{ "data": { "a": { "slug": "granite-4-2-3b", "name": "Granite 4.2 3B", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "open-weights", "summary": "Granite 4.2 3B is IBM's open-weight dense reasoning model of 3.7B parameters, post-trained from Granite 4.1 3B Base, with switchable thinking, tool calling and a 131,072-token context, published in BF16, FP8, FP4, GGUF and MLX under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/granite-4-2-3b", "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json", "repo": "https://huggingface.co/ibm-granite/granite-4.2-3b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-GGUF" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-fp8" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-mxfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-nvfp4" }, { "registry": "huggingface", "name": "ibm-granite/granite-4.2-3b-q4-mlx" }, { "registry": "ollama", "name": "granite4.2:3b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of IBM's 4.2 3B repositories is gated (checked 2026-10-21). The files carry a sigstore signature (model.sig) that IBM documents how to verify.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. watsonx.ai lists Granite 4.1 models but not 4.2, and DeepInfra is the only inference provider Hugging Face maps for it. Running it yourself costs the hardware, 7.3 GB for the BF16 files and 2.2 GB for Ollama's Q4_K_M build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/ibm-granite/granite-4.2-3b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "reasoning", "tool-calling", "multilingual", "apache-2.0", "safetensors", "fp8", "fp4", "gguf", "mlx", "signed", "self-hosted" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 59.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 49, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 21, "interface": 85, "reach": 95, "risk": 55, "upkeep": 92 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.", "bestFor": "Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off", "strengths": [ "Apache 2.0 with nothing added, ungated, signed with sigstore in IBM's own organisation", "2.2 GB as Ollama's Q4_K_M build, with IBM's own FP8, FP4, GGUF and MLX builds", "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Tool format and parser documented for vLLM and SGLang, with several calls a turn", "Thinking that turns off or runs at low effort per request", "A machine-readable disclosure of the training datasets with sources and sizes" ], "weaknesses": [ "No safety results published for 4.2, and only general safety cautions", "No documented JSON or schema output", "The card's vLLM route loads a parser plugin from the repository", "Only one inference provider, and watsonx.ai doesn't list 4.2", "No knowledge cut-off, and Granite 4.1 3B carries no note that 4.2 replaced it" ], "agentNotes": [ "Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072", "With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed", "With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content", "Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires", "Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server", "Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history", "For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2", "Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.8 } ], "editorialScores": { "capability": 21, "interface": 85, "reach": 95, "risk": 55, "upkeep": 92 }, "provenanceScore": 87, "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": "ollama run granite4.2:3b\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-3b --max-model-len 131072 \\\n --reasoning-parser granite_thinking_parser \\\n --tool-call-parser qwen3_coder --enable-auto-tool-choice", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"granite4.2:3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/granite-4-2-3b" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "granite-switch" ], "weights": { "trainedBy": "IBM", "builtOn": "ibm-granite/granite-4.1-3b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)", "params": "3.66B", "context": 131072, "goodFor": "Small dense reasoning model for tool calling, coding and multilingual chat, with thinking that is on, off or short per request.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "granite-4.2-3b-fp8", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 4.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "granite-4.2-3b-nvfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-nvfp4", "who": "publisher", "precision": "nvfp4", "sizeGB": 2.81, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ calibrated with LLM Compressor, per IBM's blog" }, { "name": "granite-4.2-3b-mxfp4", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 2.71, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ calibrated with LLM Compressor, per IBM's blog" }, { "name": "granite-4.2-3b-Q8_0", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/blob/main/granite-4.2-3b-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 3.89, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "granite-4.2-3b-Q4_K_M", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/blob/main/granite-4.2-3b-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.24, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "IBM's GGUF repository also holds Q2_K to Q6_K, Q4_0, Q4_1, Q5_0, Q5_1 and BF16 files; Q4_0 is the smallest 4-bit GGUF at 2.13 GB" }, { "name": "granite-4.2-3b-q4-mlx", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-q4-mlx", "who": "publisher", "precision": "q4", "sizeGB": 2.07, "memoryGB": 8, "format": "mlx", "runsOn": [ "mlx-lm" ], "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (3.90 GB) and bf16-mlx (7.33 GB)" }, { "name": "ollama granite4.2:3b", "url": "https://ollama.com/library/granite4.2:3b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2.2, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M, 3.66B parameters, granite architecture, per Ollama's model page (digest 40577dc168a3). Size as Ollama shows it" }, { "name": "granite-4.2-3b", "url": "https://huggingface.co/ibm-granite/granite-4.2-3b", "who": "publisher", "precision": "bf16", "sizeGB": 7.33, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "ollama run granite4.2:3b", "runBuild": "ollama granite4.2:3b", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "IBM (Granite)", "family": "Granite 4 (4.0, 4.1, 4.2)", "url": "https://www.anchorterminal.com/open-weights/#ibm", "others": [ { "name": "granite-4.2-8b", "url": "https://huggingface.co/ibm-granite/granite-4.2-8b", "params": "8B (Hub safetensors count 8.79B)" }, { "name": "granite-4.2-30b", "url": "https://huggingface.co/ibm-granite/granite-4.2-30b", "params": "30B (Hub safetensors count 29.28B)" }, { "name": "granite-4.1-8b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-8b-base", "params": "8B (Hub safetensors count 8.38B)" }, { "name": "granite-4.1-8b", "url": "https://huggingface.co/ibm-granite/granite-4.1-8b", "params": "8B (Hub safetensors count 8.79B)" }, { "name": "granite-4.1-3b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-3b-base", "params": "3B (Hub safetensors count 3.40B)" }, { "name": "granite-4.1-3b", "url": "https://huggingface.co/ibm-granite/granite-4.1-3b", "params": "3B (Hub safetensors count 3.40B)" }, { "name": "granite-4.1-30b-base", "url": "https://huggingface.co/ibm-granite/granite-4.1-30b-base", "params": "30B (Hub safetensors count 28.87B)" }, { "name": "granite-4.1-30b", "url": "https://huggingface.co/ibm-granite/granite-4.1-30b", "params": "30B (Hub safetensors count 28.87B)" }, { "name": "granite-4.0-h-350m-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m-base", "params": "350M (Hub safetensors count 340M)" }, { "name": "granite-4.0-h-350m", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m", "params": "350M (Hub safetensors count 340M)" }, { "name": "granite-4.0-h-1b-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b-base", "params": "1B (Hub safetensors count 1.46B)" }, { "name": "granite-4.0-h-1b", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b", "params": "1B (Hub safetensors count 1.46B)" }, { "name": "granite-4.0-350m-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-350m-base", "params": "350M (Hub safetensors count 352M)" }, { "name": "granite-4.0-350m", "url": "https://huggingface.co/ibm-granite/granite-4.0-350m", "params": "350M (Hub safetensors count 352M)" }, { "name": "granite-4.0-1b-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-1b-base", "params": "1B (Hub safetensors count 1.63B)" }, { "name": "granite-4.0-1b", "url": "https://huggingface.co/ibm-granite/granite-4.0-1b", "params": "1B (Hub safetensors count 1.63B)" }, { "name": "granite-4.0-micro-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-micro-base", "params": "3.40B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-micro", "url": "https://huggingface.co/ibm-granite/granite-4.0-micro", "params": "3.40B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-tiny-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny-base", "params": "6.94B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-tiny", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny", "params": "6.94B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-small-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small-base", "params": "32.21B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-small", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small", "params": "32.21B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-micro-base", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro-base", "params": "3.19B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-h-micro", "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro", "params": "3.19B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-tiny-preview", "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-preview", "params": "6.67B (Hub safetensors count; the name states none)" }, { "name": "granite-4.0-tiny-base-preview", "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview", "params": "6.67B (Hub safetensors count; the name states none)" } ], "olderCount": 64, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)", "domain": "ibm.com", "domainRegistered": "1986-03-19", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ibm-granite/granite-4.2-3b/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "IBM's Granite docs page for 4.2 links the ibm-granite collection on Hugging Face and the GitHub repository, and the docs site says it is no longer updated. The domain date is carried from the granite-guardian check.", "The ibm-granite organisation (209 members) isn't verified on the Hub, which the calibration doesn't require.", "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, 15 commits from 7 August to 20 October 2026, most titled only \"granite-4.2\".", "www.ibm.com/.well-known/security.txt is present with PSIRT contacts and expires on 21 November 2026.", "Released on 25 August 2026 by the card and IBM's blog, which firstReleased uses. The repository and its weight files date from 7 August 2026, the date BATCH.md and the IBM family page used before 21 October." ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.json" }, "answer": "Kimi K3 scores 62.4 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 2 of 5 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep.", "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": "IBM", "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", "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": "2026-10-20", "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": "3.8/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Kimi K3 scores 62.4 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 2 of 5 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Granite 4.2 3B or Kimi K3?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 50", "Agent interface, 85 against 65", "Documentation and upkeep, 92 against 41" ], "also": [ "Rated higher by the reviewer agents, 3.8 against 2.3 out of 5" ], "goodFor": "Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off", "slug": "granite-4-2-3b", "watchFor": "No safety results published for 4.2, and only general safety cautions" }, { "aheadOn": [ "Capability, 75 against 21", "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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.json", "title": "Agents-A1-4B vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b" }, { "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-granite-4-2-3b.json", "title": "Apertus v1.5 8B vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b" }, { "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-granite-4-2-3b.json", "title": "Apodex-1.1-mini vs 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"https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-5.json", "title": "Granite 4.2 3B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-7.json", "title": "Granite 4.2 3B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-muse-glimmer-30b.json", "title": "Granite 4.2 3B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Granite 4.2 3B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3.json", "title": "Inkling vs Kimi K3", "url": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3" }, { "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": 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"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": [ { "by": 54, "edge": "kimi-k3", "granite-4-2-3b": 21, "key": "capability", "kimi-k3": 75, "name": "Capability", "weight": 35 }, { "by": 45, "edge": "granite-4-2-3b", "granite-4-2-3b": 95, "key": "reach", "kimi-k3": 50, "name": "Reach", "weight": 25 }, { "by": 10, "edge": "kimi-k3", "granite-4-2-3b": 55, "key": "risk", "kimi-k3": 65, "name": "Risk", "weight": 20 }, { "by": 20, "edge": "granite-4-2-3b", "granite-4-2-3b": 85, "key": "interface", "kimi-k3": 65, "name": "Agent interface", "weight": 10 }, { "by": 51, "edge": "granite-4-2-3b", "granite-4-2-3b": 92, "key": "upkeep", "kimi-k3": 41, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Kimi K3 scores 62.4 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 2 of 5 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "granite-4-2-3b": "A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.", "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/granite-4-2-3b-vs-kimi-k3", "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.min.md" }, "markdown": "Kimi K3 scores 62.4 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 2 of 5 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Granite 4.2 3B: grade C, 59.8/100, rank #57 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.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### Granite 4.2 3B (C)\n\nGood for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off\n\nAhead on:\n- Reach, 95 against 50\n- Agent interface, 85 against 65\n- Documentation and upkeep, 92 against 41\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5\n\nWatch for: No safety results published for 4.2, and only general safety cautions\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- Capability, 75 against 21\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\n## Score by category\n\n| Category | Weight | Granite 4.2 3B | Kimi K3 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 21 | 75 | Kimi K3 +54 |\n| Reach | 25% (25 this run) | 95 | 50 | Granite 4.2 3B +45 |\n| Risk | 20% (20 this run) | 55 | 65 | Kimi K3 +10 |\n| Agent interface | 10% (10 this run) | 85 | 65 | Granite 4.2 3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 41 | Granite 4.2 3B +51 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.8 · C** | **62.4 · B** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 3B | Kimi K3 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | 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 | Kimi K3 License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 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 | 3.8/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Granite 4.2 3B.** A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.\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### Granite 4.2 3B\n\n1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072\n2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed\n3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content\n4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires\n5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server\n6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history\n7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2\n8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide\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, Granite 4.2 3B or Kimi K3?\n\nKimi K3 scores 62.4 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 2 of 5 scored categories. Granite 4.2 3B 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/granite-4-2-3b-vs-kimi-k3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-3b\", \"b\": \"kimi-k3\"}`. From a terminal: `anchor compare granite-4-2-3b kimi-k3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/kimi-k3.json\n\n## Other comparisons with Granite 4.2 3B or Kimi K3\n\n- [Agents-A1-4B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.md)\n- [Agents-A1-4B vs Kimi K3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.md)\n- [Apertus v1.5 8B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.md)\n- [Apertus v1.5 8B vs Kimi K3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.md)\n- [Apodex-1.1-mini vs Granite 4.2 3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.md)\n- [Apodex-1.1-mini vs Kimi K3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.md)\n- [BigBang-v1 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b.md)\n- [BigBang-v1 vs Kimi K3](https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.md)\n- [Cosmos-Reason2-2B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-3b.md)\n- [Cosmos-Reason2-2B vs Kimi K3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3.md)\n- [Darwin-36B-Opus vs Granite 4.2 3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-granite-4-2-3b.md)\n- [Darwin-36B-Opus vs Kimi K3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3.md)\n- [DeepSeek-V3.2 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.md)\n- [DeepSeek-V3.2 vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3.md)\n- [DeepSeek V4.1 Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-3b.md)\n- [DeepSeek V4.1 Flash vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.md)\n- [DeepSeek-V4-Flash-0731 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-granite-4-2-3b.md)\n- [DeepSeek-V4-Flash-0731 vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3.md)\n- [DiffusionGemma 26B A4B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-granite-4-2-3b.md)\n- [DiffusionGemma 26B A4B IT vs Kimi K3](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3.md)\n- [Gemma 4 12B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 12B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.md)\n- [Gemma 4 26B A4B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 26B A4B IT vs Kimi 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Kolibri-1](https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1.md)\n- [Kimi K3 vs Laguna M.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.md)\n- [Kimi K3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.md)\n- [Kimi K3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.md)\n- [Kimi K3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.md)\n- [Kimi K3 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.md)\n- [Kimi K3 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Kimi K3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.md)\n- [Kimi K3 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b.md)\n- [Kimi K3 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5.md)\n- [Kimi K3 vs 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occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.md)\n- [Kimi K3 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kimi-k3-vs-olmo-3-7b-think.md)\n- [Kimi K3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.md)\n- [Kimi K3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b.md)\n- [Kimi K3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.md)\n- [Kimi K3 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Kimi K3 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-4b-instruct-2507.md)\n- [Kimi K3 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b.md)\n- [Kimi K3 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b.md)\n- [Kimi K3 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-2b.md)\n- [Kimi K3 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b.md)\n- [Kimi K3 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-4b.md)\n- [Kimi K3 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b.md)\n- [Kimi K3 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-27b.md)\n- [Kimi K3 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.md)\n- [Kimi K3 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.md)\n- [Kimi K3 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-flash-next.md)\n- [Kimi K3 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next.md)\n- [Kimi K3 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-2b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-32b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct.md)\n- [Kimi K3 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2.md)\n- [Kimi K3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/kimi-k3-vs-smollm3-3b.md)\n- [Kimi K3 vs 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research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Granite 4.2 3B vs Kimi K3", "url": "" } ], "description": "Kimi K3 scores 62.4 (B) to Granite 4.2 3B's 59.8 (C) for open-weight models you run yourself. 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