{ "data": { "a": { "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", "dns": "kimi-k3.grade.anchorterminal.com", "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 2026-10-24).", "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 2026-10-24).", "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 2026-10-24).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-02" }, "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": 1518, "categoryRank": 23, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "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": "0.7", "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": 70, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "oldest": "2026-11-02", "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\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Kimi-K3-GGUF:UD-Q4_K_XL --jinja", "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" }, "sameCompany": [ "kimi-k3-1-pro" ], "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" }, { "name": "unsloth Kimi-K3-GGUF (UD-Q4_K_XL)", "url": "https://huggingface.co/unsloth/Kimi-K3-GGUF", "who": "unsloth", "precision": "q4_k_xl", "sizeGB": 1509.57, "memoryGB": 2048, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Moonshot's. 1508.67 GB of UD-Q4_K_XL weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" } ], "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": "2026-10-24", "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": "2017-12-15", "domainNote": "The .ai registry's RDAP server (Identity Digital) gives moonshot.ai a registration date of 15 December 2017, read 24 October 2026; rdap.org didn't answer on 21 October.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/moonshotai/Kimi-K3/commits/main", "securityTxt": "none", "checked": "2026-10-24", "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 and 24 October 2026, so no security.txt was found. The Kimi-K3 GitHub repository has no SECURITY.md." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3.json", "live": { "slug": "kimi-k3", "securityTxt": { "url": "https://moonshot.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:03.282831769Z" }, "domain": { "domain": "moonshot.ai", "registered": "2017-12-15", "source": "https://rdap.identitydigital.services/rdap/domain/moonshot.ai", "checkedAt": "2026-11-08T14:52:29.297019771Z" }, "pages": [ { "url": "https://huggingface.co/moonshotai/Kimi-K3/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:14.587006924Z", "changedAt": "2026-11-10T16:23:14.587006924Z", "fingerprint": "f16ede6bc278" } ], "updatedAt": "2026-11-10T16:23:14.587006924Z" } }, "answer": "Kimi K3 scores 62.4 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B leads on reach, agent interface and documentation and upkeep.", "b": { "slug": "wevisone-vl-4b", "name": "WeVisOne-VL-4B", "vendor": "Tencent (WeChat Vision)", "vendorUrl": "https://www.tencent.com", "kind": "weights", "category": "open-weights", "summary": "WeVisOne-VL-4B is an open-weight perception model of 4.8B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.", "url": "https://www.anchorterminal.com/tools/wevisone-vl-4b", "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json", "dns": "wevisone-vl-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/WeVisOne-VL-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/WeVisOne-VL-4B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware, 9.66 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://github.com/Tencent/WeVisOne-VL", "capabilities": [ "model.open-weights", "docs.ocr" ], "tags": [ "open-weights", "model", "vision", "grounding", "object-detection", "segmentation", "matting", "ocr", "counting", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-10-27", "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": 66, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.", "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "strengths": [ "Seven perception tasks in one model, from boxes to alpha mattes", "Returns None for a target that isn't in the image", "Apache 2.0, ungated", "Each task's input and output is documented", "9.66 GB in BF16, so it fits a 12 GB machine" ], "weaknesses": [ "The pixel decoder ships as a pickle file", "Masks and mattes need Tencent's package from GitHub", "No quantised build from Tencent", "One image and one turn at a time, no video", "No reply from Tencent on the repository's discussions", "No context length, languages, safety results or data sources on the card" ], "agentNotes": [ "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`", "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting", "A None answer means the target isn't in the image. Treat it as a result, not an error", "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's", "Send one image a turn. Video grids are rejected by the decoder", "The card says it isn't for identifying individuals or for high-impact decisions on its own" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "provenanceScore": 68, "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": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"transformers\") or backend=\"vllm\"", "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-4B" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/wevisone-vl-4b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy-memory-30b-a3b", "hy3", "wevisone-vl-2b" ], "weights": { "trainedBy": "Tencent WeChat Vision", "params": "4.8B", "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 12 GB machine.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "WeVisOne-VL-4B (BF16 safetensors)", "url": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 9.66, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package" } ], "run": "vllm serve tencent/WeVisOne-VL-4B", "runBuild": "WeVisOne-VL-4B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 12 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "WeVisOne-VL", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "WeVisOne-VL-2B", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B", "params": "2.4B (2,441,744,384 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/wevisone-vl-2b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/WeVisOne-VL-4B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history.", "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.json" }, "edges": [ { "by": 20, "category": "reach", "leads": "wevisone-vl-4b", "name": "Reach", "reasons": { "a": "Memory earns nothing, because the only build of 4-bit precision or better from Moonshot or a runtime's own library is Moonshot's MXFP4 release of 1,561 GB, about 1,873 GB with the fifth added, far above 256 GB. Ollama and LM Studio list it only as a cloud model. Runtimes earn 15, for model code in released vLLM, SGLang and llama.cpp; Transformers has no kimi_k3 code of its own and loads Moonshot's remote code, so it doesn't count. Builds earn 10 for one publisher precision, the MXFP4 files the model was trained for. Terms earn 10, a custom MIT-based licence that is free for commercial use below a revenue threshold. Download earns 10, with no gate and no account. Hosted earns 5, since Moonshot's API serves it and five inference providers are live for it on Hugging Face.", "b": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false 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. 30 + 15 + 0 + 15 + 10 + 0 = 70." }, "scores": { "a": 50, "b": 70 } }, { "by": 10, "category": "risk", "leads": "kimi-k3", "name": "Risk", "reasons": { "a": "File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and builds prompts with encoding_k3.py. Provenance earns the full 15, files in the organisation Moonshot links to with a SHA-256 for every shard. Safety evaluation earns 20 for the cyber assessment the UK AI Security Institute and CAISI ran and published, which found its safeguards didn't stop exploit development. Agent guidance earns 5 for the blog's general cautions about proactiveness and passing back thinking. Licence risk earns 10, losing 5 for the model-as-a-service revenue threshold and nothing else. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or public advisories found.", "b": "File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55." }, "scores": { "a": 65, "b": 55 } }, { "by": 7, "category": "upkeep", "leads": "wevisone-vl-4b", "name": "Documentation and upkeep", "reasons": { "a": "The model card earns 11, covering intended use, architecture, parameters and context, but not limitations, languages or a knowledge cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 15, an encoding_k3.py update on 20 August, 74 days before 2 November and within 90; the GitHub repository's last commit is 6 August. Responsiveness earns nothing, with no Moonshot reply in the 40 most recent discussions. Version policy earns 5 for a first release. File health earns 5, because a report that the tokeniser goes past the declared vocabulary waits without a reply.", "b": "Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48." }, "scores": { "a": 41, "b": 48 } }, { "by": 5, "category": "interface", "leads": "wevisone-vl-4b", "name": "Agent interface", "reasons": { "a": "The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run. The tool-calling format earns 10, since Moonshot documents the tools channel but the parser name comes only from vLLM's and SGLang's own recipes. Trained tool calling earns 20 for indexed parallel calls in one turn. Structured output earns 5, as the card names it among its linked guides without saying how to ask the weights for it, and the how sits in hosted-API docs. Reasoning control earns 10, with per-request effort documented on the card and reasoning returned as reasoning_content. Context earns 10 for 1,048,576 tokens trained in.", "b": "Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70." }, "scores": { "a": 65, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Moonshot AI", "b": "Tencent (WeChat Vision)", "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": "Kimi K3 License (modified MIT)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-09-02", "b": "2026-10-27", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "2.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Kimi K3 scores 62.4 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B leads on reach, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Kimi K3 or WeVisOne-VL-4B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 65 against 55" ], "also": null, "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" }, { "aheadOn": [ "Reach, 70 against 50", "Agent interface, 70 against 65", "Documentation and upkeep, 48 against 41" ], "also": null, "goodFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "slug": "wevisone-vl-4b", "watchFor": "The pixel decoder ships as a pickle file" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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}, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.json", "title": "Kimi K3 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl" }, { "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-minicpm-v-4-7-1b.json", "title": "Kimi K3 vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-35b-a3b.json", "title": "Kimi K3 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b.json", "title": "Kimi K3 vs MiniCPM5-2B", "url": 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"https://www.anchorterminal.com/compare/kimi-k3-vs-zaya1-8b.json", "title": "Kimi K3 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.json", "title": "Kolibri-1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.json", "title": "Kronos vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.json", "title": "Laguna M.1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.json", "title": "Laguna S 2.1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b" }, { "json": 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"https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b.json", "title": "LFM3-Omni-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.json", "title": "LFM3-VL-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b.json", "title": "Ling-3.0-tiny vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b.json", "title": "Ling-3.1-flash-1019 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.json", "title": "LongCat-2.5 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.json", "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b.json", "title": "MiniCPM-o 4.5 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b.json", "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-4b.json", "title": "MiniCPM5-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b.json", "title": "MiniMax-M2.5 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b.json", "title": "MiniMax-M2.7 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-4b.json", "title": "MiniMax-M3.1-Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-4b.json", "title": "Mistral Large 4 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b.json", "title": "Muse Glimmer 30B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.json", "title": "Nanbeige4.2-3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.json", "title": "Nanbeige4.3-33B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-4b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-4b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Nano 30B A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.json", "title": "North Mini Code 1.0 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b.json", "title": "occamy-1.0 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b.json", "title": "Olmo 3 7B Think vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-122B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-397B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-9B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b.json", "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-4b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-4b.json", "title": "Qwen3-4B-Instruct-2507 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-0.8B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-122B-A10B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-397B-A17B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.json", "title": "Qwen3.6-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-4b.json", "title": "Qwen3.6-35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "Qwen3.8-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b.json", "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b.json", "title": "Qwen3-Coder-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-2B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b.json", "title": "Recursive Flint vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.json", "title": "Ripple-65B Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.json", "title": "SmolLM3-3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.json", "title": "Spark-X2.6-4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json", "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.json", "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "wevisone-vl-4b", "key": "reach", "kimi-k3": 50, "name": "Reach", "weight": 25, "wevisone-vl-4b": 70 }, { "by": 10, "edge": "kimi-k3", "key": "risk", "kimi-k3": 65, "name": "Risk", "weight": 20, "wevisone-vl-4b": 55 }, { "by": 5, "edge": "wevisone-vl-4b", "key": "interface", "kimi-k3": 65, "name": "Agent interface", "weight": 10, "wevisone-vl-4b": 70 }, { "by": 7, "edge": "wevisone-vl-4b", "key": "upkeep", "kimi-k3": 41, "name": "Documentation and upkeep", "weight": 10, "wevisone-vl-4b": 48 } ], "summary": "Kimi K3 scores 62.4 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "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.", "wevisone-vl-4b": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b", "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.min.md" }, "markdown": "Kimi K3 scores 62.4 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Kimi K3: grade B, 62.4/100, rank #30 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.json\n- WeVisOne-VL-4B: grade C, 61/100, rank #84 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.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### 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\nWatch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine\n\n### WeVisOne-VL-4B (C)\n\nGood for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence\n\nAhead on:\n- Reach, 70 against 50\n- Agent interface, 70 against 65\n- Documentation and upkeep, 48 against 41\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Reach: WeVisOne-VL-4B +20\n\n- Kimi K3 (50): Memory earns nothing, because the only build of 4-bit precision or better from Moonshot or a runtime's own library is Moonshot's MXFP4 release of 1,561 GB, about 1,873 GB with the fifth added, far above 256 GB. Ollama and LM Studio list it only as a cloud model. Runtimes earn 15, for model code in released vLLM, SGLang and llama.cpp; Transformers has no kimi_k3 code of its own and loads Moonshot's remote code, so it doesn't count. Builds earn 10 for one publisher precision, the MXFP4 files the model was trained for. Terms earn 10, a custom MIT-based licence that is free for commercial use below a revenue threshold. Download earns 10, with no gate and no account. Hosted earns 5, since Moonshot's API serves it and five inference providers are live for it on Hugging Face.\n- WeVisOne-VL-4B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false 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. 30 + 15 + 0 + 15 + 10 + 0 = 70.\n\n### Risk: Kimi K3 +10\n\n- Kimi K3 (65): File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and builds prompts with encoding_k3.py. Provenance earns the full 15, files in the organisation Moonshot links to with a SHA-256 for every shard. Safety evaluation earns 20 for the cyber assessment the UK AI Security Institute and CAISI ran and published, which found its safeguards didn't stop exploit development. Agent guidance earns 5 for the blog's general cautions about proactiveness and passing back thinking. Licence risk earns 10, losing 5 for the model-as-a-service revenue threshold and nothing else. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or public advisories found.\n- WeVisOne-VL-4B (55): File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.\n\n### Documentation and upkeep: WeVisOne-VL-4B +7\n\n- Kimi K3 (41): The model card earns 11, covering intended use, architecture, parameters and context, but not limitations, languages or a knowledge cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 15, an encoding_k3.py update on 20 August, 74 days before 2 November and within 90; the GitHub repository's last commit is 6 August. Responsiveness earns nothing, with no Moonshot reply in the 40 most recent discussions. Version policy earns 5 for a first release. File health earns 5, because a report that the tokeniser goes past the declared vocabulary waits without a reply.\n- WeVisOne-VL-4B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.\n\n### Agent interface: WeVisOne-VL-4B +5\n\n- Kimi K3 (65): The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run. The tool-calling format earns 10, since Moonshot documents the tools channel but the parser name comes only from vLLM's and SGLang's own recipes. Trained tool calling earns 20 for indexed parallel calls in one turn. Structured output earns 5, as the card names it among its linked guides without saying how to ask the weights for it, and the how sits in hosted-API docs. Reasoning control earns 10, with per-request effort documented on the card and reasoning returned as reasoning_content. Context earns 10 for 1,048,576 tokens trained in.\n- WeVisOne-VL-4B (70): Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.\n\n## Score by category\n\n| Category | Weight | Kimi K3 | WeVisOne-VL-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 70 | WeVisOne-VL-4B +20 |\n| Risk | 20% (20 this run) | 65 | 55 | Kimi K3 +10 |\n| Agent interface | 10% (10 this run) | 65 | 70 | WeVisOne-VL-4B +5 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 48 | WeVisOne-VL-4B +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Kimi K3 | WeVisOne-VL-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | Tencent (WeChat Vision) |\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 | Kimi K3 License (modified MIT) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-02 | 2026-10-27 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## Verdicts\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**WeVisOne-VL-4B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.\n\n## Before you call either\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### WeVisOne-VL-4B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`\n2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting\n3. A None answer means the target isn't in the image. Treat it as a result, not an error\n4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's\n5. Send one image a turn. Video grids are rejected by the decoder\n6. The card says it isn't for identifying individuals or for high-impact decisions on its own\n\n## Questions\n\n### Which is better for AI agents, Kimi K3 or WeVisOne-VL-4B?\n\nKimi K3 scores 62.4 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3\", \"b\": \"wevisone-vl-4b\"}`. From a terminal: `anchor compare kimi-k3 wevisone-vl-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json\n\n## Other comparisons with Kimi K3 or WeVisOne-VL-4B\n\n- [Agents-A1-4B vs Kimi K3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.md)\n- [Agents-A1-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b.md)\n- [Apertus v1.5 8B vs Kimi K3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.md)\n- [Apertus v1.5 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b.md)\n- [Apodex-1.1-mini vs Kimi K3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.md)\n- [Apodex-1.1-mini vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b.md)\n- [Arete-27B vs Kimi K3](https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.md)\n- 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