{
  "data": {
    "a": {
      "slug": "agents-a1-4b",
      "name": "Agents-A1-4B",
      "vendor": "InternScience (Shanghai AI Laboratory)",
      "vendorUrl": "https://internscience.github.io/Agents-A1/",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/agents-a1-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json",
      "repo": "https://huggingface.co/InternScience/Agents-A1-4B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q8_0-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-F16-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of InternScience's build repositories for the 4B are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/InternScience/Agents-A1-4B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-07-14",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 27,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
        "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "strengths": [
          "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model",
          "FP8 and three GGUF precisions from the publisher",
          "Apache 2.0 through the chain, with no gate",
          "Trained for tool calling, several calls a turn, and 262,144 tokens of context",
          "An evaluation framework for its agent benchmarks is published on GitHub"
        ],
        "weaknesses": [
          "The base isn't declared, and the technical report doesn't cover the 4B",
          "No safety results, agent guidance or security contact",
          "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14",
          "Tool calling and structured output aren't documented beyond a parser name",
          "InternScience answered one of three discussions, and the history was squashed"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector",
          "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work",
          "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14",
          "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them",
          "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "provenanceScore": 40,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/agents-a1-4b"
      },
      "weights": {
        "trainedBy": "InternScience (Shanghai AI Laboratory)",
        "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B",
        "params": "4.5B",
        "context": 262144,
        "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Agents-A1-4B-Q8_0",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 5.2,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B-Q4_K_M",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 3.4,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "2.7 GB of weights and the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "Agents-A1-4B-FP8",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 7.8,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Agents-A1-4B-F16",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          }
        ],
        "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Agents-A1-4B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "InternScience (Shanghai AI Laboratory)",
        "family": "Agents-A1",
        "url": "https://www.anchorterminal.com/open-weights/#internscience",
        "others": [
          {
            "name": "Agents-A1",
            "url": "https://huggingface.co/InternScience/Agents-A1",
            "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)"
          }
        ],
        "olderCount": 6,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template",
        "domain": "internscience.github.io",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.",
          "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.",
          "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)."
        ],
        "score": 40
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json"
    },
    "answer": "Agents-A1-4B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "internvl3-5-gpt-oss-20b-a4b-preview",
      "name": "InternVL3.5-GPT-OSS-20B-A4B-Preview",
      "vendor": "OpenGVLab (Shanghai AI Laboratory)",
      "vendorUrl": "https://github.com/OpenGVLab",
      "kind": "weights",
      "category": "open-weights",
      "summary": "InternVL3.5-GPT-OSS-20B-A4B-Preview is OpenGVLab's open-weight vision-language preview that joins OpenAI's gpt-oss-20b mixture-of-experts model with the InternViT-300M vision encoder, 21.2B parameters with about 4B active, taking images and video and answering in text, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json",
      "repo": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
      "license": "Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF"
        },
        {
          "registry": "huggingface",
          "name": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the Transformers-format repository nor the GitHub-format one is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with gpt-oss-20b's Apache 2.0 and usage policy and InternViT's MIT licence carried with it. Running it costs the hardware, 42.5 GB of BF16 files in either format, since the gpt-oss experts were expanded from MXFP4 to BF16 (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/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "vision",
        "video",
        "reasoning",
        "merge",
        "preview",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2025-09-08",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.2,
        "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": 1197,
        "categoryRank": 72,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 45,
          "reach": 60,
          "risk": 55,
          "upkeep": 28
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.",
        "bestFor": "Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.",
        "strengths": [
          "Plain Apache 2.0 over Apache and MIT bases, with no gate",
          "Safetensors only and no repository code in the Transformers-format files",
          "Loads in released Transformers and vLLM, and the GitHub-format twin in SGLang and llama.cpp",
          "Thinking turned on per request with a documented system prompt",
          "About 4B active parameters per token on a 21.2B mixture of experts"
        ],
        "weaknesses": [
          "No quantised build from OpenGVLab, so about 51 GB at BF16",
          "Chat template uses markers its tokenizer lacks and differs from the twin's harmony template",
          "No change to the files since September 2025, and it's still a preview",
          "No safety results, agent guidance or security contact",
          "No documented tool calling or structured output, and trained at 32K tokens"
        ],
        "agentNotes": [
          "Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM",
          "Check the rendered prompt before relying on it, since the shipped template's \u003c|im_start|\u003e markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative",
          "Use Transformers 4.55.0 or later, as the card requires for this size",
          "For thinking, send the card's thinking system prompt and parse the reasoning out of the \u003cthink\u003e tags, with temperature 0.6 and sampling on as the card advises",
          "Keep inputs within 32K tokens, the length it was trained at, whatever the config says"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 51.2
          }
        ],
        "editorialScores": {
          "interface": 45,
          "reach": 60,
          "risk": 55,
          "upkeep": 28
        },
        "provenanceScore": 53,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/chart.png\"}}, {\"type\": \"text\", \"text\": \"Describe this chart.\"}]}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/internvl3-5-gpt-oss-20b-a4b-preview"
      },
      "weights": {
        "trainedBy": "OpenGVLab (Shanghai AI Laboratory)",
        "builtOn": "openai/gpt-oss-20b and OpenGVLab/InternViT-300M-448px-V2_5 (declared as a merge)",
        "params": "21.2B",
        "active": "4B",
        "context": 32768,
        "goodFor": "Vision-language preview for images, documents, charts and video with text out and an optional thinking mode, on the gpt-oss-20b mixture-of-experts backbone.",
        "runtimes": [
          "Transformers",
          "vLLM",
          "SGLang",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 42.5,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "Transformers",
              "vLLM"
            ],
            "note": "Transformers' native InternVL format, no repository code. The shipped chat template uses markers that aren't in the tokenizer"
          },
          {
            "name": "InternVL3_5-GPT-OSS-20B-A4B-Preview",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 42.5,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "SGLang",
              "llama.cpp (after conversion)",
              "Transformers (remote code)"
            ],
            "note": "The same weights in OpenGVLab's GitHub format (InternVLChatModel) with modeling code for trust_remote_code and gpt-oss's harmony chat template"
          }
        ],
        "run": "vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768",
        "runBuild": "InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
        "checked": "2026-10-21",
        "minMemoryGB": 64
      },
      "family": {
        "lab": "OpenGVLab (Shanghai AI Laboratory)",
        "family": "InternVL3 (3.0, 3.5)",
        "url": "https://www.anchorterminal.com/open-weights/#opengvlab",
        "others": [
          {
            "name": "InternVL3_5-8B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Flash",
            "params": "8B (Hub safetensors count 8.81B)"
          },
          {
            "name": "InternVL3_5-4B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Flash",
            "params": "4B (Hub safetensors count 4.91B)"
          },
          {
            "name": "InternVL3_5-38B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Flash",
            "params": "38B (Hub safetensors count 39.56B)"
          },
          {
            "name": "InternVL3_5-30B-A3B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Flash",
            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.99B)"
          },
          {
            "name": "InternVL3_5-2B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Flash",
            "params": "2B (Hub safetensors count 2.49B)"
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Pretrained",
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-MPO",
            "params": "8B (Hub safetensors count 8.53B)"
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Instruct",
            "params": "8B (Hub safetensors count 8.53B)"
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            "name": "InternVL3_5-8B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B",
            "params": "8B (Hub safetensors count 8.53B)"
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          {
            "name": "InternVL3_5-4B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Pretrained",
            "params": "4B (Hub safetensors count 4.73B)"
          },
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-MPO",
            "params": "4B (Hub safetensors count 4.73B)"
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            "params": "4B (Hub safetensors count 4.73B)"
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B",
            "params": "4B (Hub safetensors count 4.73B)"
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            "name": "InternVL3_5-38B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Pretrained",
            "params": "38B (Hub safetensors count 38.39B)"
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-MPO",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Instruct",
            "params": "38B (Hub safetensors count 38.39B)"
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B",
            "params": "38B (Hub safetensors count 38.39B)"
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            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)"
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          {
            "name": "InternVL3_5-30B-A3B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Instruct",
            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)"
          },
          {
            "name": "InternVL3_5-30B-A3B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B",
            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)"
          },
          {
            "name": "InternVL3_5-2B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Pretrained",
            "params": "2B (Hub safetensors count 2.35B)"
          },
          {
            "name": "InternVL3_5-2B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-MPO",
            "params": "2B (Hub safetensors count 2.35B)"
          },
          {
            "name": "InternVL3_5-2B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Instruct",
            "params": "2B (Hub safetensors count 2.35B)"
          },
          {
            "name": "InternVL3_5-2B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B",
            "params": "2B (Hub safetensors count 2.35B)"
          },
          {
            "name": "InternVL3_5-241B-A28B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Pretrained",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)"
          },
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            "name": "InternVL3_5-241B-A28B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-MPO",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)"
          },
          {
            "name": "InternVL3_5-241B-A28B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Instruct",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)"
          },
          {
            "name": "InternVL3_5-241B-A28B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)"
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          {
            "name": "InternVL3_5-1B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Pretrained",
            "params": "1B (Hub safetensors count 1.06B)"
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-MPO",
            "params": "1B (Hub safetensors count 1.06B)"
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            "params": "1B (Hub safetensors count 1.06B)"
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            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B",
            "params": "1B (Hub safetensors count 1.06B)"
          },
          {
            "name": "InternVL3_5-14B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Pretrained",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3_5-14B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-MPO",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3_5-14B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Instruct",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3_5-14B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B",
            "params": "14B (Hub safetensors count 15.12B)"
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          {
            "name": "InternVL3-9B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-9B-Pretrained",
            "params": "9B (Hub safetensors count 9.14B)"
          },
          {
            "name": "InternVL3-8B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Pretrained",
            "params": "8B (Hub safetensors count 7.94B)"
          },
          {
            "name": "InternVL3-78B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Pretrained",
            "params": "78B (Hub safetensors count 78.41B)"
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            "name": "InternVL3-38B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-38B-Pretrained",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3-2B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-2B-Pretrained",
            "params": "2B (Hub safetensors count 2.09B)"
          },
          {
            "name": "InternVL3-1B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-1B-Pretrained",
            "params": "1B (Hub safetensors count 938M)"
          },
          {
            "name": "InternVL3-14B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-14B-Pretrained",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3-9B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-9B-Instruct",
            "params": "9B (Hub safetensors count 9.14B)"
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            "name": "InternVL3-8B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct",
            "params": "8B (Hub safetensors count 7.94B)"
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          {
            "name": "InternVL3-78B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Instruct",
            "params": "78B (Hub safetensors count 78.41B)"
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          {
            "name": "InternVL3-38B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-38B-Instruct",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3-2B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-2B-Instruct",
            "params": "2B (Hub safetensors count 2.09B)"
          },
          {
            "name": "InternVL3-1B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-1B-Instruct",
            "params": "1B (Hub safetensors count 938M)"
          },
          {
            "name": "InternVL3-14B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-14B-Instruct",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3-9B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-9B",
            "params": "9B (Hub safetensors count 9.14B)"
          },
          {
            "name": "InternVL3-8B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-8B",
            "params": "8B (Hub safetensors count 7.94B)"
          },
          {
            "name": "InternVL3-78B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-78B",
            "params": "78B (Hub safetensors count 78.41B)"
          },
          {
            "name": "InternVL3-38B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-38B",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3-2B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-2B",
            "params": "2B (Hub safetensors count 2.09B)"
          },
          {
            "name": "InternVL3-1B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-1B",
            "params": "1B (Hub safetensors count 938M)"
          },
          {
            "name": "InternVL3-14B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-14B",
            "params": "14B (Hub safetensors count 15.12B)"
          }
        ],
        "olderCount": 41
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Shanghai AI Laboratory (OpenGVLab), not named in the repository",
        "domain": "github.com/OpenGVLab",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "OpenGVLab's GitHub organisation profile links its Hugging Face organisation. The repositories carry no LICENSE file or copyright line, and rdap.org has no RDAP service for .cn, so shlab.org.cn's registration date wasn't checked.",
          "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 commit history, five commits from 29 August to 8 September 2025, all titled Upload folder using huggingface_hub, which don't say what changed.",
          "www.shlab.org.cn and internvl.github.io answer 404 for security.txt, and OpenGVLab/InternVL has no security policy."
        ],
        "score": 53
      },
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    "facts": [
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        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "InternScience (Shanghai AI Laboratory)",
        "b": "OpenGVLab (Shanghai AI Laboratory)",
        "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; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-14",
        "b": "2025-09-08",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3/5 (4)",
        "b": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
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        "answer": "Agents-A1-4B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, Agents-A1-4B or InternVL3.5-GPT-OSS-20B-A4B-Preview?"
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          "Agent interface, 70 against 45",
          "Documentation and upkeep, 43 against 28"
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        "also": [
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        ],
        "goodFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "slug": "agents-a1-4b",
        "watchFor": "The base isn't declared, and the technical report doesn't cover the 4B"
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      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.",
        "slug": "internvl3-5-gpt-oss-20b-a4b-preview",
        "watchFor": "No quantised build from OpenGVLab, so about 51 GB at BF16"
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.json",
        "title": "Agents-A1-4B vs Apodex-1.1-mini",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.json",
        "title": "Agents-A1-4B vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.json",
        "title": "Agents-A1-4B vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.json",
        "title": "Agents-A1-4B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.json",
        "title": "Agents-A1-4B vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731"
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.json",
        "title": "Agents-A1-4B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash"
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      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-8b-instruct.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwopus3-8-27b-flash-v2.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-smollm3-3b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-spark-x2-6-4b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "agents-a1-4b": 90,
        "by": 30,
        "edge": "agents-a1-4b",
        "internvl3-5-gpt-oss-20b-a4b-preview": 60,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "agents-a1-4b": 55,
        "by": 0,
        "edge": "",
        "internvl3-5-gpt-oss-20b-a4b-preview": 55,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "agents-a1-4b": 70,
        "by": 25,
        "edge": "agents-a1-4b",
        "internvl3-5-gpt-oss-20b-a4b-preview": 45,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "agents-a1-4b": 43,
        "by": 15,
        "edge": "agents-a1-4b",
        "internvl3-5-gpt-oss-20b-a4b-preview": 28,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Agents-A1-4B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
      "internvl3-5-gpt-oss-20b-a4b-preview": "An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview",
    "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.min.md"
  },
  "markdown": "Agents-A1-4B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #34 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json\n- InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 51.2/100, rank #90 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Reach, 90 against 60\n- Agent interface, 70 against 45\n- Documentation and upkeep, 43 against 28\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 1.8 out of 5\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview (D)\n\nGood for: Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.\n\nWatch for: No quantised build from OpenGVLab, so about 51 GB at BF16\n\n\n## Score by category\n\n| Category | Weight | Agents-A1-4B | InternVL3.5-GPT-OSS-20B-A4B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 60 | Agents-A1-4B +30 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 45 | Agents-A1-4B +25 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 28 | Agents-A1-4B +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **51.2 · D** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | InternVL3.5-GPT-OSS-20B-A4B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | OpenGVLab (Shanghai AI Laboratory) |\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; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2025-09-08 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3/5 (4) | 1.8/5 (4) |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\n\n**InternVL3.5-GPT-OSS-20B-A4B-Preview.** An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.\n\n## Before you call either\n\n### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. Validate JSON output yourself, since structured output isn't documented\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n1. Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM\n2. Check the rendered prompt before relying on it, since the shipped template's \u003c|im_start|\u003e markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative\n3. Use Transformers 4.55.0 or later, as the card requires for this size\n4. For thinking, send the card's thinking system prompt and parse the reasoning out of the \u003cthink\u003e tags, with temperature 0.6 and sampling on as the card advises\n5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says\n\n## Questions\n\n### Which is better for AI agents, Agents-A1-4B or InternVL3.5-GPT-OSS-20B-A4B-Preview?\n\nAgents-A1-4B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"internvl3-5-gpt-oss-20b-a4b-preview\"}`. From a terminal: `anchor compare agents-a1-4b internvl3-5-gpt-oss-20b-a4b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json\n\n## Other comparisons with Agents-A1-4B or InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n- [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md)\n- [Agents-A1-4B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md)\n- [Agents-A1-4B vs BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md)\n- [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.md)\n- [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md)\n- [Agents-A1-4B vs 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