{
  "data": {
    "a": {
      "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",
      "dns": "minicpm-o-4-5.grade.anchorterminal.com",
      "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": "2026-11-03"
      },
      "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": "2026-10-24",
      "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": 1518,
        "categoryRank": 38,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 35,
          "reach": 90,
          "risk": 40,
          "upkeep": 66
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 35,
          "reach": 90,
          "risk": 40,
          "upkeep": 66
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "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": [
        "minicpm-v-4-7-1b",
        "minicpm-v-4-7-35b-a3b",
        "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": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json",
      "live": {
        "slug": "minicpm-o-4-5",
        "securityTxt": {
          "url": "https://openbmb.cn/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:10.170465559Z"
        },
        "domain": {
          "domain": "openbmb.cn",
          "checkedAt": "2026-11-08T14:50:03.275690576Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:38.685365772Z",
            "changedAt": "2026-11-10T16:23:38.685365772Z",
            "fingerprint": "0600a4aee37d"
          }
        ],
        "updatedAt": "2026-11-10T16:23:38.685365772Z"
      }
    },
    "answer": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk and agent interface.",
    "b": {
      "slug": "qwen-agentworld-35b-a3b",
      "name": "Qwen-AgentWorld-35B-A3B",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen-AgentWorld-35B-A3B is Alibaba's open-weight language world model, a 35B mixture of experts with 3B active that simulates agent environments by predicting the next observation across seven domains, published in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json",
      "dns": "qwen-agentworld-35b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen-AgentWorld-35B-A3B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a LICENSE file in the repository. Running it yourself costs the hardware, 69.3 GB for the BF16 files, or 22.1 GB for Unsloth's 4-bit GGUF (checked 2026-11-10).",
      "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 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "world-model",
        "agent-simulation",
        "reasoning",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-06-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.1,
        "grade": "D",
        "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": 1518,
        "categoryRank": 104,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 50,
          "reach": 55,
          "risk": 55,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.",
        "bestFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine",
        "strengths": [
          "Simulates seven kinds of agent environment in one model, with the system prompts for each published",
          "Apache 2.0, ungated, safetensors only",
          "Runs in vLLM, SGLang, Transformers and llama.cpp from their releases",
          "Fits a 128 GB machine in BF16, and 32 GB from Unsloth's 4-bit GGUF",
          "A 262,144-token context for long interaction histories"
        ],
        "weaknesses": [
          "Not a general assistant, and the card doesn't describe using it to call tools",
          "No quantised build from Alibaba, so the GGUF route depends on a third party",
          "No reply from Alibaba on any of 10 discussions, and no change to the files since 25 June 2026",
          "No limitations, safety results or security contact on the card",
          "Its results are on Alibaba's own benchmark"
        ],
        "agentNotes": [
          "Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation",
          "With vLLM pass `--language-model-only`, or startup fails on the missing vision weights",
          "Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends",
          "Keep at least 128K of context, since the simulation depends on the history",
          "Thinking is on by default and comes back through the qwen3 reasoning parser",
          "A simulated observation is a prediction. Check anything that matters against the real environment"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.1
          }
        ],
        "editorialScores": {
          "interface": 50,
          "reach": 55,
          "risk": 55,
          "upkeep": 41
        },
        "provenanceScore": 66,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM, as the card gives it (four GPUs)\nvllm serve Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tensor-parallel-size 4 \\\n  --max-model-len 262144 --reasoning-parser qwen3 --language-model-only --trust-remote-code\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"Qwen/Qwen-AgentWorld-35B-A3B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.6}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen-agentworld-35b-a3b"
      },
      "sameCompany": [
        "qwen3-embedding-0-6b",
        "qwen3-embedding-4b",
        "qwen3-embedding-8b",
        "qwen3-vl-embedding-2b",
        "qwen3-vl-embedding-8b",
        "qwen3-asr-0-6b",
        "qwen3-asr-1-7b",
        "qwen3-tts-1-7b-customvoice",
        "qwen3-30b-a3b-instruct-2507",
        "qwen3-4b-instruct-2507",
        "qwen3-coder-30b-a3b-instruct",
        "qwen3-coder-next",
        "qwen3-omni-30b-a3b-instruct",
        "qwen3-vl-2b-instruct",
        "qwen3-vl-30b-a3b-instruct",
        "qwen3-vl-32b-instruct",
        "qwen3-vl-4b-instruct",
        "qwen3-vl-8b-instruct",
        "qwen3-5-0-8b",
        "qwen3-5-122b-a10b",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen)",
        "builtOn": "Qwen3.5-35B-A3B-Base, named on the card and in its metadata",
        "params": "34.7B",
        "active": "3B",
        "context": 262144,
        "goodFor": "A simulator of agent environments (MCP tools, search, terminal, software engineering, Android, web and OS) that predicts the next observation, for training and testing agents without the real environment.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 22.13,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Alibaba's. 22.13 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (Q8_0)",
            "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF",
            "who": "unsloth",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Alibaba's. 36.90 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 69.32,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ],
            "note": "The only build Alibaba publishes. Language weights only"
          }
        ],
        "run": "llama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja",
        "runBuild": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)",
        "checked": "2026-11-10",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Alibaba (Qwen)",
        "family": "Qwen-AgentWorld",
        "url": "https://www.anchorterminal.com/open-weights/#qwen",
        "olderCount": 109
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Alibaba Cloud (named in the licence's copyright line)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The LICENSE file names Alibaba Cloud, as the Qwen3.6-35B-A3B listing records for the organisation.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, nine commits between 22 and 25 June 2026, the upload and updates to the card.",
          "The Qwen3.6-35B-A3B listing found no security.txt on qwen.ai. unchecked on 10 November."
        ],
        "score": 66
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.json"
    },
    "edges": [
      {
        "by": 35,
        "category": "reach",
        "leads": "minicpm-o-4-5",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, OpenBMB's 4-bit GGUF with its vision file is 5.9 GB, about 7 GB with a fifth added. Runtimes 15 of 25, released vLLM, llama.cpp and Ollama run it, while Transformers 5.22.1 still needs OpenBMB's remote code and SGLang 0.5.23 supports only the older 2.6. Builds 15, GGUF at several precisions plus AWQ and GPTQ. Terms 15, plain Apache 2.0. Download 10, nothing gated. Hosted 5, OpenBMB's own API serves it.",
          "b": "Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55."
        },
        "scores": {
          "a": 90,
          "b": 55
        }
      },
      {
        "by": 25,
        "category": "upkeep",
        "leads": "minicpm-o-4-5",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11 of 20, limitations, context length and a knowledge cut-off are missing. Training data 5, described by kind without sources, sizes or a cut-off. Last change 20, the card changed on 24 October 2026. Responsiveness 10, OpenBMB answered half of the 12 questions. Version policy 10, earlier models stay in their own repositories and releases are dated. File health 10, the files agree and the two bug reports have replies.",
          "b": "Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41."
        },
        "scores": {
          "a": 66,
          "b": 41
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "qwen-agentworld-35b-a3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 5 of 25, two pickle files sit in the speech stage and the documented load runs repository code. Provenance 15, the files are in the openbmb organisation OpenBMB's GitHub links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only, nothing about voice cloning misuse, prompt injection or refusals. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt, policy or advisories.",
          "b": "File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55."
        },
        "scores": {
          "a": 40,
          "b": 55
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "qwen-agentworld-35b-a3b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja template in the tokenizer config. Tool-calling format 10 of 25, only the inherited Qwen3 template says how tools work. Trained tool calling 0, OpenBMB never mentions tool use. Structured output 0, not documented. Reasoning control 0, thinking turns on and off per request but nobody says where the reasoning comes back. Context 5, 40,960 tokens. Read as an omni model, it takes images, video and audio and returns text and streaming speech, which the card documents with code.",
          "b": "Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50."
        },
        "scores": {
          "a": 35,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "OpenBMB",
        "b": "Alibaba (Qwen team)",
        "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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-24",
        "b": "2026-06-25",
        "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": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk and agent interface.",
        "question": "Which is better for AI agents, MiniCPM-o 4.5 or Qwen-AgentWorld-35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 55",
          "Documentation and upkeep, 66 against 41"
        ],
        "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"
      },
      {
        "aheadOn": [
          "Risk, 55 against 40",
          "Agent interface, 50 against 35"
        ],
        "also": null,
        "goodFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine",
        "slug": "qwen-agentworld-35b-a3b",
        "watchFor": "Not a general assistant, and the card doesn't describe using it to call tools"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.json",
        "title": "Agents-A1-4B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Agents-A1-4B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.json",
        "title": "Apertus v1.5 8B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Apertus v1.5 8B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.json",
        "title": "Apodex-1.1-mini vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b.json",
        "title": "Apodex-1.1-mini vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-o-4-5.json",
        "title": "Arete-27B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Arete-27B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5.json",
        "title": "BigBang-v1 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-o-4-5.json",
        "title": "Carbon-X vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b.json",
        "title": "Carbon-X vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-o-4-5.json",
        "title": "Cosmos-Reason2-2B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b"
      },
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      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-o-4-5.json",
        "title": "gpt-oss-120b vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b.json",
        "title": "gpt-oss-120b vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-o-4-5.json",
        "title": "gpt-oss-20b vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen-agentworld-35b-a3b.json",
        "title": "gpt-oss-20b vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-o-4-5.json",
        "title": "Granite 4.2 30B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-o-4-5.json",
        "title": "Granite 4.2 3B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Granite 4.2 3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-o-4-5.json",
        "title": "Granite 4.2 8B vs MiniCPM-o 4.5",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-o-4-5.json",
        "title": "Hy-Memory-30B-A3B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b.json",
        "title": "Inkling-Small vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-o-4-5.json",
        "title": "Kimi K3.1 Pro vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kimi K3 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5.json",
        "title": "Kolibri-1 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kronos vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Laguna M.1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5.json",
        "title": "Laguna S 2.1 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-o-4-5.json",
        "title": "Laguna XS 2.1 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5.json",
        "title": "LBS-27B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-o-4-5.json",
        "title": "LFM3-1.7B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-o-4-5.json",
        "title": "LFM3-Audio-2B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-o-4-5.json",
        "title": "LFM3-VL-2B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b"
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        "title": "Ling-3.0-tiny vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minicpm-o-4-5.json",
        "title": "Ling-3.1-flash-1019 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-o-4-5.json",
        "title": "LongCat-2.5 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.json",
        "title": "LongCat-2.5 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-o-4-5.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-o-4-5.json",
        "title": "MiMo-V2.6-Flash-RL vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen-agentworld-35b-a3b.json",
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        "title": "Nanbeige4.3-33B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen-AgentWorld-35B-A3B",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b.json",
        "title": "North Mini Code 1.0 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b.json",
        "title": "occamy-1.0 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b.json",
        "title": "Olmo 3 7B Think vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ornith-1.5-397B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ornith-1.5-9B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-2b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-4b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-27b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "minicpm-o-4-5",
        "key": "reach",
        "minicpm-o-4-5": 90,
        "name": "Reach",
        "qwen-agentworld-35b-a3b": 55,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "qwen-agentworld-35b-a3b",
        "key": "risk",
        "minicpm-o-4-5": 40,
        "name": "Risk",
        "qwen-agentworld-35b-a3b": 55,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "qwen-agentworld-35b-a3b",
        "key": "interface",
        "minicpm-o-4-5": 35,
        "name": "Agent interface",
        "qwen-agentworld-35b-a3b": 50,
        "weight": 10
      },
      {
        "by": 25,
        "edge": "minicpm-o-4-5",
        "key": "upkeep",
        "minicpm-o-4-5": 66,
        "name": "Documentation and upkeep",
        "qwen-agentworld-35b-a3b": 41,
        "weight": 10
      }
    ],
    "summary": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk and agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "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.",
      "qwen-agentworld-35b-a3b": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b",
    "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.min.md"
  },
  "markdown": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- MiniCPM-o 4.5: grade C, 61/100, rank #48 of 160 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- Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.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 6359 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 581 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md\n\n## Which one, for what\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- Reach, 90 against 55\n- Documentation and upkeep, 66 against 41\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### Qwen-AgentWorld-35B-A3B (D)\n\nGood for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine\n\nAhead on:\n- Risk, 55 against 40\n- Agent interface, 50 against 35\n\nWatch for: Not a general assistant, and the card doesn't describe using it to call tools\n\n\n## Why the edge\n\n### Reach: MiniCPM-o 4.5 +35\n\n- MiniCPM-o 4.5 (90): Memory 30, OpenBMB's 4-bit GGUF with its vision file is 5.9 GB, about 7 GB with a fifth added. Runtimes 15 of 25, released vLLM, llama.cpp and Ollama run it, while Transformers 5.22.1 still needs OpenBMB's remote code and SGLang 0.5.23 supports only the older 2.6. Builds 15, GGUF at several precisions plus AWQ and GPTQ. Terms 15, plain Apache 2.0. Download 10, nothing gated. Hosted 5, OpenBMB's own API serves it.\n- Qwen-AgentWorld-35B-A3B (55): Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.\n\n### Documentation and upkeep: MiniCPM-o 4.5 +25\n\n- MiniCPM-o 4.5 (66): Model card 11 of 20, limitations, context length and a knowledge cut-off are missing. Training data 5, described by kind without sources, sizes or a cut-off. Last change 20, the card changed on 24 October 2026. Responsiveness 10, OpenBMB answered half of the 12 questions. Version policy 10, earlier models stay in their own repositories and releases are dated. File health 10, the files agree and the two bug reports have replies.\n- Qwen-AgentWorld-35B-A3B (41): Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.\n\n### Risk: Qwen-AgentWorld-35B-A3B +15\n\n- MiniCPM-o 4.5 (40): File safety 5 of 25, two pickle files sit in the speech stage and the documented load runs repository code. Provenance 15, the files are in the openbmb organisation OpenBMB's GitHub links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only, nothing about voice cloning misuse, prompt injection or refusals. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt, policy or advisories.\n- Qwen-AgentWorld-35B-A3B (55): File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n### Agent interface: Qwen-AgentWorld-35B-A3B +15\n\n- MiniCPM-o 4.5 (35): Chat template 20, a Jinja template in the tokenizer config. Tool-calling format 10 of 25, only the inherited Qwen3 template says how tools work. Trained tool calling 0, OpenBMB never mentions tool use. Structured output 0, not documented. Reasoning control 0, thinking turns on and off per request but nobody says where the reasoning comes back. Context 5, 40,960 tokens. Read as an omni model, it takes images, video and audio and returns text and streaming speech, which the card documents with code.\n- Qwen-AgentWorld-35B-A3B (50): Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.\n\n## Score by category\n\n| Category | Weight | MiniCPM-o 4.5 | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 55 | MiniCPM-o 4.5 +35 |\n| Risk | 20% (20 this run) | 40 | 55 | Qwen-AgentWorld-35B-A3B +15 |\n| Agent interface | 10% (10 this run) | 35 | 50 | Qwen-AgentWorld-35B-A3B +15 |\n| Documentation and upkeep | 10% (10 this run) | 66 | 41 | MiniCPM-o 4.5 +25 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-o 4.5 | Qwen-AgentWorld-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | Alibaba (Qwen team) |\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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-24 | 2026-06-25 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\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**Qwen-AgentWorld-35B-A3B.** A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.\n\n## Before you call either\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### Qwen-AgentWorld-35B-A3B\n\n1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation\n2. With vLLM pass `--language-model-only`, or startup fails on the missing vision weights\n3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends\n4. Keep at least 128K of context, since the simulation depends on the history\n5. Thinking is on by default and comes back through the qwen3 reasoning parser\n6. A simulated observation is a prediction. Check anything that matters against the real environment\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-o 4.5 or Qwen-AgentWorld-35B-A3B?\n\nMiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-o-4-5\", \"b\": \"qwen-agentworld-35b-a3b\"}`. 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