{
  "data": {
    "a": {
      "slug": "minicpm-v-4-7-35b-a3b",
      "name": "MiniCPM-V 4.7 35B-A3B",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-V 4.7 35B-A3B is OpenBMB's open-weight vision-language model, a 35B mixture of experts with about 3B active per token that reads images and video, calls tools, and ships in BF16, FP8, 4-bit and GGUF builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json",
      "dns": "minicpm-v-4-7-35b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-V-4.7-35B-A3B-int4"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-v4.7:35b-a3b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, FP8, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, about 70 GB for the BF16 files, 22.7 GB for the 4-bit safetensors builds or 18.7 GB for the IQ4_XS 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/openbmb/MiniCPM-V-4.7-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "video",
        "on-device",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-11-03",
      "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": 1520,
        "categoryRank": 61,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 68
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.",
        "bestFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence",
        "strengths": [
          "Fits about 24 GB from OpenBMB's IQ4_XS GGUF and 32 GB from its Q4_K_M, 4-bit safetensors or Ollama builds",
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions, with llama.cpp's own guide",
          "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code",
          "OpenBMB answered 8 of 12 discussions and dates its releases",
          "Images, several images and video in one model, with thinking off by default"
        ],
        "weaknesses": [
          "No limitations section, safety results, agent-use guidance or security contact",
          "Training data isn't described, and the card gives no context length, languages or knowledge cut-off",
          "The card shows a tool-calling request but not the reply, and names no parser",
          "OpenBMB's cookbook is behind the runtimes on SGLang and Ollama support",
          "No inference provider is mapped on Hugging Face"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`",
          "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost",
          "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace",
          "With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`",
          "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends",
          "Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "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": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 68
        },
        "provenanceScore": 58,
        "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": "ollama run openbmb/minicpm-v4.7:35b-a3b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-35B-A3B --port 8000 --host 0.0.0.0",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"openbmb/minicpm-v4.7:35b-a3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-v-4-7-35b-a3b"
      },
      "sameCompany": [
        "minicpm-v-4-7-1b",
        "minicpm-o-4-5",
        "minicpm5-2b"
      ],
      "weights": {
        "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)",
        "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_moe_text with 40 layers and 256 experts",
        "params": "35.2B (3B active)",
        "context": 262144,
        "goodFor": "Vision-language mixture-of-experts model for images, several images and video, with tool calling and thinking off by default, fast for its size with about 3B active parameters.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniCPM-V-4_7-35B-A3B-IQ4_XS",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 18.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "The smallest file at 4 bits. IQ3_M and IQ3_XS (15.4 and 14.5 GB) are below the 4-bit line and are not listed"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-AWQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-AWQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "AWQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ, 4-bit weights with 16-bit activations"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-int4",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 22.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "Listed on the card with the FP8 build under llm-compressor's FP8 and INT4 guide"
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q5_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 24.7,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.2,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-model-f16.gguf (1.1 GB) or mmproj-model-f16-4x.gguf (0.9 GB) for image input. The same file with thinking on is in the thinking/ folder"
          },
          {
            "name": "ollama openbmb/minicpm-v4.7:35b-a3b",
            "url": "https://ollama.com/openbmb/minicpm-v4.7",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "OpenBMB's own namespace on Ollama, not Ollama's library. Q4_K_M, 22 GB as the tags page shows it, with q8_0 (38 GB) and fp16 (70 GB) tags beside it"
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B-FP8",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 38,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-Q6_K",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 28.5,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "MiniCPM-V-4.7-35B-A3B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 70.4,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "MiniCPM-V-4_7-35B-A3B-F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-F16.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 69.4,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja",
        "runBuild": "MiniCPM-V-4_7-35B-A3B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-V 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "others": [
          {
            "name": "MiniCPM-V 4.7 1B",
            "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B",
            "params": "1.55B (1,554,476,272 per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b"
          }
        ],
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)",
        "domain": "openbmb.cn",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/OpenBMB/MiniCPM-V",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.",
          "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 GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.",
          "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.",
          "unchecked: the domain's registration date."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.json",
      "live": {
        "slug": "minicpm-v-4-7-35b-a3b",
        "securityTxt": {
          "url": "https://openbmb.cn/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-11T16:12:11.187770635Z"
        },
        "updatedAt": "2026-11-11T16:12:11.187770635Z"
      }
    },
    "answer": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk.",
    "b": {
      "slug": "olmo-3-7b-think",
      "name": "Olmo 3 7B Think",
      "vendor": "Ai2 (Allen Institute for AI)",
      "vendorUrl": "https://allenai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Olmo 3 7B Think is Ai2's fully open 7B reasoning model for maths, code and step-by-step answers, with training data, code and checkpoints published, a 65,536-token context and thinking always on, released in November 2025 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/olmo-3-7b-think",
      "markdownUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json",
      "dns": "olmo-3-7b-think.grade.anchorterminal.com",
      "repo": "https://huggingface.co/allenai/Olmo-3-7B-Think",
      "license": "Apache-2.0, with Ai2's Responsible Use Guidelines named on the card",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "allenai/Olmo-3-7B-Think"
        },
        {
          "registry": "ollama",
          "name": "olmo-3:7b-think"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says it is intended for research and educational use in line with Ai2's Responsible Use Guidelines, which list disallowed uses outside research. Running it yourself costs the hardware, 14.6 GB in BF16 and 4.5 GB for Ollama's 4-bit build (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/allenai/Olmo-3-7B-Think",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "reasoning",
        "fully-open",
        "open-data",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-06-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.3,
        "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": 1520,
        "categoryRank": 103,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 28,
          "interface": 35,
          "reach": 75,
          "risk": 75,
          "upkeep": 52
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.",
        "bestFor": "Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand",
        "strengths": [
          "Training data, code, checkpoints and evaluation harness all published, the data with sources and sizes",
          "Safety results published with a harness to rerun them",
          "Ollama's 4-bit build fits in about 5.4 GB, and released llama.cpp, Ollama, vLLM, LM Studio and Transformers run it",
          "Safetensors only with no repository code, a checksum on every file and no gate",
          "Apache 2.0 with no revenue threshold"
        ],
        "weaknesses": [
          "Not trained for tool calling, and the tool format lives only in the template",
          "Thinking is always on with no way to turn it off, and 65,536 tokens of context",
          "Ai2 publishes no quantised build and no provider hosts it",
          "The card binds use to Ai2's Responsible Use Guidelines, which Ai2 can change",
          "No Ai2 reply in the discussions, and the template's default prompt contradicts the card"
        ],
        "agentNotes": [
          "Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB",
          "With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend",
          "Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long",
          "Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags",
          "Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available",
          "For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling",
          "A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens",
          "Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.3
          }
        ],
        "editorialScores": {
          "capability": 28,
          "interface": 35,
          "reach": 75,
          "risk": 75,
          "upkeep": 52
        },
        "provenanceScore": 70,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run olmo-3:7b-think\n# or\nvllm serve allenai/Olmo-3-7B-Think --max-model-len 65536",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"olmo-3:7b-think\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"temperature\": 0.6, \"top_p\": 0.95}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/olmo-3-7b-think"
      },
      "weights": {
        "trainedBy": "Ai2 (Allen Institute for AI)",
        "builtOn": "allenai/Olmo-3-7B-Think-DPO",
        "params": "7.3B",
        "context": 65536,
        "goodFor": "Fully open 7B reasoning model for maths, code and step-by-step answers in English, with published training data and checkpoints for research, always thinking, not trained for tool calls.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama olmo-3:7b-think-q4_K_M",
            "url": "https://ollama.com/library/olmo-3:7b-think-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 4.5,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Digest b8d4c92ac9c1, also the 7b-think and latest tags. q8_0 is 7.8 GB and fp16 15 GB. Sizes as Ollama's tags page shows them"
          },
          {
            "name": "LM Studio allenai/olmo-3-7b-think (Q4_K_M GGUF)",
            "url": "https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 4.5,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "Olmo-3-7B-Think-Q4_K_M.gguf, 4.47 GB from the Hub's file list. The catalogue entry also lists Q6_K, Q8_0 and MLX 4, 6 and 8-bit builds; the MLX 4-bit is 4.12 GB (Mac only)"
          },
          {
            "name": "Olmo-3-7B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 14.6,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536",
        "runBuild": "Olmo-3-7B-Think",
        "checked": "2026-11-04",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Ai2 (Allen Institute for AI)",
        "family": "Olmo 3 (3, 3.1)",
        "url": "https://www.anchorterminal.com/open-weights/#ai2",
        "others": [
          {
            "name": "Olmo-3.1-7B-RL-Zero-Math",
            "url": "https://huggingface.co/allenai/Olmo-3.1-7B-RL-Zero-Math",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3.1-7B-RL-Zero-Code",
            "url": "https://huggingface.co/allenai/Olmo-3.1-7B-RL-Zero-Code",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3.1-32B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Think",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Mix",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Mix",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct-DPO",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-32B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct-SFT",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct-DPO",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-Think-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think-DPO",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Math",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Math",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-IF",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-IF",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-General",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-General",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Code",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Code",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-32B-Think-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think-SFT",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-32B-Think-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think-DPO",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-1125-32B",
            "url": "https://huggingface.co/allenai/Olmo-3-1125-32B",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-Think-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think-SFT",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-1025-7B",
            "url": "https://huggingface.co/allenai/Olmo-3-1025-7B",
            "params": "7B (Hub safetensors count 7.30B)"
          }
        ],
        "olderCount": 95,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "The Allen Institute for Artificial Intelligence (Ai2), a 501(c)(3) nonprofit, per its Responsible Use Guidelines",
        "domain": "allenai.org",
        "domainRegistered": "2010-12-03",
        "endpointOnVendorDomain": null,
        "terms": "https://allenai.org/terms",
        "privacy": "https://allenai.org/privacy-policy",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "expired",
        "checked": "2026-10-21",
        "notes": [
          "RDAP for allenai.org gives a registration date of 3 December 2010.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. Ai2's guidelines say its website terms also govern use of its tools.",
          "allenai.org serves a security.txt with a contact and an Expires date of 31 July 2026, which has passed. The Responsible Use Guidelines name a channel for bugs and security concerns with Ai2's models.",
          "No changelog. The commits are card edits, two configuration fixes from pull requests in November and December 2025, and a parameter-count fix on 25 June 2026.",
          "allenai.org links to huggingface.co/allenai, so the repository is the publisher's own."
        ],
        "score": 70
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.json",
      "live": {
        "slug": "olmo-3-7b-think",
        "securityTxt": {
          "url": "https://allenai.org/.well-known/security.txt",
          "state": "expired",
          "expires": "2026-07-31T05:00:00.000Z",
          "checkedAt": "2026-11-11T16:11:07.429604508Z"
        },
        "domain": {
          "domain": "allenai.org",
          "registered": "2010-12-03",
          "source": "https://rdap.publicinterestregistry.org/rdap/domain/allenai.org",
          "checkedAt": "2026-11-08T15:03:50.561358192Z"
        },
        "pages": [
          {
            "url": "https://allenai.org/privacy-policy",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-11-11T16:50:20.356542714Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "6575e6dd3713"
          },
          {
            "url": "https://allenai.org/terms",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-11-11T16:50:23.72630181Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "fde9cf039435"
          }
        ],
        "updatedAt": "2026-11-11T16:50:23.72630181Z"
      }
    },
    "edges": [
      {
        "by": 35,
        "category": "interface",
        "leads": "minicpm-v-4-7-35b-a3b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.",
          "b": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35."
        },
        "scores": {
          "a": 70,
          "b": 35
        }
      },
      {
        "by": 20,
        "category": "risk",
        "leads": "olmo-3-7b-think",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.",
          "b": "File safety earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75."
        },
        "scores": {
          "a": 55,
          "b": 75
        }
      },
      {
        "by": 16,
        "category": "upkeep",
        "leads": "minicpm-v-4-7-35b-a3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.",
          "b": "The model card earns 17, missing only the context length. Training data earns the full 20, as every dataset is released with sources and sizes. The last change earns 10, a card fix on 25 June 2026, within 180 days. Responsiveness earns nothing, with no Ai2 reply among the 4 replies in the discussions. Version policy earns 5, with earlier and later models in their own repositories and no dated notes. File health earns nothing, because the template's default prompt contradicts the card's on the cut-off and a stray swap file and an unanswered report sit in the repository. 17 + 20 + 10 + 0 + 5 + 0 = 52."
        },
        "scores": {
          "a": 68,
          "b": 52
        }
      },
      {
        "by": 15,
        "category": "reach",
        "leads": "minicpm-v-4-7-35b-a3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.",
          "b": "Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75."
        },
        "scores": {
          "a": 90,
          "b": 75
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "OpenBMB",
        "b": "Ai2 (Allen Institute for AI)",
        "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, with Ai2's Responsible Use Guidelines named on the card",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-03",
        "b": "2026-06-25",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "2024-09-25",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "2025-02-19",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not read yet",
        "b": "yes",
        "name": "Customer content may train models"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "not read yet",
        "b": "yes",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "none",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk.",
        "question": "Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Olmo 3 7B Think?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 75",
          "Agent interface, 70 against 35",
          "Documentation and upkeep, 68 against 52"
        ],
        "also": null,
        "goodFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence",
        "slug": "minicpm-v-4-7-35b-a3b",
        "watchFor": "No limitations section, safety results, agent-use guidance or security contact"
      },
      {
        "aheadOn": [
          "Risk, 75 against 55"
        ],
        "also": null,
        "goodFor": "Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand",
        "slug": "olmo-3-7b-think",
        "watchFor": "Not trained for tool calling, and the tool format lives only in the template"
      }
    ],
    "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-v-4-7-35b-a3b.json",
        "title": "Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "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/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think.json",
        "title": "Apertus v1.5 8B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Apodex-1.1-mini vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think.json",
        "title": "Apodex-1.1-mini vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Arete-27B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-olmo-3-7b-think.json",
        "title": "Arete-27B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-olmo-3-7b-think"
      },
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next.json",
        "title": "Olmo 3 7B Think vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-next.json",
        "title": "Olmo 3 7B Think vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Olmo 3 7B Think vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-recursive-flint.json",
        "title": "Olmo 3 7B Think vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ripple-65b-preview.json",
        "title": "Olmo 3 7B Think vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-sensenova-loop-9b-preview.json",
        "title": "Olmo 3 7B Think vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.json",
        "title": "Olmo 3 7B Think vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b.json",
        "title": "Olmo 3 7B Think vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-step-3-7-flash.json",
        "title": "Olmo 3 7B Think vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2.json",
        "title": "Olmo 3 7B Think vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b.json",
        "title": "Olmo 3 7B Think vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b.json",
        "title": "Olmo 3 7B Think vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-zaya1-8b.json",
        "title": "Olmo 3 7B Think vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "minicpm-v-4-7-35b-a3b",
        "key": "reach",
        "minicpm-v-4-7-35b-a3b": 90,
        "name": "Reach",
        "olmo-3-7b-think": 75,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "olmo-3-7b-think",
        "key": "risk",
        "minicpm-v-4-7-35b-a3b": 55,
        "name": "Risk",
        "olmo-3-7b-think": 75,
        "weight": 20
      },
      {
        "by": 35,
        "edge": "minicpm-v-4-7-35b-a3b",
        "key": "interface",
        "minicpm-v-4-7-35b-a3b": 70,
        "name": "Agent interface",
        "olmo-3-7b-think": 35,
        "weight": 10
      },
      {
        "by": 16,
        "edge": "minicpm-v-4-7-35b-a3b",
        "key": "upkeep",
        "minicpm-v-4-7-35b-a3b": 68,
        "name": "Documentation and upkeep",
        "olmo-3-7b-think": 52,
        "weight": 10
      }
    ],
    "summary": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "minicpm-v-4-7-35b-a3b": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.",
      "olmo-3-7b-think": "A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-olmo-3-7b-think",
    "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-olmo-3-7b-think.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-olmo-3-7b-think.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-olmo-3-7b-think.min.md"
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  "markdown": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk. Both do open-weight models you run yourself.\n\n- MiniCPM-V 4.7 35B-A3B: grade C, 61/100, rank #77 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json\n- Olmo 3 7B Think: grade D, 52.3/100, rank #139 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/olmo-3-7b-think.md · JSON https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.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\n## Which one, for what\n\n### MiniCPM-V 4.7 35B-A3B (C)\n\nGood for: Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence\n\nAhead on:\n- Reach, 90 against 75\n- Agent interface, 70 against 35\n- Documentation and upkeep, 68 against 52\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n### Olmo 3 7B Think (D)\n\nGood for: Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand\n\nAhead on:\n- Risk, 75 against 55\n\nWatch for: Not trained for tool calling, and the tool format lives only in the template\n\n\n## Why the edge\n\n### Agent interface: MiniCPM-V 4.7 35B-A3B +35\n\n- MiniCPM-V 4.7 35B-A3B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.\n- Olmo 3 7B Think (35): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35.\n\n### Risk: Olmo 3 7B Think +20\n\n- MiniCPM-V 4.7 35B-A3B (55): File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.\n- Olmo 3 7B Think (75): File safety earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75.\n\n### Documentation and upkeep: MiniCPM-V 4.7 35B-A3B +16\n\n- MiniCPM-V 4.7 35B-A3B (68): Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.\n- Olmo 3 7B Think (52): The model card earns 17, missing only the context length. Training data earns the full 20, as every dataset is released with sources and sizes. The last change earns 10, a card fix on 25 June 2026, within 180 days. Responsiveness earns nothing, with no Ai2 reply among the 4 replies in the discussions. Version policy earns 5, with earlier and later models in their own repositories and no dated notes. File health earns nothing, because the template's default prompt contradicts the card's on the cut-off and a stray swap file and an unanswered report sit in the repository. 17 + 20 + 10 + 0 + 5 + 0 = 52.\n\n### Reach: MiniCPM-V 4.7 35B-A3B +15\n\n- MiniCPM-V 4.7 35B-A3B (90): Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n- Olmo 3 7B Think (75): Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 35B-A3B | Olmo 3 7B Think | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 75 | MiniCPM-V 4.7 35B-A3B +15 |\n| Risk | 20% (20 this run) | 55 | 75 | Olmo 3 7B Think +20 |\n| Agent interface | 10% (10 this run) | 70 | 35 | MiniCPM-V 4.7 35B-A3B +35 |\n| Documentation and upkeep | 10% (10 this run) | 68 | 52 | MiniCPM-V 4.7 35B-A3B +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 35B-A3B | Olmo 3 7B Think |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | Ai2 (Allen Institute for AI) |\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, with Ai2's Responsible Use Guidelines named on the card |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-03 | 2026-06-25 |\n| Terms last updated | no document linked | 2024-09-25 |\n| Privacy policy last updated | no document linked | 2025-02-19 |\n| Customer content may train models | not read yet | yes |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | yes |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**MiniCPM-V 4.7 35B-A3B.** A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.\n\n**Olmo 3 7B Think.** A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.\n\n## Before you call either\n\n### MiniCPM-V 4.7 35B-A3B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`\n5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format\n7. Validate JSON output yourself, since structured output isn't documented\n\n### Olmo 3 7B Think\n\n1. Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB\n2. With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend\n3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long\n4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags\n5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available\n6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling\n7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens\n8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Olmo 3 7B Think?\n\nMiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-olmo-3-7b-think.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-35b-a3b\", \"b\": \"olmo-3-7b-think\"}`. From a terminal: `anchor compare minicpm-v-4-7-35b-a3b olmo-3-7b-think`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json and https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json\n\n## Other comparisons with MiniCPM-V 4.7 35B-A3B or Olmo 3 7B Think\n\n- [Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Agents-A1-4B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think.md)\n- [Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Apertus v1.5 8B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think.md)\n- [Apodex-1.1-mini vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b.md)\n- [Apodex-1.1-mini vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think.md)\n- [Arete-27B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Arete-27B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/arete-27b-vs-olmo-3-7b-think.md)\n- [BigBang-v1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b.md)\n- [BigBang-v1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/bigbang-v1-vs-olmo-3-7b-think.md)\n- [Carbon-X vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b.md)\n- [Carbon-X vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/carbon-x-vs-olmo-3-7b-think.md)\n- [Cola2 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b.md)\n- [Cola2 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/cola2-vs-olmo-3-7b-think.md)\n- [Cosmos-Reason2-2B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Cosmos-Reason2-2B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-olmo-3-7b-think.md)\n- [Darwin-36B-Opus vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-35b-a3b.md)\n- [Darwin-36B-Opus vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-olmo-3-7b-think.md)\n- [DeepSeek-V3.2 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-35b-a3b.md)\n- [DeepSeek-V3.2 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-olmo-3-7b-think.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think.md)\n- [DeepSeek-V4-Flash-0731 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vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think.md)\n- [Kimi K3 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-35b-a3b.md)\n- [Kimi K3 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kimi-k3-vs-olmo-3-7b-think.md)\n- [Kolibri-1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-35b-a3b.md)\n- [Kolibri-1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kolibri-1-vs-olmo-3-7b-think.md)\n- [Kronos vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-35b-a3b.md)\n- [Kronos vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kronos-vs-olmo-3-7b-think.md)\n- [Laguna M.1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-v-4-7-35b-a3b.md)\n- [Laguna M.1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/laguna-m-1-vs-olmo-3-7b-think.md)\n- [Laguna S 2.1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-35b-a3b.md)\n- [Laguna S 2.1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-olmo-3-7b-think.md)\n- [Laguna XS 2.1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-v-4-7-35b-a3b.md)\n- [Laguna XS 2.1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-olmo-3-7b-think.md)\n- [LBS-27B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LBS-27B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lbs-27b-vs-olmo-3-7b-think.md)\n- [LFM2.5-2.6B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM2.5-2.6B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.md)\n- [LFM3-1.7B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM3-1.7B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-olmo-3-7b-think.md)\n- [LFM3-Audio-2B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM3-Audio-2B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-olmo-3-7b-think.md)\n- [LFM3-Omni-2B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM3-Omni-2B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-olmo-3-7b-think.md)\n- [LFM3-VL-2B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM3-VL-2B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think.md)\n- [Ling-3.0-tiny vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-v-4-7-35b-a3b.md)\n- [Ling-3.0-tiny vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think.md)\n- [Ling-3.1-flash-1019 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minicpm-v-4-7-35b-a3b.md)\n- [Ling-3.1-flash-1019 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-olmo-3-7b-think.md)\n- [LongCat-2.5 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.md)\n- [LongCat-2.5 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/longcat-2-5-vs-olmo-3-7b-think.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-v-4-7-35b-a3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-olmo-3-7b-think.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-35b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-olmo-3-7b-think.md)\n- [MiniCPM-o 4.5 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minicpm-v-4-7-35b-a3b.md)\n- [MiniCPM-o 4.5 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-olmo-3-7b-think.md)\n- [MiniCPM-V 4.7 1B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-minicpm-v-4-7-35b-a3b.md)\n- [MiniCPM-V 4.7 1B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-olmo-3-7b-think.md)\n- [MiniCPM-V 4.7 35B-A3B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-minicpm5-2b.md)\n- [MiniCPM-V 4.7 35B-A3B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-minimax-m2-5.md)\n- 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