{
  "data": {
    "a": {
      "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",
      "dns": "internvl3-5-gpt-oss-20b-a4b-preview.grade.anchorterminal.com",
      "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": 53.2,
        "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": 1518,
        "categoryRank": 101,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 45,
          "reach": 65,
          "risk": 55,
          "upkeep": 28
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "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, llama.cpp and Ollama through its llama.cpp pin",
          "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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 53.2
          }
        ],
        "editorialScores": {
          "interface": 45,
          "reach": 65,
          "risk": 55,
          "upkeep": 28
        },
        "provenanceScore": 61,
        "checked": {
          "categories": {
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "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",
          "Ollama"
        ],
        "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)",
              "Ollama (through its llama.cpp pin, 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": "2026-11-04",
        "minMemoryGB": 64
      },
      "family": {
        "lab": "OpenGVLab (Shanghai AI Laboratory)",
        "family": "InternVL3 (3.0, 3.5)",
        "url": "https://www.anchorterminal.com/open-weights/#opengvlab",
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          {
            "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": "2026-11-04",
        "notes": [
          "OpenGVLab's GitHub organisation profile links its Hugging Face organisation. The repositories carry no LICENSE file or copyright line, and 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. Unchanged on 4 November 2026.",
          "www.shlab.org.cn and internvl.github.io answered 404 for security.txt on 21 October 2026, and OpenGVLab/InternVL has no security policy."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.json",
      "live": {
        "slug": "internvl3-5-gpt-oss-20b-a4b-preview",
        "domain": {
          "domain": "github.com/opengvlab",
          "checkedAt": "2026-11-08T15:08:02.394346699Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:18.761399377Z",
            "changedAt": "2026-11-10T16:22:18.761399377Z",
            "fingerprint": "e63419d9bf08"
          }
        ],
        "updatedAt": "2026-11-10T16:22:18.761399377Z"
      }
    },
    "answer": "ZAYA1-8B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "zaya1-8b",
      "name": "ZAYA1-8B",
      "vendor": "Zyphra",
      "vendorUrl": "https://www.zyphra.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "ZAYA1-8B is Zyphra's open-weight reasoning model, a mixture of experts of 8.4B parameters with 760M active, trained from scratch for maths and coding. Apache 2.0. Zyphra's 4-bit build is 5.9 GB. Released SGLang and Transformers run it.",
      "url": "https://www.anchorterminal.com/tools/zaya1-8b",
      "markdownUrl": "https://www.anchorterminal.com/tools/zaya1-8b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/zaya1-8b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/zaya1-8b.json",
      "dns": "zaya1-8b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Zyphra/ZAYA1-8B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Zyphra/ZAYA1-8B"
        },
        {
          "registry": "huggingface",
          "name": "Zyphra/ZAYA1-8B-FP8-Experts"
        },
        {
          "registry": "huggingface",
          "name": "Zyphra/ZAYA1-8B-MXFP4-Experts"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0, per the main repository's metadata. Running it yourself costs little: 5.9 GB with the experts at 4 bits (MXFP4), 9.6 GB in FP8 or 17.7 GB in BF16 (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/Zyphra/ZAYA1-8B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "small",
        "reasoning",
        "maths",
        "coding",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-07-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 67,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 80,
          "risk": 55,
          "upkeep": 50
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.",
        "bestFor": "People who want a small, fast reasoning model for maths and code and can live without tool calling",
        "strengths": [
          "Apache 2.0, ungated",
          "5.9 GB at 4 bits, 760M parameters active",
          "Released Transformers and SGLang carry the architecture",
          "Zyphra replied in most of the recent discussions",
          "The original checkpoint is kept beside the reshaped one"
        ],
        "weaknesses": [
          "Tool calling needs a parser that only Zyphra's vLLM fork has",
          "Released vLLM and llama.cpp don't run it, and there's no GGUF",
          "The card's install steps use forks",
          "No limitations, context length, languages, data or safety results on the card",
          "Benchmarks on the card are all from Zyphra's own harness"
        ],
        "agentNotes": [
          "Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork",
          "Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in",
          "Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work",
          "Download `Zyphra/ZAYA1-8B`, not the legacy repository, for the released runtimes",
          "The quantised repositories have no licence tag. The main one is Apache 2.0"
        ],
        "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": 60,
          "reach": 80,
          "risk": 55,
          "upkeep": 50
        },
        "provenanceScore": 56,
        "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": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Zyphra/ZAYA1-8B --reasoning-parser qwen3\n# the card's own route is vLLM from Zyphra's fork, with --tool-call-parser zaya_xml",
        "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. The card recommends temperature 1.0\n# and top_p 0.95 in general, and temperature 0.6 for agent and code work"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/zaya1-8b"
      },
      "weights": {
        "trainedBy": "Zyphra",
        "builtOn": "ZAYA1-reasoning-base, Zyphra's own pre-trained model, per the card",
        "params": "8.4B",
        "active": "760M",
        "context": 131072,
        "goodFor": "Long step-by-step reasoning in maths and code on a small machine, and as the model inside a test-time compute harness.",
        "runtimes": [
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ZAYA1-8B-MXFP4-Experts",
            "url": "https://huggingface.co/Zyphra/ZAYA1-8B-MXFP4-Experts",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 5.85,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "Zyphra's build with the experts at 4 bits. Which released runtime loads it wasn't established"
          },
          {
            "name": "ZAYA1-8B-FP8-Experts",
            "url": "https://huggingface.co/Zyphra/ZAYA1-8B-FP8-Experts",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 9.64,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "Zyphra's build with the experts in FP8"
          },
          {
            "name": "ZAYA1-8B (BF16 safetensors)",
            "url": "https://huggingface.co/Zyphra/ZAYA1-8B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.68,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "SGLang",
              "Transformers"
            ],
            "note": "Four shards, reshaped for Transformers"
          }
        ],
        "run": "python -m sglang.launch_server --model-path Zyphra/ZAYA1-8B --reasoning-parser qwen3",
        "runBuild": "ZAYA1-8B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Zyphra",
        "family": "ZAYA1",
        "url": "https://www.anchorterminal.com/open-weights/#zyphra",
        "others": [
          {
            "name": "ZAYA1-VL-8B",
            "url": "https://huggingface.co/Zyphra/ZAYA1-VL-8B"
          },
          {
            "name": "ZAYA1-74B-preview",
            "url": "https://huggingface.co/Zyphra/ZAYA1-74B-preview"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Zyphra, named on the card and its site. The legal entity wasn't looked up",
        "domain": "zyphra.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Zyphra/ZAYA1-8B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "www.zyphra.com's robots.txt allows reading outside /studio and /api. Its about page gives a San Francisco address and names no people in the text read.",
          "The domain's registration date wasn't looked up. www.zyphra.com has no security.txt (404 on 10 November 2026).",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. Zyphra's site lists a cloud product for its open models, which wasn't read.",
          "The changelog is the commit history, 41 commits from 4 May to 26 June 2026."
        ],
        "score": 56
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/zaya1-8b.json"
    },
    "edges": [
      {
        "by": 22,
        "category": "upkeep",
        "leads": "zaya1-8b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.",
          "b": "Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50."
        },
        "scores": {
          "a": 28,
          "b": 50
        }
      },
      {
        "by": 15,
        "category": "reach",
        "leads": "zaya1-8b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.",
          "b": "Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80."
        },
        "scores": {
          "a": 65,
          "b": 80
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "zaya1-8b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.",
          "b": "Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60."
        },
        "scores": {
          "a": 45,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "OpenGVLab (Shanghai AI Laboratory)",
        "b": "Zyphra",
        "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; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2025-09-08",
        "b": "2026-07-02",
        "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": "1.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "ZAYA1-8B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or ZAYA1-8B?"
      }
    ],
    "goodFor": [
      {
        "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"
      },
      {
        "aheadOn": [
          "Reach, 80 against 65",
          "Agent interface, 60 against 45",
          "Documentation and upkeep, 50 against 28"
        ],
        "also": null,
        "goodFor": "People who want a small, fast reasoning model for maths and code and can live without tool calling",
        "slug": "zaya1-8b",
        "watchFor": "Tool calling needs a parser that only Zyphra's vLLM fork has"
      }
    ],
    "history": {
      "internvl3-5-gpt-oss-20b-a4b-preview": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 2,
          "from": 51.2,
          "fromGrade": "D",
          "to": 53.2,
          "toGrade": "D"
        }
      ],
      "zaya1-8b": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-zaya1-8b.json",
        "title": "Agents-A1-4B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Apertus v1.5 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-zaya1-8b.json",
        "title": "Apertus v1.5 8B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Apodex-1.1-mini vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-zaya1-8b.json",
        "title": "Apodex-1.1-mini vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Arete-27B vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b.json",
        "title": "Arete-27B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "BigBang-v1 vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-zaya1-8b.json",
        "title": "BigBang-v1 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Carbon-X vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview"
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      {
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        "title": "Qwen3.6-27B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-zaya1-8b.json",
        "title": "Qwen3.6-35B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-zaya1-8b.json",
        "title": "Qwen3.8-27B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b.json",
        "title": "Qwen3.8-Flash-Next vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-zaya1-8b.json",
        "title": "Qwen3-Coder-Next vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-VL-2B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-VL-32B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-VL-4B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-VL-8B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b.json",
        "title": "Recursive Flint vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.json",
        "title": "Ripple-65B Preview vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json",
        "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b.json",
        "title": "SmolLM3-3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.json",
        "title": "Spark-X2.6-4B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.json",
        "title": "Step 3.7 Flash vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b.json",
        "title": "Swift 1.5 Qwen3.8-27B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b.json",
        "title": "ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b.json",
        "title": "Baidu Unlimited-OCR-2 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.json",
        "title": "WeVisOne-VL-2B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.json",
        "title": "WeVisOne-VL-4B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "zaya1-8b",
        "internvl3-5-gpt-oss-20b-a4b-preview": 65,
        "key": "reach",
        "name": "Reach",
        "weight": 25,
        "zaya1-8b": 80
      },
      {
        "by": 0,
        "edge": "",
        "internvl3-5-gpt-oss-20b-a4b-preview": 55,
        "key": "risk",
        "name": "Risk",
        "weight": 20,
        "zaya1-8b": 55
      },
      {
        "by": 15,
        "edge": "zaya1-8b",
        "internvl3-5-gpt-oss-20b-a4b-preview": 45,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10,
        "zaya1-8b": 60
      },
      {
        "by": 22,
        "edge": "zaya1-8b",
        "internvl3-5-gpt-oss-20b-a4b-preview": 28,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10,
        "zaya1-8b": 50
      }
    ],
    "summary": "ZAYA1-8B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "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.",
      "zaya1-8b": "A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b.min.md"
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  "markdown": "ZAYA1-8B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 53.2/100, rank #136 of 160 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- ZAYA1-8B: grade C, 61/100, rank #85 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/zaya1-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/zaya1-8b.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### 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### ZAYA1-8B (C)\n\nGood for: People who want a small, fast reasoning model for maths and code and can live without tool calling\n\nAhead on:\n- Reach, 80 against 65\n- Agent interface, 60 against 45\n- Documentation and upkeep, 50 against 28\n\nWatch for: Tool calling needs a parser that only Zyphra's vLLM fork has\n\n\n## Why the edge\n\n### Documentation and upkeep: ZAYA1-8B +22\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview (28): Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.\n- ZAYA1-8B (50): Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.\n\n### Reach: ZAYA1-8B +15\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview (65): Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.\n- ZAYA1-8B (80): Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.\n\n### Agent interface: ZAYA1-8B +15\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview (45): Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.\n- ZAYA1-8B (60): Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n## Score by category\n\n| Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | ZAYA1-8B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 80 | ZAYA1-8B +15 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 45 | 60 | ZAYA1-8B +15 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 50 | ZAYA1-8B +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **53.2 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | ZAYA1-8B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenGVLab (Shanghai AI Laboratory) | Zyphra |\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; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-09-08 | 2026-07-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 1.8/5 (4) | none |\n\n## How the scores have moved\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n- +2.0 on 4 November 2026: correction\n\n### ZAYA1-8B\n\nNo score change since it was listed.\n\n## Verdicts\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**ZAYA1-8B.** A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.\n\n## Before you call either\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### ZAYA1-8B\n\n1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork\n2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in\n3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work\n4. Download `Zyphra/ZAYA1-8B`, not the legacy repository, for the released runtimes\n5. The quantised repositories have no licence tag. The main one is Apache 2.0\n\n## Questions\n\n### Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or ZAYA1-8B?\n\nZAYA1-8B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (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/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"internvl3-5-gpt-oss-20b-a4b-preview\", \"b\": \"zaya1-8b\"}`. From a terminal: `anchor compare internvl3-5-gpt-oss-20b-a4b-preview zaya1-8b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json and https://www.anchorterminal.com/api/v1/tools/zaya1-8b.json\n\n## Other comparisons with InternVL3.5-GPT-OSS-20B-A4B-Preview or ZAYA1-8B\n\n- [Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Agents-A1-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-zaya1-8b.md)\n- [Apertus v1.5 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Apertus v1.5 8B vs ZAYA1-8B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-zaya1-8b.md)\n- [Apodex-1.1-mini vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Apodex-1.1-mini vs ZAYA1-8B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-zaya1-8b.md)\n- [Arete-27B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Arete-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b.md)\n- [BigBang-v1 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [BigBang-v1 vs ZAYA1-8B](https://www.anchorterminal.com/compare/bigbang-v1-vs-zaya1-8b.md)\n- [Carbon-X vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Carbon-X vs ZAYA1-8B](https://www.anchorterminal.com/compare/carbon-x-vs-zaya1-8b.md)\n- [Cola2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/cola2-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Cola2 vs ZAYA1-8B](https://www.anchorterminal.com/compare/cola2-vs-zaya1-8b.md)\n- [Cosmos-Reason2-2B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Cosmos-Reason2-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-zaya1-8b.md)\n- [Darwin-36B-Opus vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Darwin-36B-Opus vs ZAYA1-8B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-zaya1-8b.md)\n- [DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DeepSeek-V3.2 vs ZAYA1-8B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-zaya1-8b.md)\n- [DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DeepSeek V4.1 Flash vs ZAYA1-8B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-zaya1-8b.md)\n- [DeepSeek-V4-Flash-0731 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DeepSeek-V4-Flash-0731 vs ZAYA1-8B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-zaya1-8b.md)\n- [Devstral Small 2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Devstral Small 2 vs ZAYA1-8B](https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b.md)\n- [DiffusionGemma 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DiffusionGemma 26B A4B IT vs ZAYA1-8B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-zaya1-8b.md)\n- [EXAONE 4.5 33B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [EXAONE 4.5 33B vs ZAYA1-8B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-zaya1-8b.md)\n- [Gemma 4 12B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 12B IT vs ZAYA1-8B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-zaya1-8b.md)\n- [Gemma 4 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 26B A4B IT vs ZAYA1-8B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-zaya1-8b.md)\n- [Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 31B IT vs ZAYA1-8B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-zaya1-8b.md)\n- [Gemma 4 E2B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 E2B IT vs ZAYA1-8B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-zaya1-8b.md)\n- [Gemma 4 E4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 E4B IT vs ZAYA1-8B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-zaya1-8b.md)\n- [GLM-4.5-Air vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-4.5-Air vs ZAYA1-8B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-zaya1-8b.md)\n- [GLM-4.7-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-4.7-Flash vs ZAYA1-8B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-zaya1-8b.md)\n- [GLM-5.3-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-5.3-Flash vs ZAYA1-8B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-zaya1-8b.md)\n- [GLM-5.3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-5.3 vs ZAYA1-8B](https://www.anchorterminal.com/compare/glm-5-3-vs-zaya1-8b.md)\n- [gpt-oss-120b vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [gpt-oss-120b vs ZAYA1-8B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-zaya1-8b.md)\n- [gpt-oss-20b vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [gpt-oss-20b vs ZAYA1-8B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-zaya1-8b.md)\n- [Granite 4.2 30B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Granite 4.2 30B vs ZAYA1-8B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-zaya1-8b.md)\n- [Granite 4.2 3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Granite 4.2 3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-zaya1-8b.md)\n- [Granite 4.2 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Granite 4.2 8B vs ZAYA1-8B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-zaya1-8b.md)\n- [Hy-Memory-30B-A3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Hy-Memory-30B-A3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b.md)\n- [Hy3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/hy3-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Hy3 vs ZAYA1-8B](https://www.anchorterminal.com/compare/hy3-vs-zaya1-8b.md)\n- [Inkling-Small vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/inkling-small-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Inkling-Small vs ZAYA1-8B](https://www.anchorterminal.com/compare/inkling-small-vs-zaya1-8b.md)\n- [Inkling vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/inkling-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Inkling vs ZAYA1-8B](https://www.anchorterminal.com/compare/inkling-vs-zaya1-8b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3-1-pro.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kolibri-1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kolibri-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kronos](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kronos.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna M.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-m-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-s-2-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna XS 2.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LBS-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-1.7B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-1-7b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-audio-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-omni-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-VL-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-1-flash-1019.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LongCat-2.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-longcat-2-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-distill-qwen-9b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-1b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-35b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM5-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm5-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M2.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m2-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M2.7](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m2-7.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m3-1-flash.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Mistral Large 4](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-muse-glimmer-30b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-2-3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-3-33b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-30b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Super 120B 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    "description": "ZAYA1-8B scores 61 (C) to InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.",
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    "h1": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B",
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    "path": "/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b",
    "published": "2026-10-01",
    "section": "tools",
    "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B for AI agents (2026)",
    "toc": null,
    "updated": "2026-11-10",
    "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b"
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