{ "data": { "a": { "slug": "arete-27b", "name": "Arete-27B", "vendor": "Nous Research", "vendorUrl": "https://nousresearch.com", "kind": "weights", "category": "open-weights", "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/arete-27b", "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json", "dns": "arete-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/NousResearch/Arete-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "NousResearch/Arete-27B" } ], "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 Apache 2.0, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (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/NousResearch/Arete-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "fine-tune", "qwen", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 55.1, "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": 92, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "strengths": [ "The training pairs and the evaluation set are both published", "Apache 2.0, ungated, safetensors only, no remote code", "The base's architecture, so vLLM, SGLang and Transformers run it from their releases", "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF", "Nous Research reports no meaningful loss against the base on its agent benchmarks" ], "weaknesses": [ "No quantised build from Nous Research, so the GGUF route depends on a third party", "The card doesn't document tool calling, structured output or how to switch thinking", "No limitations section, safety results or security contact", "None of the 13 discussions has a reply from Nous Research", "The results are Nous Research's own evaluation and nobody else has rerun it yet" ], "agentNotes": [ "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM", "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's", "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off", "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for", "Validate JSON output yourself, since structured output isn't documented" ], "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": 55.1 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "provenanceScore": 61, "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 serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/arete-27b" }, "sameCompany": [ "hermes-agent" ], "weights": { "trainedBy": "Nous Research", "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher", "params": "27.4B", "context": 262144, "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 18.13, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 29.6, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Arete-27B", "url": "https://huggingface.co/NousResearch/Arete-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Nous Research publishes" } ], "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Nous Research (NousResearch)", "family": "Arete", "url": "https://www.anchorterminal.com/open-weights/#nous-research" }, "area": "models", "provenance": { "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)", "domain": "nousresearch.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, eight commits on 6 November 2026, the upload and updates to the card.", "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.", "unchecked: the domain's registration date." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json" }, "answer": "Arete-27B scores 55.1 (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 agent interface.", "b": { "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" }, "edges": [ { "by": 12, "category": "upkeep", "leads": "arete-27b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.", "b": "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." }, "scores": { "a": 63, "b": 51 } }, { "by": 10, "category": "reach", "leads": "arete-27b", "name": "Reach", "reasons": { "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.", "b": "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." }, "scores": { "a": 50, "b": 40 } }, { "by": 5, "category": "risk", "leads": "arete-27b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "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." }, "scores": { "a": 55, "b": 50 } }, { "by": 5, "category": "interface", "leads": "kimi-k3-1-pro", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "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." }, "scores": { "a": 60, "b": 65 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nous Research", "b": "Moonshot AI", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Kimi K3.1 Pro License (modified MIT)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-11-05", "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": "Arete-27B scores 55.1 (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 agent interface.", "question": "Which is better for AI agents, Arete-27B or Kimi K3.1 Pro?" } ], "goodFor": [ { "aheadOn": [ "Reach, 50 against 40", "Risk, 55 against 50", "Documentation and upkeep, 63 against 51" ], "also": null, "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "slug": "arete-27b", "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party" }, { "aheadOn": [ "Agent interface, 65 against 60" ], "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" } ], "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-arete-27b.json", "title": "Agents-A1-4B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b" }, { "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/apertus-v1-5-8b-vs-arete-27b.json", "title": "Apertus v1.5 8B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b" }, { "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/apodex-1-1-mini-vs-arete-27b.json", "title": "Apodex-1.1-mini vs Arete-27B", "url": 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"url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.json", "title": "Arete-27B vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.json", "title": "Arete-27B vs Granite 4.2 8B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.json", "title": "Arete-27B vs Hy-Memory-30B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy3.json", "title": "Arete-27B vs Hy3", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling.json", "title": "Arete-27B vs Inkling", "url": 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"Kimi K3.1 Pro vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lbs-27b.json", "title": "Kimi K3.1 Pro vs LBS-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm2-5-2-6b.json", "title": "Kimi K3.1 Pro vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-1-7b.json", "title": "Kimi K3.1 Pro vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-audio-2b.json", "title": "Kimi K3.1 Pro vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-audio-2b" }, { "json": 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"https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Kimi K3.1 Pro vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl.json", "title": "Kimi K3.1 Pro vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-o-4-5.json", "title": "Kimi K3.1 Pro vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-1b.json", "title": "Kimi K3.1 Pro vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-35b-a3b.json", "title": "Kimi K3.1 Pro vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm5-2b.json", "title": "Kimi K3.1 Pro vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m2-5.json", "title": "Kimi K3.1 Pro vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m2-5" }, { "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 Nanbeige4.2-3B", "url": "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-4-3-33b.json", "title": "Kimi K3.1 Pro vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Kimi K3.1 Pro vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b.json", "title": "Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-4b.json", "title": "Kimi K3.1 Pro vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-super-120b-a12b.json", "title": "Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-ultra-550b-a55b.json", "title": "Kimi K3.1 Pro vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.json", "title": "Kimi K3.1 Pro vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0.json", "title": "Kimi K3.1 Pro vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think.json", "title": "Kimi K3.1 Pro vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b.json", "title": "Kimi K3.1 Pro vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b.json", "title": "Kimi K3.1 Pro vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-397b.json", "title": "Kimi K3.1 Pro vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-9b.json", "title": "Kimi K3.1 Pro vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.json", "title": "Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.json", "title": "Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Kimi K3.1 Pro vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-4b-instruct-2507.json", "title": "Kimi K3.1 Pro vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b.json", "title": "Kimi K3.1 Pro vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b.json", "title": "Kimi K3.1 Pro vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-2b.json", "title": "Kimi K3.1 Pro vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b.json", "title": "Kimi K3.1 Pro vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-4b.json", "title": "Kimi K3.1 Pro vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-9b.json", "title": "Kimi K3.1 Pro vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-27b.json", "title": "Kimi K3.1 Pro vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-35b-a3b.json", "title": "Kimi K3.1 Pro vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-27b.json", "title": "Kimi K3.1 Pro vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-flash-next.json", "title": "Kimi K3.1 Pro vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-next.json", "title": "Kimi K3.1 Pro vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-2b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2.json", "title": "Kimi K3.1 Pro vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint.json", "title": "Kimi K3.1 Pro vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview.json", "title": "Kimi K3.1 Pro vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.json", "title": "Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b.json", "title": "Kimi K3.1 Pro vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b.json", "title": "Kimi K3.1 Pro vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.json", "title": "Kimi K3.1 Pro vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b.json", "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.json", "title": "Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)", "url": "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-unlimited-ocr-2.json", "title": "Kimi K3.1 Pro vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.json", "title": "Kimi K3.1 Pro vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.json", "title": "Kimi K3.1 Pro vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b.json", "title": "Kimi K3.1 Pro vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.json", "title": "Kimi K3 vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "arete-27b": 50, "by": 10, "edge": "arete-27b", "key": "reach", "kimi-k3-1-pro": 40, "name": "Reach", "weight": 25 }, { "arete-27b": 55, "by": 5, "edge": "arete-27b", "key": "risk", "kimi-k3-1-pro": 50, "name": "Risk", "weight": 20 }, { "arete-27b": 60, "by": 5, "edge": "kimi-k3-1-pro", "key": "interface", "kimi-k3-1-pro": 65, "name": "Agent interface", "weight": 10 }, { "arete-27b": 63, "by": 12, "edge": "arete-27b", "key": "upkeep", "kimi-k3-1-pro": 51, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Arete-27B scores 55.1 (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 agent interface. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro", "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.min.md" }, "markdown": "Arete-27B scores 55.1 (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 agent interface. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\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- 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### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nAhead on:\n- Reach, 50 against 40\n- Risk, 55 against 50\n- Documentation and upkeep, 63 against 51\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\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- Agent interface, 65 against 60\n\nWatch for: 1,561 GB of files, so it needs a node of eight large accelerators at least\n\n\n## Why the edge\n\n### Documentation and upkeep: Arete-27B +12\n\n- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.\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\n### Reach: Arete-27B +10\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\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\n### Risk: Arete-27B +5\n\n- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\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\n### Agent interface: Kimi K3.1 Pro +5\n\n- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.\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\n## Score by category\n\n| Category | Weight | Arete-27B | Kimi K3.1 Pro | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 40 | Arete-27B +10 |\n| Risk | 20% (20 this run) | 55 | 50 | Arete-27B +5 |\n| Agent interface | 10% (10 this run) | 60 | 65 | Kimi K3.1 Pro +5 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 51 | Arete-27B +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | Kimi K3.1 Pro |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | Moonshot AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Kimi K3.1 Pro License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-11-05 |\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**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\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## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Arete-27B or Kimi K3.1 Pro?\n\nArete-27B scores 55.1 (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 agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"kimi-k3-1-pro\"}`. 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Pro](https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.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": "2026-11-10", "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": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Arete-27B vs Kimi K3.1 Pro", "url": "" } ], "description": "Arete-27B scores 55.1 (C) to Kimi K3.1 Pro's 48.6 (D) for open-weight 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