
            "who": "publisher",
            "precision": "awq-int4",
            "sizeGB": 13.07,
            "memoryGB": 16,
            "format": "safetensors",
            "note": "4-bit AWQ language model with the vision, audio and TTS modules left unquantised, plus a bundled CosyVoice2-0.5B folder. Remote code"
          },
          {
            "name": "MiniCPM-o-4_5-gguf F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 17.48,
            "memoryGB": 24,
            "format": "gguf",
            "note": "16.38 GB language file plus MiniCPM-o-4_5-vision-F16.gguf (1.10 GB) for image input"
          },
          {
            "name": "MiniCPM-o-4_5",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 20.05,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers (remote code)"
            ],
            "note": "Size is the Hub's file list summed, 18.7 GB of safetensors plus the token2wav stage, example media and code. Speech output needs the remote code with Transformers 4.51.0"
          }
        ],
        "run": "vllm serve openbmb/MiniCPM-o-4_5 --dtype auto --trust-remote-code --max-model-len 4096 --max-num-batched-tokens 2048",
        "runBuild": "MiniCPM-o-4_5",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-o 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "olderCount": 22
      },
      "alsoIn": [
        "voice-agents"
      ],
      "area": "models",
      "provenance": {
        "legalEntity": "OpenBMB (named as the MiniCPM-o team on the technical report; the GitHub LICENSE is the unfilled Apache template)",
        "domain": "openbmb.cn",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card links OpenBMB's GitHub repository, whose README links back to this Hugging Face repository. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.",
          "The listing is the weights, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. OpenBMB's API at api.modelbest.cn has its own terms, not read.",
          "The changelog is the repository's commit history, whose messages are mostly README updates, with code fixes on 4 March and 6 July 2026. OpenBMB's GitHub README carries dated news entries (release on 3 February 2026, technical report on 7 May, API on 17 May).",
          "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and OpenBMB/MiniCPM-o has no security policy."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "InternScience (Shanghai AI Laboratory)",
        "b": "OpenBMB",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-14",
        "b": "2026-10-16",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3/5 (4)",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Agents-A1-4B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Agents-A1-4B or MiniCPM-o 4.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 55 against 40",
          "Agent interface, 70 against 35"
        ],
        "also": null,
        "goodFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "slug": "agents-a1-4b",
        "watchFor": "The base isn't declared, and the technical report doesn't cover the 4B"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 66 against 43"
        ],
        "also": null,
        "goodFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.",
        "slug": "minicpm-o-4-5",
        "watchFor": "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.json",
        "title": "Agents-A1-4B vs Apertus v1.5 8B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.json",
        "title": "Agents-A1-4B vs Apodex-1.1-mini",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json",
        "title": "Agents-A1-4B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.json",
        "title": "Agents-A1-4B vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.json",
        "title": "Agents-A1-4B vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.json",
        "title": "Agents-A1-4B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.json",
        "title": "Agents-A1-4B vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.json",
        "title": "Agents-A1-4B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Agents-A1-4B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.json",
        "title": "Agents-A1-4B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.json",
        "title": "Agents-A1-4B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.json",
        "title": "Agents-A1-4B vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.json",
        "title": "Agents-A1-4B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b.json",
        "title": "Agents-A1-4B vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b.json",
        "title": "Agents-A1-4B vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.json",
        "title": "Agents-A1-4B vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.json",
        "title": "Agents-A1-4B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.json",
        "title": "Agents-A1-4B vs Inkling",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.json",
        "title": "Agents-A1-4B vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.json",
        "title": "Agents-A1-4B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.json",
        "title": "Agents-A1-4B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.json",
        "title": "Agents-A1-4B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.json",
        "title": "Agents-A1-4B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.json",
        "title": "Agents-A1-4B vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.json",
        "title": "Agents-A1-4B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.json",
        "title": "Agents-A1-4B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Agents-A1-4B vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm5-2b.json",
        "title": "Agents-A1-4B vs MiniCPM5-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.json",
        "title": "Agents-A1-4B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.json",
        "title": "Agents-A1-4B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b.json",
        "title": "Agents-A1-4B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.json",
        "title": "Agents-A1-4B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think.json",
        "title": "Agents-A1-4B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Agents-A1-4B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-4b-instruct-2507.json",
        "title": "Agents-A1-4B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-0-8b.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-397b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-4b-instruct-2507.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-4b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-27b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniCPM-o 4.5 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b.json",
        "title": "MiniCPM-o 4.5 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.json",
        "title": "MiniCPM-o 4.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "agents-a1-4b": 90,
        "by": 0,
        "edge": "",
        "key": "reach",
        "minicpm-o-4-5": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "agents-a1-4b": 55,
        "by": 15,
        "edge": "agents-a1-4b",
        "key": "risk",
        "minicpm-o-4-5": 40,
        "name": "Risk",
        "weight": 20
      },
      {
        "agents-a1-4b": 70,
        "by": 35,
        "edge": "agents-a1-4b",
        "key": "interface",
        "minicpm-o-4-5": 35,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "agents-a1-4b": 43,
        "by": 23,
        "edge": "minicpm-o-4-5",
        "key": "upkeep",
        "minicpm-o-4-5": 66,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Agents-A1-4B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
      "minicpm-o-4-5": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5",
    "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.min.md"
  },
  "markdown": "Agents-A1-4B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #34 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json\n- MiniCPM-o 4.5: grade C, 61/100, rank #42 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-o-4-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md\n\n## Which one, for what\n\n### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Risk, 55 against 40\n- Agent interface, 70 against 35\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\n\n### MiniCPM-o 4.5 (C)\n\nGood for: Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.\n\nAhead on:\n- Documentation and upkeep, 66 against 43\n\nWatch for: Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni\n\n\n## Score by category\n\n| Category | Weight | Agents-A1-4B | MiniCPM-o 4.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 55 | 40 | Agents-A1-4B +15 |\n| Agent interface | 10% (10 this run) | 70 | 35 | Agents-A1-4B +35 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 66 | MiniCPM-o 4.5 +23 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | MiniCPM-o 4.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | OpenBMB |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\n\n**MiniCPM-o 4.5.** A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.\n\n## Before you call either\n\n### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. Validate JSON output yourself, since structured output isn't documented\n\n### MiniCPM-o 4.5\n\n1. Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB\n2. With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does\n3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni\n4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off\n5. Keep prompts within 40,960 tokens\n6. Don't rely on tool calls or JSON output, neither is documented; validate any you get\n\n## Questions\n\n### Which is better for AI agents, Agents-A1-4B or MiniCPM-o 4.5?\n\nAgents-A1-4B and MiniCPM-o 4.5 score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-o 4.5 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"minicpm-o-4-5\"}`. From a terminal: `anchor compare agents-a1-4b minicpm-o-4-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json\n\n## Other comparisons with Agents-A1-4B or MiniCPM-o 4.5\n\n- [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md)\n- [Agents-A1-4B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md)\n- [Agents-A1-4B vs BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md)\n- [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.md)\n- [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md)\n- [Agents-A1-4B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.md)\n- [Agents-A1-4B vs DeepSeek V4.1 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