{ "data": { "a": { "slug": "kimi-k3", "name": "Kimi K3", "vendor": "Moonshot AI", "vendorUrl": "https://www.moonshot.ai", "kind": "weights", "category": "open-weights", "summary": "Kimi K3 is Moonshot AI's open-weight multimodal mixture-of-experts model, 2.8T parameters with 104B active and a 1M-token context, trained in MXFP4 and published on Hugging Face under the modified-MIT Kimi K3 Licence.", "url": "https://www.anchorterminal.com/tools/kimi-k3", "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3.json", "repo": "https://huggingface.co/moonshotai/Kimi-K3", "license": "Kimi K3 License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "moonshotai/Kimi-K3" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3 Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/moonshotai/Kimi-K3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "modified-mit", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2026-09-02", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 24, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "bestFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "strengths": [ "1,048,576 tokens of context trained in, with indexed parallel tool calls in one turn", "Model code in released vLLM, SGLang and llama.cpp, with recipes linked from the card", "A third-party cyber evaluation by the UK AI Security Institute and CAISI", "Safetensors only, with a SHA-256 for every shard, downloadable with no account", "Served by Moonshot's API and five inference providers for agents without the hardware" ], "weaknesses": [ "1,561 GB of files at its smallest publisher build, far beyond any single machine", "No Jinja template, so prompts, tokeniser and model load through Moonshot's Python with trust_remote_code", "No Moonshot reply in the 40 most recent discussions, and a tokeniser report waits unanswered", "No disclosure route for flaws and no prompt-injection guidance", "No training sources, sizes or knowledge cut-off, and the card leaves out limitations and languages" ], "agentNotes": [ "Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect", "Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable", "Thinking is always on; set reasoning_effort to low or high when max is too slow", "Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones", "Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear", "Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "provenanceScore": 53, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# SGLang, per its Kimi-K3 cookbook linked from the card (one node of 8 B300 GPUs)\nsglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 \\\n --context-length 65536 --mem-fraction-static 0.82 \\\n --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"moonshotai/Kimi-K3\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"high\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kimi-k3" }, "weights": { "trainedBy": "Moonshot AI", "params": "2.8T", "active": "104B", "context": 1048576, "goodFor": "Large multimodal agent model for coding and long tool-calling sessions, text and images in, with always-on reasoning and JSON Schema output.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Kimi-K3", "url": "https://huggingface.co/moonshotai/Kimi-K3", "who": "publisher", "precision": "mxfp4", "sizeGB": 1560.9, "memoryGB": 2048, "format": "safetensors", "note": "Trained in MXFP4; the only build Moonshot publishes" } ], "run": "sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "runBuild": "Kimi-K3", "checked": "2026-10-21", "minMemoryGB": 2048 }, "family": { "lab": "Moonshot AI (Kimi)", "family": "Kimi K3", "url": "https://www.anchorterminal.com/open-weights/#moonshot", "olderCount": 18, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)", "domain": "moonshot.ai", "domainRegistered": "", "domainNote": "moonshot.ai's registration date couldn't be read through RDAP on 21 October 2026.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/moonshotai/Kimi-K3/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names Moonshot AI. The Kimi Platform's terms (platform.kimi.ai/docs/agreement/modeluse) name Moonshot AI PTE. LTD. as the party for its API.", "Nothing is hosted, so the Kimi K3 Licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Its messages mostly say \"Update README.md\" or \"Update encoding_k3.py\" without saying what changed; the last change, on 2 September 2026, added an evaluation result.", "moonshot.ai, www.moonshot.ai, kimi.com and www.kimi.com answered /.well-known/security.txt with an HTML page and platform.kimi.ai with 404 on 21 October 2026, so no security.txt was found. The Kimi-K3 GitHub repository has no SECURITY.md." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3.json" }, "answer": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Kimi K3's 62.4 (B), and leads in 4 of 5 scored categories. Kimi K3 leads on capability.", "b": { "slug": "muse-glimmer-30b", "name": "Muse Glimmer 30B", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai/docs/muse-glimmer", "kind": "weights", "category": "open-weights", "summary": "Muse Glimmer 30B is Meta's open-weight dense multimodal model for local agent work, 29.6B parameters with text and image input and a 128K-token context, published on Hugging Face under Apache 2.0 with GGUF and ExecuTorch builds.", "url": "https://www.anchorterminal.com/tools/muse-glimmer-30b", "markdownUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json", "repo": "https://huggingface.co/meta-models/Muse-Glimmer-30B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "meta-models/Muse-Glimmer-30B" }, { "registry": "huggingface", "name": "meta-models/Muse-Glimmer-30B-GGUF" }, { "registry": "huggingface", "name": "meta-models/Muse-Glimmer-30B-assistant" }, { "registry": "huggingface", "name": "meta-models/Muse-Glimmer-30B-ExecuTorch-PTE" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-10-21), and Meta's docs say there is no API key and no gated access.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a separate Muse Glimmer Usage Policy listing prohibited uses. Meta Model API doesn't serve it. Running it yourself costs the hardware, 17 GB for Meta's Q4_K_M GGUF or about 60 GB in BF16 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://dev.meta.ai/docs/muse-glimmer", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "self-hosted", "new" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 70, "grade": "BB", "agentReady": true, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 5, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 50, "interface": 75, "reach": 90, "risk": 80, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.", "bestFor": "Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough", "strengths": [ "Meta's own 16.8 GB Q4_K_M GGUF fits a 24 GB card, with a DFlash drafter for speculative decoding", "Released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama, no trust_remote_code", "Tool format and parser names per runtime documented by Meta, with a Jinja template in the repository", "Trained against indirect prompt injection with a principal hierarchy, and injection results published", "Ungated download, safetensors and GGUF from Meta's verified organisation, and three live inference providers" ], "weaknesses": [ "One tool call per turn and no parallel calls", "No structured output documented, and reasoning can't be turned off", "A usage policy adds prohibited uses beyond the law to Apache 2.0", "Meta replied to 1 of the 40 most recent discussions, and a chat template bug report has waited since 11 August", "Safety results are Meta's own, with nothing published to rerun them" ], "agentNotes": [ "Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later", "In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text", "Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400", "Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice=\"required\"`", "Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `\u003c|eom|\u003e`", "Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length", "For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70 } ], "editorialScores": { "capability": 50, "interface": 75, "reach": 90, "risk": 80, "upkeep": 65 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run muse-glimmer\n# or Meta's 17 GB GGUF with llama.cpp b10353 or later:\nllama serve -hf meta-models/Muse-Glimmer-30B-GGUF \\\n --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf \\\n -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 \\\n --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"muse-glimmer\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/muse-glimmer-30b" }, "sameCompany": [ "meta-model-api", "muse-spark-open", "muse-code" ], "weights": { "trainedBy": "Meta", "builtOn": "Distilled from Muse Spark", "params": "29.6B", "context": 131072, "goodFor": "Dense multimodal model for local agent work, text and images in, with tool calls and always-on adjustable reasoning.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Muse-Glimmer-30B-KQuant-Dynamic-Q4_K_XL", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/blob/main/Muse-Glimmer-30B-KQuant-Dynamic-Q4_K_XL.gguf", "who": "publisher", "precision": "q4_k_xl", "sizeGB": 19.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Meta's dynamic K-quant; the card targets 32 GB. Add mmproj-Muse-Glimmer-30B-Q4_K_M.gguf (1.4 GB) for images" }, { "name": "Muse-Glimmer-30B-KQuant-17GB-Q4_K_M", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/blob/main/Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 16.8, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "The card targets 24 GB. Add the 1.4 GB mmproj for images and the 1.6 GB DFlash drafter for speculative decoding" }, { "name": "Muse-Glimmer-30B-ExecuTorch-PTE (smallest: k-quant-17G text-solo, Metal)", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-ExecuTorch-PTE", "who": "publisher", "precision": "q4_k_m", "sizeGB": 17.9, "memoryGB": 24, "format": "executorch", "note": "16 ExecuTorch exports from 17.9 to 31.4 GB, for Apple Metal or NVIDIA (sm80) only" }, { "name": "ollama muse-glimmer:latest", "url": "https://ollama.com/library/muse-glimmer:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 18, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a 1.4 GB vision projector. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Muse-Glimmer-30B", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B", "who": "publisher", "precision": "bf16", "sizeGB": 59.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'", "runBuild": "Muse-Glimmer-30B-KQuant-17GB-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Meta", "family": "Muse", "url": "https://www.anchorterminal.com/open-weights/#meta", "others": [ { "name": "Muse Glimmer 30B assistant (DFlash drafter)", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-assistant", "params": "2.56B (Hub safetensors count; the card states no figure)" }, { "name": "Muse Spark Open 816B-A42B", "url": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B", "params": "816B total, 42B active (MoE)", "listing": "https://www.anchorterminal.com/tools/muse-spark-open" } ], "olderCount": 50, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Meta (the verified Hugging Face organisation is \"Meta Inc.\", the card names Meta Superintelligence Lab, and the licence carries no copyright line)", "domain": "meta.ai", "domainRegistered": "2017-12-16", "domainNote": "Registration date as recorded by RDAP for the muse-spark-open listing.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/meta-models/Muse-Glimmer-30B/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "meta-models is a verified Hugging Face organisation named Meta Inc. Meta's launch post on research.meta.ai and its developer docs on dev.meta.ai link to huggingface.co/meta-models/Muse-Glimmer-30B, and the same organisation holds Muse Spark Open.", "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. Some messages say what changed (the duplicate reasoning-strength fix on 11 August); 7 of the 15 read Update README.md.", "dev.meta.ai/.well-known/security.txt and research.meta.ai/.well-known/security.txt answered 404 on 21 October 2026, and ai.meta.com's redirected and wasn't followed. The usage policy sends bugs and security concerns to facebook.com/whitehat/info." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Moonshot AI", "b": "Meta", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Kimi K3 License (modified MIT)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-09-02", "b": "2026-10-20", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.3/5 (4)", "b": "3.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Kimi K3's 62.4 (B), and leads in 4 of 5 scored categories. Kimi K3 leads on capability.", "question": "Which is better for AI agents, Kimi K3 or Muse Glimmer 30B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 75 against 50" ], "also": null, "goodFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "slug": "kimi-k3", "watchFor": "1,561 GB of files at its smallest publisher build, far beyond any single machine" }, { "aheadOn": [ "Reach, 90 against 50", "Risk, 80 against 65", "Agent interface, 75 against 65", "Documentation and upkeep, 65 against 41" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 2.3 out of 5" ], "goodFor": "Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough", "slug": "muse-glimmer-30b", "watchFor": "One tool call per turn and no parallel calls" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.json", "title": "Agents-A1-4B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b.json", "title": "Agents-A1-4B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.json", "title": "Apertus v1.5 8B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.json", "title": "Apertus v1.5 8B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.json", "title": "Apodex-1.1-mini vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-muse-glimmer-30b.json", "title": "Apodex-1.1-mini vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.json", "title": "BigBang-v1 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b.json", "title": "BigBang-v1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3.json", "title": "Cosmos-Reason2-2B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-muse-glimmer-30b.json", "title": "Cosmos-Reason2-2B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3.json", "title": "Darwin-36B-Opus vs Kimi K3", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b.json", "title": "Darwin-36B-Opus vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3.json", "title": "DeepSeek-V3.2 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-muse-glimmer-30b.json", "title": "DeepSeek-V3.2 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.json", "title": "DeepSeek V4.1 Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.json", "title": "DeepSeek V4.1 Flash vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3.json", "title": "DeepSeek-V4-Flash-0731 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.json", "title": "DeepSeek-V4-Flash-0731 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3.json", "title": "DiffusionGemma 26B A4B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-muse-glimmer-30b.json", "title": "DiffusionGemma 26B A4B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.json", "title": "Gemma 4 12B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b.json", "title": "Gemma 4 12B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.json", "title": "Gemma 4 26B A4B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.json", "title": "Gemma 4 26B A4B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.json", "title": "Gemma 4 31B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.json", "title": "Gemma 4 31B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3.json", "title": "Gemma 4 E2B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-muse-glimmer-30b.json", "title": "Gemma 4 E2B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3.json", "title": "Gemma 4 E4B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-muse-glimmer-30b.json", "title": "Gemma 4 E4B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3.json", "title": "GLM-4.5-Air vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b.json", "title": "GLM-4.5-Air vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3.json", "title": "GLM-4.7-Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-muse-glimmer-30b.json", "title": "GLM-4.7-Flash vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.json", "title": "GLM-5.3-Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-muse-glimmer-30b.json", "title": "GLM-5.3-Flash vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3.json", "title": "GLM-5.3 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-muse-glimmer-30b.json", "title": "GLM-5.3 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3.json", "title": "gpt-oss-120b vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-muse-glimmer-30b.json", "title": "gpt-oss-120b vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3.json", "title": "gpt-oss-20b vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-muse-glimmer-30b.json", "title": "gpt-oss-20b vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.json", "title": "Granite 4.2 3B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-muse-glimmer-30b.json", "title": "Granite 4.2 3B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-kimi-k3.json", "title": "Hy3 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/hy3-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b.json", "title": "Hy3 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3.json", "title": "Inkling-Small vs Kimi K3", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-muse-glimmer-30b.json", "title": "Inkling-Small vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3.json", "title": "Inkling vs Kimi K3", "url": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-muse-glimmer-30b.json", "title": "Inkling vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/inkling-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-muse-glimmer-30b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1.json", "title": "Kimi K3 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.json", "title": "Kimi K3 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.json", "title": "Kimi K3 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.json", "title": "Kimi K3 vs Laguna XS 2.1", "url": 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"https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5.json", "title": "Kimi K3 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7.json", "title": "Kimi K3 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Kimi K3 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b.json", "title": "Kimi K3 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-4b.json", "title": "Kimi K3 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Kimi K3 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.json", "title": "Kimi K3 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-ultra-550b-a55b.json", "title": "Kimi K3 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.json", "title": "Kimi K3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-olmo-3-7b-think.json", "title": "Kimi K3 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.json", "title": "Kimi K3 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b.json", "title": "Kimi K3 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.json", "title": "Kimi K3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Kimi K3 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-4b-instruct-2507.json", "title": "Kimi K3 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b.json", "title": "Kimi K3 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b.json", "title": "Kimi K3 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-2b.json", "title": "Kimi K3 vs Qwen3.5-2B", "url": 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vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2.json", "title": "Kimi K3 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-smollm3-3b.json", "title": "Kimi K3 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b.json", "title": "Kimi K3 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b.json", "title": "Kimi K3 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Kimi K3 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b.json", "title": "Kimi K3 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b.json", "title": "Kolibri-1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.json", "title": "Laguna M.1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b.json", "title": "Laguna S 2.1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b.json", "title": "Laguna XS 2.1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.json", "title": "LFM2.5-2.6B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-muse-glimmer-30b.json", "title": "Ling-3.0-tiny vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-muse-glimmer-30b" }, { "json": 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"title": "MiniMax-M2.7 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Muse Glimmer 30B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-30b-a3b.json", "title": "Muse Glimmer 30B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-4b.json", "title": "Muse Glimmer 30B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-4b" }, { "json": 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"https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Muse Glimmer 30B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-4b-instruct-2507.json", "title": "Muse Glimmer 30B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-0-8b.json", "title": "Muse Glimmer 30B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b.json", "title": "Muse Glimmer 30B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b.json", "title": "Muse Glimmer 30B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b.json", "title": "Muse Glimmer 30B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-4b.json", "title": "Muse Glimmer 30B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b.json", "title": "Muse Glimmer 30B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b.json", "title": "Muse Glimmer 30B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b.json", "title": "Muse Glimmer 30B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b.json", "title": "Muse Glimmer 30B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next.json", "title": "Muse Glimmer 30B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-next.json", "title": "Muse Glimmer 30B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2.json", "title": "Muse Glimmer 30B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b.json", "title": "Muse Glimmer 30B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b.json", "title": "Muse Glimmer 30B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b.json", "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b.json", "title": "Muse Glimmer 30B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 25, "edge": "kimi-k3", "key": "capability", "kimi-k3": 75, "muse-glimmer-30b": 50, "name": "Capability", "weight": 35 }, { "by": 40, "edge": "muse-glimmer-30b", "key": "reach", "kimi-k3": 50, "muse-glimmer-30b": 90, "name": "Reach", "weight": 25 }, { "by": 15, "edge": "muse-glimmer-30b", "key": "risk", "kimi-k3": 65, "muse-glimmer-30b": 80, "name": "Risk", "weight": 20 }, { "by": 10, "edge": "muse-glimmer-30b", "key": "interface", "kimi-k3": 65, "muse-glimmer-30b": 75, "name": "Agent interface", "weight": 10 }, { "by": 24, "edge": "muse-glimmer-30b", "key": "upkeep", "kimi-k3": 41, "muse-glimmer-30b": 65, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Kimi K3's 62.4 (B), and leads in 4 of 5 scored categories. Kimi K3 leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "kimi-k3": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "muse-glimmer-30b": "A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b", "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.min.md" }, "markdown": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Kimi K3's 62.4 (B), and leads in 4 of 5 scored categories. Kimi K3 leads on capability. Both do open-weight models you run yourself.\n\n- Kimi K3: grade B, 62.4/100, rank #29 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.json\n- Muse Glimmer 30B: grade BB, 70/100, rank #7 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/muse-glimmer-30b.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Kimi K3 (B)\n\nGood for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work\n\nAhead on:\n- Capability, 75 against 50\n\nWatch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine\n\n### Muse Glimmer 30B (BB)\n\nGood for: Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough\n\nAhead on:\n- Reach, 90 against 50\n- Risk, 80 against 65\n- Agent interface, 75 against 65\n- Documentation and upkeep, 65 against 41\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5\n\nWatch for: One tool call per turn and no parallel calls\n\n\n## Score by category\n\n| Category | Weight | Kimi K3 | Muse Glimmer 30B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 75 | 50 | Kimi K3 +25 |\n| Reach | 25% (25 this run) | 50 | 90 | Muse Glimmer 30B +40 |\n| Risk | 20% (20 this run) | 65 | 80 | Muse Glimmer 30B +15 |\n| Agent interface | 10% (10 this run) | 65 | 75 | Muse Glimmer 30B +10 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 65 | Muse Glimmer 30B +24 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **70 · BB** | |\n\n## Facts side by side\n\n| Fact | Kimi K3 | Muse Glimmer 30B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | Meta |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Kimi K3 License (modified MIT) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-09-02 | 2026-10-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.3/5 (4) | 3.8/5 (4) |\n\n## Verdicts\n\n**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.\n\n**Muse Glimmer 30B.** A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.\n\n## Before you call either\n\n### Kimi K3\n\n1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect\n3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable\n4. Thinking is always on; set reasoning_effort to low or high when max is too slow\n5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones\n6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear\n7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page\n\n### Muse Glimmer 30B\n\n1. Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later\n2. In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text\n3. Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400\n4. Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice=\"required\"`\n5. Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `\u003c|eom|\u003e`\n6. Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length\n7. For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth\n\n## Questions\n\n### Which is better for AI agents, Kimi K3 or Muse Glimmer 30B?\n\nMuse Glimmer 30B scores 70 (BB) on agent readiness against Kimi K3's 62.4 (B), and leads in 4 of 5 scored categories. Kimi K3 leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3\", \"b\": \"muse-glimmer-30b\"}`. 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