{ "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": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on risk.", "b": { "slug": "thinkingcap-qwen3-8-27b", "name": "ThinkingCap (Qwen3.8 27B)", "vendor": "BottleCap AI", "vendorUrl": "https://bottlecapai.com", "kind": "weights", "category": "open-weights", "summary": "ThinkingCap-Qwen3.8-27B is BottleCap AI's fine-tune of Alibaba's Qwen3.8-27B that shortens its reasoning traces, a 27B dense vision-language model on Hugging Face under PolyForm Small Business 1.0.0, with a gated main repository.", "url": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json", "dns": "thinkingcap-qwen3-8-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "license": "PolyForm-Small-Business-1.0.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-FP8" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw" } ], "auth": "none", "authNotes": "The BF16 and FP8 repositories are gated (gated auto). A Hugging Face account must accept the card's conditions and give a name, a company and an email before the files open, with automatic approval. The GGUF, NVFP4, NVFP4A4-AWQ, MLX and RCO GGUF repositories were not gated on 21 October 2026, though their cards carry the same request form in their metadata (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free for personal non-commercial use and for companies with fewer than 100 people and under 1,000,000 USD (2019, inflation-adjusted) revenue, under PolyForm Small Business 1.0.0 plus BottleCap's personal-use grant. Larger organisations need a separate commercial licence from BottleCap AI. Running it costs the hardware, 55.6 GB for BF16 and 15.5 GB for the smallest 4-bit GGUF (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": "2026-11-03" }, "docsUrl": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "derivative", "gated", "source-available", "safetensors", "gguf", "fp8", "nvfp4", "mlx", "self-hosted" ], "lastRelease": "2026-10-06", "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": 48, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 75, "risk": 45, "upkeep": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.", "bestFor": "Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.", "strengths": [ "Same architecture and template as Qwen3.8-27B, so released vLLM, SGLang, Transformers and llama.cpp run it", "BottleCap publishes FP8, NVFP4, MLX and GGUF builds, the IQ4_XS needing about 19 GB", "Per-request reasoning effort, several tool calls a turn and 262,144 tokens of context", "The card documents its own evaluation method with seeds, intervals and paired quantisation checks", "Safetensors only, in a verified organisation" ], "weaknesses": [ "PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue", "The BF16 and FP8 repositories are gated behind contact details", "No safety results, agent guidance, security contact or training-data description", "No hosted API or inference provider", "Every accuracy and token-saving figure comes from BottleCap's own harness" ], "agentNotes": [ "Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap", "For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB", "On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`", "Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error", "Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop", "The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 75, "risk": 45, "upkeep": 53 }, "provenanceScore": 49, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja\n# or the gated BF16 weights, after accepting the card's conditions:\nvllm serve bottlecapai/ThinkingCap-Qwen3.8-27B --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/thinkingcap-qwen3-8-27b" }, "weights": { "trainedBy": "BottleCap AI", "builtOn": "Qwen/Qwen3.8-27B (Apache 2.0), declared in the card's metadata (base_model, base_model_relation finetune) and in NOTICE as the base checkpoint. ThinkingCap's own weights are under PolyForm Small Business 1.0.0 with a personal-use grant, and NOTICE keeps Apache 2.0 for the upstream Qwen materials", "params": "27B", "context": 262144, "goodFor": "Qwen3.8-27B with shorter reasoning traces, for agent and reasoning work where thinking tokens cost time, with images and video in.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ThinkingCap-Qwen3.8-27B-IQ4_XS", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 15.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Not gated. Image input needs the 0.93 GB mmproj file from the same repository" }, { "name": "ThinkingCap-Qwen3.8-27B-Q4_K_M", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 17.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B-RCO-4.2bpw", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw", "who": "publisher", "precision": "mixed-4.2bpw", "sizeGB": 14.3, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Not gated. Mixed precision averaging 4.19 bits per weight, with the feed-forward weights at 3.8 to 3.9 bits, so it doesn't count as a 4-bit build for the memory line" }, { "name": "ThinkingCap-Qwen3.8-27B-NVFP4", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 20.6, "memoryGB": 32, "format": "safetensors", "note": "Not gated. Weight-only NVFP4 for vLLM on Hopper (Marlin) and Blackwell" }, { "name": "ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.4, "memoryGB": 32, "format": "safetensors", "note": "Not gated. NVFP4 weights and activations, Blackwell only" }, { "name": "ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ", "who": "publisher", "precision": "mlx-4bit", "sizeGB": 22.8, "memoryGB": 32, "format": "mlx", "runsOn": [ "mlx-vlm" ], "note": "Not gated. Mixed 4 and 8-bit DWQ with the vision tower and MTP head (22.0 GB without the MTP files). The card says to use mlx-vlm or oMLX" }, { "name": "ThinkingCap-Qwen3.8-27B-Q6_K", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 23.9, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B-FP8", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 31.2, "memoryGB": 48, "format": "safetensors", "note": "Gated like the main repository. Block-wise FP8 for vLLM on Hopper and Blackwell, per the card" }, { "name": "ThinkingCap-Qwen3.8-27B-Q8_0", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 29, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "who": "publisher", "precision": "bf16", "sizeGB": 55.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "Gated (auto approval after accepting the card's conditions)" }, { "name": "ThinkingCap-Qwen3.8-27B-f16", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-f16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 54.7, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] } ], "run": "llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja", "runBuild": "ThinkingCap-Qwen3.8-27B-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "BottleCap AI (bottlecapai)", "family": "ThinkingCap", "url": "https://www.anchorterminal.com/open-weights/#bottlecap", "others": [ { "name": "ThinkingCap-Qwen3.6-27B", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B", "params": "27B (27,356,728,560 by the Hub's safetensors count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "BottleCap AI (named in the licence's copyright line)", "domain": "bottlecapai.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://bottlecapai.com/privacy-policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-03", "notes": [ "The Hugging Face organisation bottlecapai is verified (team plan, 14 members on 3 November 2026). BottleCap's blog post for this model links the repository.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "bottlecapai.com/.well-known/security.txt returns 404 (re-read 3 November 2026). A trust centre at trust.mycroft.io/bottlecap-ai lists SOC 2 and policies behind a request form, with no route for reporting a flaw in the model.", "unchecked: the domain's registration date, and the main repository's commit history, which is behind the gate." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.json", "live": { "slug": "thinkingcap-qwen3-8-27b", "securityTxt": { "url": "https://bottlecapai.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:23.314260227Z" }, "domain": { "domain": "bottlecapai.com", "registered": "2025-02-06", "source": "https://rdap.verisign.com/com/v1/domain/bottlecapai.com", "checkedAt": "2026-11-08T14:51:11.863400113Z" }, "pages": [ { "url": "https://bottlecapai.com/privacy-policy/", "kind": "privacy", "status": 200, "checkedAt": "2026-11-10T16:10:53.84275598Z", "changedAt": "2026-10-31T06:05:10.398956441Z", "fingerprint": "780a269ec4e7" } ], "updatedAt": "2026-11-10T16:10:53.84275598Z" } }, "edges": [ { "by": 35, "category": "reach", "leads": "thinkingcap-qwen3-8-27b", "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 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps the base's qwen3_5 architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, while Ollama's entry is the base's. Builds 15, FP8, NVFP4, MLX and GGUF from BottleCap. Terms 10, PolyForm Small Business is free commercially only below 100 people and 1,000,000 USD (2019) revenue. Download 5, the main repository is gated behind a click-through with an account and contact details, though the GGUF, NVFP4 and MLX builds aren't. Hosted 0, no API or inference provider serves it." }, "scores": { "a": 40, "b": 75 } }, { "by": 5, "category": "risk", "leads": "kimi-k3-1-pro", "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 25, safetensors only and no code in the repository. Provenance 10 of 15, the files are in BottleCap's verified organisation, but the Hub hides the gated files' SHA-256 from the public, so checksums couldn't be confirmed. Safety evaluation 0, none published. Agent guidance 0. Licence risk 10, 5 off for the size and revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact." }, "scores": { "a": 50, "b": 45 } }, { "by": 5, "category": "interface", "leads": "thinkingcap-qwen3-8-27b", "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, the same Jinja template as Qwen3.8-27B, in the repository. Tool-calling format 10 of 25, the card names the parsers but the format lives only in the template. Trained tool calling 20, several calls a turn, and BottleCap evaluated agent tasks. Structured output 0, not documented. Reasoning control 10, reasoning_effort budgets thinking per request and the card says where the reasoning comes back. Context 10, 262,144 tokens." }, "scores": { "a": 65, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Moonshot AI", "b": "BottleCap 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": "Kimi K3.1 Pro License (modified MIT)", "b": "PolyForm-Small-Business-1.0.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-05", "b": "2026-10-06", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "2026-01-21", "name": "Privacy policy last updated" }, { "a": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on risk.", "question": "Which is better for AI agents, Kimi K3.1 Pro or ThinkingCap (Qwen3.8 27B)?" } ], "goodFor": [ { "aheadOn": [ "Risk, 50 against 45" ], "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, 75 against 40", "Agent interface, 70 against 65" ], "also": null, "goodFor": "Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.", "slug": "thinkingcap-qwen3-8-27b", "watchFor": "PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { 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"url": "https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3-1-pro" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b.json", "title": "Inkling-Small vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3-1-pro.json", "title": "Inkling vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3-1-pro" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b.json", "title": "Inkling vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3-1-pro.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3.1 Pro", "url": 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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-4-2-3b.json", "title": "Kimi K3.1 Pro vs 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"https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-2B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-32B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b.json", "title": "Recursive Flint vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.json", "title": "Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b.json", "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b.json", "title": "ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 35, "edge": "thinkingcap-qwen3-8-27b", "key": "reach", "kimi-k3-1-pro": 40, "name": "Reach", "thinkingcap-qwen3-8-27b": 75, "weight": 25 }, { "by": 5, "edge": "kimi-k3-1-pro", "key": "risk", "kimi-k3-1-pro": 50, "name": "Risk", "thinkingcap-qwen3-8-27b": 45, "weight": 20 }, { "by": 5, "edge": "thinkingcap-qwen3-8-27b", "key": "interface", "kimi-k3-1-pro": 65, "name": "Agent interface", "thinkingcap-qwen3-8-27b": 70, "weight": 10 }, { "by": 2, "edge": "thinkingcap-qwen3-8-27b", "key": "upkeep", "kimi-k3-1-pro": 51, "name": "Documentation and upkeep", "thinkingcap-qwen3-8-27b": 53, "weight": 10 } ], "summary": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on risk. 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.", "thinkingcap-qwen3-8-27b": "A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b", "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.min.md" }, "markdown": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on risk. 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- ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #58 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.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\nAhead on:\n- Risk, 50 against 45\n\nWatch for: 1,561 GB of files, so it needs a node of eight large accelerators at least\n\n### ThinkingCap (Qwen3.8 27B) (C)\n\nGood for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.\n\nAhead on:\n- Reach, 75 against 40\n- Agent interface, 70 against 65\n\nWatch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue\n\n\n## Why the edge\n\n### Reach: ThinkingCap (Qwen3.8 27B) +35\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- ThinkingCap (Qwen3.8 27B) (75): Memory 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps the base's qwen3_5 architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, while Ollama's entry is the base's. Builds 15, FP8, NVFP4, MLX and GGUF from BottleCap. Terms 10, PolyForm Small Business is free commercially only below 100 people and 1,000,000 USD (2019) revenue. Download 5, the main repository is gated behind a click-through with an account and contact details, though the GGUF, NVFP4 and MLX builds aren't. Hosted 0, no API or inference provider serves it.\n\n### Risk: Kimi K3.1 Pro +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- ThinkingCap (Qwen3.8 27B) (45): File safety 25, safetensors only and no code in the repository. Provenance 10 of 15, the files are in BottleCap's verified organisation, but the Hub hides the gated files' SHA-256 from the public, so checksums couldn't be confirmed. Safety evaluation 0, none published. Agent guidance 0. Licence risk 10, 5 off for the size and revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact.\n\n### Agent interface: ThinkingCap (Qwen3.8 27B) +5\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- ThinkingCap (Qwen3.8 27B) (70): Chat template 20, the same Jinja template as Qwen3.8-27B, in the repository. Tool-calling format 10 of 25, the card names the parsers but the format lives only in the template. Trained tool calling 20, several calls a turn, and BottleCap evaluated agent tasks. Structured output 0, not documented. Reasoning control 10, reasoning_effort budgets thinking per request and the card says where the reasoning comes back. Context 10, 262,144 tokens.\n\n## Score by category\n\n| Category | Weight | Kimi K3.1 Pro | ThinkingCap (Qwen3.8 27B) | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 75 | ThinkingCap (Qwen3.8 27B) +35 |\n| Risk | 20% (20 this run) | 50 | 45 | Kimi K3.1 Pro +5 |\n| Agent interface | 10% (10 this run) | 65 | 70 | ThinkingCap (Qwen3.8 27B) +5 |\n| Documentation and upkeep | 10% (10 this run) | 51 | 53 | ThinkingCap (Qwen3.8 27B) +2 |\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 | ThinkingCap (Qwen3.8 27B) |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | BottleCap 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 | Kimi K3.1 Pro License (modified MIT) | PolyForm-Small-Business-1.0.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-05 | 2026-10-06 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | 2026-01-21 |\n| Agent reviews | none | 2.5/5 (4) |\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**ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.\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### ThinkingCap (Qwen3.8 27B)\n\n1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap\n2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB\n3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`\n4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error\n5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop\n6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor\n\n## Questions\n\n### Which is better for AI agents, Kimi K3.1 Pro or ThinkingCap (Qwen3.8 27B)?\n\nThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3-1-pro\", \"b\": \"thinkingcap-qwen3-8-27b\"}`. From a terminal: `anchor compare kimi-k3-1-pro thinkingcap-qwen3-8-27b`\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/thinkingcap-qwen3-8-27b.json\n\n## Other comparisons with Kimi K3.1 Pro or ThinkingCap (Qwen3.8 27B)\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 ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/agents-a1-4b-vs-thinkingcap-qwen3-8-27b.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 ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b.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 ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-thinkingcap-qwen3-8-27b.md)\n- [Arete-27B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.md)\n- [Arete-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b.md)\n- [BigBang-v1 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3-1-pro.md)\n- [BigBang-v1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b.md)\n- [Carbon-X vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3-1-pro.md)\n- [Carbon-X vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/carbon-x-vs-thinkingcap-qwen3-8-27b.md)\n- [Cola2 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro.md)\n- [Cola2 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/cola2-vs-thinkingcap-qwen3-8-27b.md)\n- [Cosmos-Reason2-2B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3-1-pro.md)\n- [Cosmos-Reason2-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-thinkingcap-qwen3-8-27b.md)\n- [Darwin-36B-Opus vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3-1-pro.md)\n- [Darwin-36B-Opus vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-thinkingcap-qwen3-8-27b.md)\n- [DeepSeek-V3.2 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3-1-pro.md)\n- [DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-thinkingcap-qwen3-8-27b.md)\n- [DeepSeek V4.1 Flash vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3-1-pro.md)\n- [DeepSeek V4.1 Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [DeepSeek-V4-Flash-0731 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3-1-pro.md)\n- [DeepSeek-V4-Flash-0731 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-thinkingcap-qwen3-8-27b.md)\n- [Devstral Small 2 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/devstral-small-2-vs-kimi-k3-1-pro.md)\n- [Devstral Small 2 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/devstral-small-2-vs-thinkingcap-qwen3-8-27b.md)\n- [DiffusionGemma 26B A4B IT vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3-1-pro.md)\n- [DiffusionGemma 26B A4B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [EXAONE 4.5 33B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kimi-k3-1-pro.md)\n- [EXAONE 4.5 33B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 12B IT vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3-1-pro.md)\n- [Gemma 4 12B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 26B A4B IT vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3-1-pro.md)\n- [Gemma 4 26B A4B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3-1-pro.md)\n- [Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3-1-pro.md)\n- [Gemma 4 E2B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 E4B IT vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3-1-pro.md)\n- [Gemma 4 E4B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-4.5-Air vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3-1-pro.md)\n- [GLM-4.5-Air vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-4-5-air-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-4.7-Flash vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3-1-pro.md)\n- [GLM-4.7-Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3-1-pro.md)\n- [GLM-5.3-Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-5.3 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3-1-pro.md)\n- [GLM-5.3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-vs-thinkingcap-qwen3-8-27b.md)\n- [gpt-oss-120b vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3-1-pro.md)\n- [gpt-oss-120b vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b.md)\n- [gpt-oss-20b vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3-1-pro.md)\n- [gpt-oss-20b vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-thinkingcap-qwen3-8-27b.md)\n- [Granite 4.2 30B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kimi-k3-1-pro.md)\n- [Granite 4.2 30B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-thinkingcap-qwen3-8-27b.md)\n- [Granite 4.2 3B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3-1-pro.md)\n- [Granite 4.2 3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-thinkingcap-qwen3-8-27b.md)\n- [Granite 4.2 8B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kimi-k3-1-pro.md)\n- [Granite 4.2 8B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-thinkingcap-qwen3-8-27b.md)\n- [Hy-Memory-30B-A3B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-kimi-k3-1-pro.md)\n- [Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b.md)\n- [Hy3 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/hy3-vs-kimi-k3-1-pro.md)\n- [Hy3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/hy3-vs-thinkingcap-qwen3-8-27b.md)\n- [Inkling-Small vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3-1-pro.md)\n- [Inkling-Small vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b.md)\n- [Inkling vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/inkling-vs-kimi-k3-1-pro.md)\n- [Inkling vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3-1-pro.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.md)\n- [Kimi K3.1 Pro vs Kolibri-1](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-kolibri-1.md)\n- [Kimi K3.1 Pro vs Kronos](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-kronos.md)\n- [Kimi K3.1 Pro vs Laguna M.1](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-m-1.md)\n- [Kimi K3.1 Pro vs Laguna S 2.1](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-s-2-1.md)\n- [Kimi K3.1 Pro vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-xs-2-1.md)\n- [Kimi K3.1 Pro vs LBS-27B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lbs-27b.md)\n- [Kimi K3.1 Pro vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm2-5-2-6b.md)\n- [Kimi K3.1 Pro vs LFM3-1.7B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-1-7b.md)\n- [Kimi K3.1 Pro vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-audio-2b.md)\n- [Kimi K3.1 Pro vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-omni-2b.md)\n- [Kimi K3.1 Pro vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-vl-2b.md)\n- [Kimi K3.1 Pro vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ling-3-0-tiny.md)\n- [Kimi K3.1 Pro vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ling-3-1-flash-1019.md)\n- [Kimi K3.1 Pro vs LongCat-2.5](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-longcat-2-5.md)\n- [Kimi K3.1 Pro vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Kimi K3.1 Pro vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl.md)\n- [Kimi K3.1 Pro vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-o-4-5.md)\n- [Kimi K3.1 Pro vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-1b.md)\n- [Kimi K3.1 Pro vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-35b-a3b.md)\n- [Kimi K3.1 Pro vs MiniCPM5-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm5-2b.md)\n- [Kimi K3.1 Pro vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m2-5.md)\n- [Kimi K3.1 Pro vs MiniMax-M2.7](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m2-7.md)\n- [Kimi K3.1 Pro vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m3-1-flash.md)\n- [Kimi K3.1 Pro vs Mistral Large 4](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4.md)\n- [Kimi K3.1 Pro vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-muse-glimmer-30b.md)\n- [Kimi K3.1 Pro vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.md)\n- [Kimi K3.1 Pro vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-3-33b.md)\n- [Kimi K3.1 Pro vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b.md)\n- [Kimi K3.1 Pro vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-4b.md)\n- [Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-super-120b-a12b.md)\n- [Kimi K3.1 Pro vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-ultra-550b-a55b.md)\n- [Kimi K3.1 Pro vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.md)\n- [Kimi K3.1 Pro vs occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0.md)\n- [Kimi K3.1 Pro vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think.md)\n- [Kimi K3.1 Pro vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b.md)\n- [Kimi K3.1 Pro vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b.md)\n- [Kimi K3.1 Pro vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-397b.md)\n- [Kimi K3.1 Pro vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-9b.md)\n- [Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.md)\n- [Kimi K3.1 Pro vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Kimi K3.1 Pro vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-4b-instruct-2507.md)\n- [Kimi K3.1 Pro vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b.md)\n- [Kimi K3.1 Pro vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b.md)\n- [Kimi K3.1 Pro vs Qwen3.5-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-2b.md)\n- [Kimi K3.1 Pro vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b.md)\n- [Kimi K3.1 Pro vs 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