{ "data": { "a": { "slug": "kimi-k3-1-pro", "name": "Kimi K3.1 Pro", "vendor": "Moonshot AI", "vendorUrl": "https://www.moonshot.ai", "kind": "weights", "category": "open-weights", "summary": "Kimi K3.1 Pro is Moonshot AI's open-weight multimodal mixture-of-experts model, post-trained from Kimi K3, with 2.8T parameters, 104B active and a 1M-token context, published in MXFP4 under the modified-MIT Kimi K3.1 Pro Licence.", "url": "https://www.anchorterminal.com/tools/kimi-k3-1-pro", "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json", "dns": "kimi-k3-1-pro.grade.anchorterminal.com", "repo": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro", "license": "Kimi K3.1 Pro License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "moonshotai/Kimi-K3.1-Pro" } ], "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, commercial use included, under the Kimi K3.1 Pro 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 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "modified-mit", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2026-11-05", "graded": true, "anchor": { "graded": true, "score": 48.6, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 109, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 40, "risk": 50, "upkeep": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.", "bestFor": "Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API", "strengths": [ "Published weights for a model Moonshot calls its most capable, with a 1M-token context and images in", "Free commercial use under a modified MIT licence, ungated, with a hash on every file", "Moonshot's API and Together serve the same model for agents without the hardware", "reasoning_effort per request and several tool calls a turn", "vLLM and SGLang both publish serving recipes for it" ], "weaknesses": [ "1,561 GB of files, so it needs a node of eight large accelerators at least", "Only llama.cpp has the model code in a release, and no GGUF is published", "No chat template, and prompts are built by code from the repository", "No limitations, safety guidance or security contact on the card, and no independent safety evaluation yet", "None of the newest discussions has a reply from Moonshot" ], "agentNotes": [ "Use Moonshot's API or Together unless you have a node of eight B300-class accelerators", "With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`", "Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires", "Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off", "Kimi K3's model code doesn't load it, because the attention layers changed", "Validate JSON output yourself, since the card doesn't document structured output for the weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 48.6 } ], "editorialScores": { "interface": 65, "reach": 40, "risk": 50, "upkeep": 51 }, "provenanceScore": 70, "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": "# vLLM, per its Kimi-K3.1-Pro recipe linked from the card (vLLM 0.34.0 or later, two GB300 trays)\nvllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code \\\n --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP \\\n --load-format fastsafetensors --enable-auto-tool-choice \\\n --tool-call-parser kimi_k3 --reasoning-parser kimi_k3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"moonshotai/Kimi-K3.1-Pro\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"high\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kimi-k3-1-pro" }, "sameCompany": [ "kimi-k3" ], "weights": { "trainedBy": "Moonshot AI", "builtOn": "Kimi K3, post-trained, per the card. Not declared in the metadata", "params": "2.8T", "active": "104B", "context": 1048576, "goodFor": "Large multimodal agent model for coding, research and long tool-calling sessions, text and images in, with always-on reasoning you can budget.", "runtimes": [ "llama.cpp" ], "builds": [ { "name": "Kimi-K3.1-Pro", "url": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro", "who": "publisher", "precision": "mxfp4", "sizeGB": 1561.3, "memoryGB": 2048, "format": "safetensors", "note": "Trained in MXFP4, the only build Moonshot publishes. vLLM's recipe needs 0.34.0 or later" } ], "run": "vllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP --load-format fastsafetensors --enable-auto-tool-choice --tool-call-parser kimi_k3 --reasoning-parser kimi_k3", "runBuild": "Kimi-K3.1-Pro", "checked": "2026-11-10", "minMemoryGB": 2048 }, "family": { "lab": "Moonshot AI (Kimi)", "family": "Kimi K3.1", "url": "https://www.anchorterminal.com/open-weights/#moonshot", "olderCount": 18 }, "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 gave moonshot.ai a registration date of 15 December 2017, read 24 October 2026 for the Kimi K3 listing.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The licence names Moonshot AI. The Kimi Platform's terms name Moonshot AI PTE. LTD. as the party for its API, as the Kimi K3 listing records.", "Nothing is hosted, so the Kimi K3.1 Pro Licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, two commits, the first on 2 November 2026 and an update to encoding_k3.py on 5 November.", "No security.txt was found on moonshot.ai or kimi.com for the Kimi K3 listing on 24 October 2026. unchecked on 10 November." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.json" }, "answer": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.", "b": { "slug": "nanbeige-4-2-3b", "name": "Nanbeige4.2-3B", "vendor": "Nanbeige LLM Lab (BOSS Zhipin)", "vendorUrl": "https://huggingface.co/Nanbeige", "kind": "weights", "category": "open-weights", "summary": "Nanbeige4.2-3B is a small agent model from BOSS Zhipin's Nanbeige lab, 4.2B parameters with layers reused in a loop, with tool calling, a thinking switch and a 256K context. Apache 2.0. Released SGLang and llama.cpp run it.", "url": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b", "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json", "dns": "nanbeige-4-2-3b.grade.anchorterminal.com", "repo": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B" }, { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B-FP8" }, { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B-GPTQ-Int8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0, per the card's metadata. Running it yourself costs little: 5.2 GB in FP8 or 8-bit from Nanbeige, 8.3 GB in BF16, and 2.7 GB for bartowski's Q4_K_M conversion (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "small", "looped-transformer", "reasoning", "tool-calling", "agentic", "long-context", "apache-2.0", "safetensors", "fp8", "custom-code", "self-hosted" ], "lastRelease": "2026-09-11", "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": 62, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.", "bestFor": "Local agents that need tool calling and a long context on a laptop or a small GPU", "strengths": [ "Trained for multi-step tool use, with parsers in released SGLang", "A 256K context in a model that fits 8 GB", "Apache 2.0, ungated", "FP8 and 8-bit builds from Nanbeige", "Nanbeige replied in most of the recent discussions" ], "weaknesses": [ "Its own architecture: Transformers loads it only with the repository's code", "No GGUF from Nanbeige, so llama.cpp users rely on a third party's conversion", "The card's serve instructions still point at Nanbeige's forks", "No safety results on the card", "Training data described only in general terms", "Not changed since September" ], "agentNotes": [ "Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`", "Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`", "Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat", "Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card", "For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's", "If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it" ], "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": 85, "reach": 85, "risk": 55, "upkeep": 72 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige", "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. Pass tools in the request, and\n# chat_template_kwargs {\"enable_thinking\": false} for a direct answer" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nanbeige-4-2-3b" }, "sameCompany": [ "nanbeige-4-3-33b" ], "weights": { "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)", "builtOn": "Nanbeige4.2-3B-Base, per the card and the metadata", "params": "4.2B", "context": 262144, "goodFor": "A small local agent for tool use, coding and office tasks, with thinking you can turn off and a long context.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Nanbeige4.2-3B-FP8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 5.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Nanbeige's FP8 build" }, { "name": "Nanbeige4.2-3B-GPTQ-Int8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-GPTQ-Int8", "who": "publisher", "precision": "int8", "sizeGB": 5.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Nanbeige's 8-bit GPTQ build" }, { "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 2.68, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. Its base_model tag names Nanbeige/Nanbeige4.2-3B, from the Hub's model information on 10 November 2026" }, { "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 4.43, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. From the Hub's model information on 10 November 2026" }, { "name": "Nanbeige4.2-3B (BF16 safetensors)", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "who": "publisher", "precision": "bf16", "sizeGB": 8.34, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Two shards" } ], "run": "python -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige", "runBuild": "Nanbeige4.2-3B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Nanbeige LLM Lab (BOSS Zhipin)", "family": "Nanbeige4.2", "url": "https://www.anchorterminal.com/open-weights/#nanbeige", "others": [ { "name": "Nanbeige4.2-3B-Base", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base" } ], "olderCount": 7 }, "area": "models", "provenance": { "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed", "domain": "zhipin.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The publisher's identity, domain and security facts are carried from our Nanbeige4.3-33B listing: the lab's report for this model names Nanbeige LLM Lab, BOSS Zhipin, and the card's contact address is on kanzhun.com.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 29 commits from 21 July to 11 September 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.json" }, "edges": [ { "by": 45, "category": "reach", "leads": "nanbeige-4-2-3b", "name": "Reach", "reasons": { "a": "Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.", "b": "Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85." }, "scores": { "a": 40, "b": 85 } }, { "by": 21, "category": "upkeep", "leads": "nanbeige-4-2-3b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.", "b": "Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72." }, "scores": { "a": 51, "b": 72 } }, { "by": 20, "category": "interface", "leads": "nanbeige-4-2-3b", "name": "Agent interface", "reasons": { "a": "Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.", "b": "Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85." }, "scores": { "a": 65, "b": 85 } }, { "by": 5, "category": "risk", "leads": "nanbeige-4-2-3b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.", "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55." }, "scores": { "a": 50, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Moonshot AI", "b": "Nanbeige LLM Lab (BOSS Zhipin)", "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 K3.1 Pro License (modified MIT)", "b": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-05", "b": "2026-09-11", "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" } ], "faq": [ { "answer": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.", "question": "Which is better for AI agents, Kimi K3.1 Pro or Nanbeige4.2-3B?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API", "slug": "kimi-k3-1-pro", "watchFor": "1,561 GB of files, so it needs a node of eight large accelerators at least" }, { "aheadOn": [ "Reach, 85 against 40", "Risk, 55 against 50", "Agent interface, 85 against 65", "Documentation and upkeep, 72 against 51" ], "also": null, "goodFor": "Local agents that need tool calling and a long context on a laptop or a small GPU", "slug": "nanbeige-4-2-3b", "watchFor": "Its own architecture: Transformers loads it only with the repository's code" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro.json", "title": "Agents-A1-4B vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b.json", "title": "Agents-A1-4B vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.json", "title": "Apertus v1.5 8B vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.json", "title": "Apertus 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{ "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m2-7.json", "title": "Kimi K3.1 Pro vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m3-1-flash.json", "title": "Kimi K3.1 Pro vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4.json", "title": "Kimi K3.1 Pro vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-muse-glimmer-30b.json", "title": "Kimi K3.1 Pro vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-3-33b.json", "title": "Kimi K3.1 Pro vs 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"https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-32b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-4b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nanbeige4.2-3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint.json", "title": "Nanbeige4.2-3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ripple-65b-preview.json", "title": "Nanbeige4.2-3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-sensenova-loop-9b-preview.json", "title": "Nanbeige4.2-3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-smollm3-3b.json", "title": "Nanbeige4.2-3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-spark-x2-6-4b.json", "title": "Nanbeige4.2-3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-step-3-7-flash.json", "title": "Nanbeige4.2-3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nanbeige4.2-3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2.json", "title": "Nanbeige4.2-3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.json", "title": "Nanbeige4.2-3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b" }, { "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-4-2-3b-vs-zaya1-8b.json", "title": "Nanbeige4.2-3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 45, "edge": "nanbeige-4-2-3b", "key": "reach", "kimi-k3-1-pro": 40, "name": "Reach", "nanbeige-4-2-3b": 85, "weight": 25 }, { "by": 5, "edge": "nanbeige-4-2-3b", "key": "risk", "kimi-k3-1-pro": 50, "name": "Risk", "nanbeige-4-2-3b": 55, "weight": 20 }, { "by": 20, "edge": "nanbeige-4-2-3b", "key": "interface", "kimi-k3-1-pro": 65, "name": "Agent interface", "nanbeige-4-2-3b": 85, "weight": 10 }, { "by": 21, "edge": "nanbeige-4-2-3b", "key": "upkeep", "kimi-k3-1-pro": 51, "name": "Documentation and upkeep", "nanbeige-4-2-3b": 72, "weight": 10 } ], "summary": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "kimi-k3-1-pro": "Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.", "nanbeige-4-2-3b": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b", "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.min.md" }, "markdown": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Kimi K3.1 Pro: grade D, 48.6/100, rank #151 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3-1-pro.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json\n- Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.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 K3.1 Pro (D)\n\nGood for: Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API\n\nWatch for: 1,561 GB of files, so it needs a node of eight large accelerators at least\n\n### Nanbeige4.2-3B (C)\n\nGood for: Local agents that need tool calling and a long context on a laptop or a small GPU\n\nAhead on:\n- Reach, 85 against 40\n- Risk, 55 against 50\n- Agent interface, 85 against 65\n- Documentation and upkeep, 72 against 51\n\nWatch for: Its own architecture: Transformers loads it only with the repository's code\n\n\n## Why the edge\n\n### Reach: Nanbeige4.2-3B +45\n\n- Kimi K3.1 Pro (40): Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.\n- Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.\n\n### Documentation and upkeep: Nanbeige4.2-3B +21\n\n- Kimi K3.1 Pro (51): Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.\n- Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.\n\n### Agent interface: Nanbeige4.2-3B +20\n\n- Kimi K3.1 Pro (65): Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.\n- Nanbeige4.2-3B (85): Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n### Risk: Nanbeige4.2-3B +5\n\n- Kimi K3.1 Pro (50): File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.\n- Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.\n\n## Score by category\n\n| Category | Weight | Kimi K3.1 Pro | Nanbeige4.2-3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 85 | Nanbeige4.2-3B +45 |\n| Risk | 20% (20 this run) | 50 | 55 | Nanbeige4.2-3B +5 |\n| Agent interface | 10% (10 this run) | 65 | 85 | Nanbeige4.2-3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 51 | 72 | Nanbeige4.2-3B +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **48.6 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Kimi K3.1 Pro | Nanbeige4.2-3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | Nanbeige LLM Lab (BOSS Zhipin) |\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 K3.1 Pro License (modified MIT) | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-05 | 2026-09-11 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Kimi K3.1 Pro.** Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.\n\n**Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.\n\n## Before you call either\n\n### Kimi K3.1 Pro\n\n1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators\n2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`\n3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires\n4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off\n5. Kimi K3's model code doesn't load it, because the attention layers changed\n6. Validate JSON output yourself, since the card doesn't document structured output for the weights\n\n### Nanbeige4.2-3B\n\n1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`\n2. Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`\n3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat\n4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card\n5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's\n6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it\n\n## Questions\n\n### Which is better for AI agents, Kimi K3.1 Pro or Nanbeige4.2-3B?\n\nNanbeige4.2-3B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3-1-pro\", \"b\": \"nanbeige-4-2-3b\"}`. From a terminal: `anchor compare kimi-k3-1-pro nanbeige-4-2-3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json and https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json\n\n## Other comparisons with Kimi K3.1 Pro or Nanbeige4.2-3B\n\n- [Agents-A1-4B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro.md)\n- [Agents-A1-4B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b.md)\n- [Apertus v1.5 8B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.md)\n- [Apertus v1.5 8B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.md)\n- [Apodex-1.1-mini vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.md)\n- [Apodex-1.1-mini vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b.md)\n- [Arete-27B vs Kimi K3.1 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vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.md)\n- [Nanbeige4.2-3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.md)\n- [Nanbeige4.2-3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": 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