{ "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", "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)" }, { "name": "InternVL3_5-241B-A28B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Flash", "params": "241B total, 28B active (MoE) (Hub safetensors count 241.71B)" }, { "name": "InternVL3_5-1B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Flash", "params": "1B (Hub safetensors count 1.15B)" }, { "name": "InternVL3_5-14B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Flash", "params": "14B (Hub safetensors count 15.50B)" }, { "name": "InternVL3_5-8B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Pretrained", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-MPO", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Instruct", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-4B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Pretrained", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-MPO", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-38B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Pretrained", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-MPO", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Instruct", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-30B-A3B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Pretrained", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-30B-A3B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-MPO", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "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)" }, { "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)" }, { "name": "InternVL3_5-1B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Pretrained", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-MPO", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Instruct", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B", "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)" }, { "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)" }, { "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)" }, { "name": "InternVL3-8B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct", "params": "8B (Hub safetensors count 7.94B)" }, { "name": "InternVL3-78B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Instruct", "params": "78B (Hub safetensors count 78.41B)" }, { "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 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.json" }, "answer": "Kimi K3 scores 62.4 (B) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach.", "b": { "slug": "kimi-k3", "name": "Kimi K3", "vendor": "Moonshot AI", "vendorUrl": "https://www.moonshot.ai", "kind": "weights", "category": "open-weights", "summary": "Kimi K3 is Moonshot AI's open-weight multimodal mixture-of-experts model, 2.8T parameters with 104B active and a 1M-token context, trained in MXFP4 and published on Hugging Face under the modified-MIT Kimi K3 Licence.", "url": "https://www.anchorterminal.com/tools/kimi-k3", "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3.json", "repo": "https://huggingface.co/moonshotai/Kimi-K3", "license": "Kimi K3 License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "moonshotai/Kimi-K3" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3 Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (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/moonshotai/Kimi-K3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "modified-mit", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2026-09-02", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "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": 24, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "bestFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "strengths": [ "1,048,576 tokens of context trained in, with indexed parallel tool calls in one turn", "Model code in released vLLM, SGLang and llama.cpp, with recipes linked from the card", "A third-party cyber evaluation by the UK AI Security Institute and CAISI", "Safetensors only, with a SHA-256 for every shard, downloadable with no account", "Served by Moonshot's API and five inference providers for agents without the hardware" ], "weaknesses": [ "1,561 GB of files at its smallest publisher build, far beyond any single machine", "No Jinja template, so prompts, tokeniser and model load through Moonshot's Python with trust_remote_code", "No Moonshot reply in the 40 most recent discussions, and a tokeniser report waits unanswered", "No disclosure route for flaws and no prompt-injection guidance", "No training sources, sizes or knowledge cut-off, and the card leaves out limitations and languages" ], "agentNotes": [ "Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect", "Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable", "Thinking is always on; set reasoning_effort to low or high when max is too slow", "Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones", "Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear", "Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "provenanceScore": 53, "checked": { "categories": { "capability": "2026-10-21", "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": "# SGLang, per its Kimi-K3 cookbook linked from the card (one node of 8 B300 GPUs)\nsglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 \\\n --context-length 65536 --mem-fraction-static 0.82 \\\n --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"moonshotai/Kimi-K3\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"high\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kimi-k3" }, "weights": { "trainedBy": "Moonshot AI", "params": "2.8T", "active": "104B", "context": 1048576, "goodFor": "Large multimodal agent model for coding and long tool-calling sessions, text and images in, with always-on reasoning and JSON Schema output.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Kimi-K3", "url": "https://huggingface.co/moonshotai/Kimi-K3", "who": "publisher", "precision": "mxfp4", "sizeGB": 1560.9, "memoryGB": 2048, "format": "safetensors", "note": "Trained in MXFP4; the only build Moonshot publishes" } ], "run": "sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "runBuild": "Kimi-K3", "checked": "2026-10-21", "minMemoryGB": 2048 }, "family": { "lab": "Moonshot AI (Kimi)", "family": "Kimi K3", "url": "https://www.anchorterminal.com/open-weights/#moonshot", "olderCount": 18, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)", "domain": "moonshot.ai", "domainRegistered": "", "domainNote": "moonshot.ai's registration date couldn't be read through RDAP on 21 October 2026.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/moonshotai/Kimi-K3/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names Moonshot AI. The Kimi Platform's terms (platform.kimi.ai/docs/agreement/modeluse) name Moonshot AI PTE. LTD. as the party for its API.", "Nothing is hosted, so the Kimi K3 Licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Its messages mostly say \"Update README.md\" or \"Update encoding_k3.py\" without saying what changed; the last change, on 2 September 2026, added an evaluation result.", "moonshot.ai, www.moonshot.ai, kimi.com and www.kimi.com answered /.well-known/security.txt with an HTML page and platform.kimi.ai with 404 on 21 October 2026, so no security.txt was found. The Kimi-K3 GitHub repository has no SECURITY.md." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenGVLab (Shanghai AI Laboratory)", "b": "Moonshot AI", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)", "b": "Kimi K3 License (modified MIT)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-09-08", "b": "2026-09-02", "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": "", "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": "1.8/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Kimi K3 scores 62.4 (B) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach.", "question": "Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or Kimi K3?" } ], "goodFor": [ { "aheadOn": [ "Reach, 60 against 50" ], "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": [ "Risk, 65 against 55", "Agent interface, 65 against 45", "Documentation and upkeep, 41 against 28" ], "also": [ "Rated higher by the reviewer agents, 2.3 against 1.8 out of 5" ], "goodFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "slug": "kimi-k3", "watchFor": "1,561 GB of files at its smallest publisher build, far beyond any single machine" } ], "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-kimi-k3.json", "title": "Agents-A1-4B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/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-kimi-k3.json", "title": "Apertus v1.5 8B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "Apodex-1.1-mini vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "BigBang-v1 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3" }, { "json": 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"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" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1.json", "title": "Kimi K3 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.json", "title": "Kimi K3 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.json", "title": "Kimi K3 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.json", "title": "Kimi K3 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.json", "title": "Kimi K3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.json", "title": "Kimi K3 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Kimi K3 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.json", "title": "Kimi K3 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b.json", "title": "Kimi K3 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5.json", "title": "Kimi K3 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7.json", "title": "Kimi K3 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.json", "title": "Kimi K3 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Kimi K3 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b.json", "title": "Kimi K3 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-4b.json", "title": "Kimi K3 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Kimi K3 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.json", "title": "Kimi K3 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-ultra-550b-a55b.json", "title": "Kimi K3 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.json", "title": "Kimi K3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-olmo-3-7b-think.json", "title": "Kimi K3 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.json", "title": "Kimi K3 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b.json", "title": "Kimi K3 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.json", "title": "Kimi K3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Kimi K3 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-4b-instruct-2507.json", "title": "Kimi K3 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b.json", "title": "Kimi K3 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b.json", "title": "Kimi K3 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-2b.json", "title": "Kimi K3 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b.json", "title": "Kimi K3 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-4b.json", "title": "Kimi K3 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b.json", "title": "Kimi K3 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-27b.json", "title": "Kimi K3 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.json", "title": "Kimi K3 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.json", "title": "Kimi K3 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-flash-next.json", "title": "Kimi K3 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Kimi K3 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next.json", "title": "Kimi K3 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Kimi K3 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-2b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-32b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2.json", "title": "Kimi K3 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-smollm3-3b.json", "title": "Kimi K3 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b.json", "title": "Kimi K3 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b.json", "title": "Kimi K3 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Kimi K3 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b.json", "title": "Kimi K3 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "internvl3-5-gpt-oss-20b-a4b-preview", "internvl3-5-gpt-oss-20b-a4b-preview": 60, "key": "reach", "kimi-k3": 50, "name": "Reach", "weight": 25 }, { "by": 10, "edge": "kimi-k3", "internvl3-5-gpt-oss-20b-a4b-preview": 55, "key": "risk", "kimi-k3": 65, "name": "Risk", "weight": 20 }, { "by": 20, "edge": "kimi-k3", "internvl3-5-gpt-oss-20b-a4b-preview": 45, "key": "interface", "kimi-k3": 65, "name": "Agent interface", "weight": 10 }, { "by": 13, "edge": "kimi-k3", "internvl3-5-gpt-oss-20b-a4b-preview": 28, "key": "upkeep", "kimi-k3": 41, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Kimi K3 scores 62.4 (B) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach. Both do open-weight models you run yourself.", "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.", "kimi-k3": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3", "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.min.md" }, "markdown": "Kimi K3 scores 62.4 (B) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach. Both do open-weight models you run yourself.\n\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- Kimi K3: grade B, 62.4/100, rank #29 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.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### 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\nAhead on:\n- Reach, 60 against 50\n\nWatch for: No quantised build from OpenGVLab, so about 51 GB at BF16\n\n### Kimi K3 (B)\n\nGood for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work\n\nAhead on:\n- Risk, 65 against 55\n- Agent interface, 65 against 45\n- Documentation and upkeep, 41 against 28\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.3 against 1.8 out of 5\n\nWatch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine\n\n\n## Score by category\n\n| Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | Kimi K3 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 50 | InternVL3.5-GPT-OSS-20B-A4B-Preview +10 |\n| Risk | 20% (20 this run) | 55 | 65 | Kimi K3 +10 |\n| Agent interface | 10% (10 this run) | 45 | 65 | Kimi K3 +20 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 41 | Kimi K3 +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.2 · D** | **62.4 · B** | |\n\n## Facts side by side\n\n| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | Kimi K3 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenGVLab (Shanghai AI Laboratory) | Moonshot AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | Kimi K3 License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-09-08 | 2026-09-02 |\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 | 1.8/5 (4) | 2.3/5 (4) |\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**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.\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### Kimi K3\n\n1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect\n3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable\n4. Thinking is always on; set reasoning_effort to low or high when max is too slow\n5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones\n6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear\n7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page\n\n## Questions\n\n### Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or Kimi K3?\n\nKimi K3 scores 62.4 (B) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"internvl3-5-gpt-oss-20b-a4b-preview\", \"b\": \"kimi-k3\"}`. 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