{ "data": { "a": { "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", "dns": "muse-glimmer-30b.grade.anchorterminal.com", "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": 1518, "categoryRank": 5, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 50, "interface": 75, "reach": 90, "risk": 80, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "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": "0.7", "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": 73, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "oldest": "2026-11-01", "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-ember", "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": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.json", "live": { "slug": "muse-glimmer-30b", "securityTxt": { "url": "https://meta.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:58.767810687Z" }, "llmsTxt": { "url": "https://dev.meta.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-10T15:44:02.548249718Z" }, "domain": { "domain": "meta.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/meta.ai", "checkedAt": "2026-11-08T14:51:01.706547979Z" }, "pages": [ { "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:04.598495005Z", "changedAt": "2026-11-10T16:23:04.598495005Z", "fingerprint": "fe9fc096c77c" } ], "updatedAt": "2026-11-10T16:23:04.598495005Z" } }, "answer": "Muse Glimmer 30B scores 70 (BB) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.", "b": { "slug": "sensenova-loop-9b-preview", "name": "SenseNova-Loop-9B-Preview", "vendor": "SenseTime (SenseNova)", "vendorUrl": "https://www.sensetime.com/en", "kind": "weights", "category": "open-weights", "summary": "SenseNova-Loop-9B-Preview is SenseTime's open-weight multimodal model of about 9B parameters. It runs 24 of its 32 decoder layers twice, with text and images in, tool calling, a thinking switch and a 256K context. Apache 2.0. No released runtime loads it.", "url": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json", "dns": "sensenova-loop-9b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "sensenova/SenseNova-Loop-9B-Preview" } ], "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 under Apache 2.0. Running it yourself costs the hardware: the files are 38.1 GB because most weights are stored in 32-bit floats, and the card's example loads them as BF16 (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://github.com/OpenSenseNova/SenseNova-Loop", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "looped-transformer", "tool-calling", "reasoning", "long-context", "preview", "apache-2.0", "safetensors", "custom-code", "self-hosted" ], "lastRelease": "2026-11-02", "graded": true, "anchor": { "graded": true, "score": 46.8, "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": 110, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 40, "risk": 50, "upkeep": 44 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.", "bestFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today", "strengths": [ "Apache 2.0, ungated", "A 256K training context, stated on the card", "Thinking can be switched per request", "The architecture and parameter counts are documented", "Changed in the last ten days" ], "weaknesses": [ "No released runtime runs it. vLLM and SGLang need unmerged pull requests", "Loading it runs Python from the repository", "The files are 38.1 GB, mostly F32, though the card says BF16", "No quantised build", "No tool-calling documentation beyond the template", "No limitations, safety results or data sources, and the technical report isn't out", "None of 5 questions answered" ], "agentNotes": [ "Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it", "Pin transformers 5.18.0, the version the card checked", "Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size", "Pass enable_thinking=False to apply_chat_template for a direct answer", "Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser", "Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API" ], "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": 46.8 } ], "editorialScores": { "interface": 60, "reach": 40, "risk": 50, "upkeep": 44 }, "provenanceScore": 51, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"transformers==5.18.0\" accelerate safetensors\n# then AutoModelForCausalLM.from_pretrained(\"sensenova/SenseNova-Loop-9B-Preview\", trust_remote_code=True, dtype=torch.bfloat16)\n# trust_remote_code runs modeling_sensenova_loop.py from the repository. Read it first", "http": "# No released server runs it. The card's vLLM and SGLang commands build from open pull requests,\n# and its LightLLM example serves an OpenAI-compatible API at http://localhost:8000/v1 on four GPUs" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/sensenova-loop-9b-preview" }, "weights": { "trainedBy": "SenseTime (SenseNova)", "params": "9B", "context": 262144, "goodFor": "Trying a looped transformer for long-context, tool-using work, if you can run the repository's own code on a large GPU.", "runtimes": [], "builds": [ { "name": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)", "url": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/tree/main", "who": "publisher", "precision": "f32", "sizeGB": 38.14, "memoryGB": 48, "format": "safetensors", "note": "Eight shards and the MTP head. Stored mostly as 32-bit floats; the card's example loads it as BF16, about half the size in memory" } ], "run": "python -c \"from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('sensenova/SenseNova-Loop-9B-Preview', trust_remote_code=True)\"", "runBuild": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)", "checked": "2026-11-10", "minMemoryGB": 48 }, "family": { "lab": "SenseTime (SenseNova)", "family": "SenseNova-Loop", "url": "https://www.anchorterminal.com/open-weights/#sensenova" }, "area": "models", "provenance": { "legalEntity": "SenseTime, listed on the Stock Exchange of Hong Kong (00020) per its investor page. SenseNova is its foundation model series, per its site", "domain": "sensetime.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://github.com/OpenSenseNova/SenseNova-Loop/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names SenseNova only. SenseTime's site presents SenseNova as its foundation model series, and that is the tie between the two; the site wasn't read for a link to this repository.", "The domain's registration date wasn't looked up.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "www.sensetime.com has no security.txt (404 on 10 November 2026). Its about page gives a security contact.", "The changelog is the commit history, 39 commits from 28 October to 2 November 2026." ], "score": 51 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.json" }, "edges": [ { "by": 50, "category": "reach", "leads": "muse-glimmer-30b", "name": "Reach", "reasons": { "a": "Memory 25, Meta's 16.8 GB Q4_K_M GGUF needs about 20 GB with the runtime's fifth, so it fits a 24 GB card but not 16 GB. Runtimes 25, released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama's library fills the cap. Builds 15, Meta publishes GGUF and ExecuTorch 4-bit builds, read as two kinds as gemma-4-31b-it was, though all are 4-bit k-quants. Terms 10, Apache 2.0 with a usage policy that adds limits beyond the law, so a free commercial licence under a use policy. Download 10, no repository is gated. Hosted 5, Meta's API doesn't serve it but Together, Featherless AI and DeepInfra are live on its Hub page.", "b": "Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40." }, "scores": { "a": 90, "b": 40 } }, { "by": 30, "category": "risk", "leads": "muse-glimmer-30b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF, with the ExecuTorch pos_embed.bin files read as raw embedding tables by their exact size and the Hub's scans, and no repository code runs. Provenance 15, the verified meta-models organisation Meta links, with a checksum on every file. Safety evaluation 10, Meta's own published results with stated set-ups but nothing to rerun and no outside assessor. Agent guidance 15, trained against indirect prompt injection with a principal hierarchy, and results published. Licence risk 10, 5 off for the use policy, and the policy's pointer to a latest copy leads back to the same repository file with no change clause. Security programme 5, a whitehat route in the usage policy, with no security.txt and public advisories unchecked, so scored as absent.", "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50." }, "scores": { "a": 80, "b": 50 } }, { "by": 21, "category": "upkeep", "leads": "muse-glimmer-30b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 20, all seven parts present, languages as a count of more than 100. Training data 10, a general description with a 4 January 2026 cut-off and no sources or sizes. Last change 20, a card update on 20 October citing the technical report, read as a change to the files as card edits were for other listings; Meta added its own MMI evaluation results file on 22 October. Responsiveness 5, a Meta member replied to 1 of the 40 most recent non-PR discussions (re-counted on 1 November). Version policy 5, a first release. File health 5, the files agree but a chat template bug report from 11 August still has no reply.", "b": "Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44." }, "scores": { "a": 65, "b": 44 } }, { "by": 15, "category": "interface", "leads": "muse-glimmer-30b", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Meta documents declaring tools, the ATEM block that comes back and the parser for each runtime. Trained tool calling 10, one call per turn and no parallel calls. Structured output 0, nothing documented for JSON or JSON Schema. Reasoning control 10, reasoning can't be turned off but is budgeted per request with four strength levels and returned as reasoning_content. Context 10, 131,072 tokens trained in.", "b": "Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60." }, "scores": { "a": 75, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Meta", "b": "SenseTime (SenseNova)", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-11-02", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "3.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Muse Glimmer 30B scores 70 (BB) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.", "question": "Which is better for AI agents, Muse Glimmer 30B or SenseNova-Loop-9B-Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 40", "Risk, 80 against 50", "Agent interface, 75 against 60", "Documentation and upkeep, 65 against 44" ], "also": [ "Agent-ready, a grade of BB or better" ], "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" }, { "aheadOn": null, "also": null, "goodFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today", "slug": "sensenova-loop-9b-preview", 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"https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-2B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-32B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview.json", "title": "Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview.json", "title": "Recursive Flint vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview.json", "title": "Ripple-65B Preview vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.json", "title": "SenseNova-Loop-9B-Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b.json", "title": "SenseNova-Loop-9B-Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.json", "title": "SenseNova-Loop-9B-Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.json", "title": "SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json", "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 50, "edge": "muse-glimmer-30b", "key": "reach", "muse-glimmer-30b": 90, "name": "Reach", "sensenova-loop-9b-preview": 40, "weight": 25 }, { "by": 30, "edge": "muse-glimmer-30b", "key": "risk", "muse-glimmer-30b": 80, "name": "Risk", "sensenova-loop-9b-preview": 50, "weight": 20 }, { "by": 15, "edge": "muse-glimmer-30b", "key": "interface", "muse-glimmer-30b": 75, "name": "Agent interface", "sensenova-loop-9b-preview": 60, "weight": 10 }, { "by": 21, "edge": "muse-glimmer-30b", "key": "upkeep", "muse-glimmer-30b": 65, "name": "Documentation and upkeep", "sensenova-loop-9b-preview": 44, "weight": 10 } ], "summary": "Muse Glimmer 30B scores 70 (BB) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "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.", "sensenova-loop-9b-preview": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview", "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview.min.md" }, "markdown": "Muse Glimmer 30B scores 70 (BB) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Muse Glimmer 30B: grade BB, 70/100, rank #7 of 160 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- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.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### 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 40\n- Risk, 80 against 50\n- Agent interface, 75 against 60\n- Documentation and upkeep, 65 against 44\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: One tool call per turn and no parallel calls\n\n### SenseNova-Loop-9B-Preview (D)\n\nGood for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today\n\nWatch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests\n\n\n## Why the edge\n\n### Reach: Muse Glimmer 30B +50\n\n- Muse Glimmer 30B (90): Memory 25, Meta's 16.8 GB Q4_K_M GGUF needs about 20 GB with the runtime's fifth, so it fits a 24 GB card but not 16 GB. Runtimes 25, released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama's library fills the cap. Builds 15, Meta publishes GGUF and ExecuTorch 4-bit builds, read as two kinds as gemma-4-31b-it was, though all are 4-bit k-quants. Terms 10, Apache 2.0 with a usage policy that adds limits beyond the law, so a free commercial licence under a use policy. Download 10, no repository is gated. Hosted 5, Meta's API doesn't serve it but Together, Featherless AI and DeepInfra are live on its Hub page.\n- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.\n\n### Risk: Muse Glimmer 30B +30\n\n- Muse Glimmer 30B (80): File safety 25, safetensors and GGUF, with the ExecuTorch pos_embed.bin files read as raw embedding tables by their exact size and the Hub's scans, and no repository code runs. Provenance 15, the verified meta-models organisation Meta links, with a checksum on every file. Safety evaluation 10, Meta's own published results with stated set-ups but nothing to rerun and no outside assessor. Agent guidance 15, trained against indirect prompt injection with a principal hierarchy, and results published. Licence risk 10, 5 off for the use policy, and the policy's pointer to a latest copy leads back to the same repository file with no change clause. Security programme 5, a whitehat route in the usage policy, with no security.txt and public advisories unchecked, so scored as absent.\n- SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.\n\n### Documentation and upkeep: Muse Glimmer 30B +21\n\n- Muse Glimmer 30B (65): Model card 20, all seven parts present, languages as a count of more than 100. Training data 10, a general description with a 4 January 2026 cut-off and no sources or sizes. Last change 20, a card update on 20 October citing the technical report, read as a change to the files as card edits were for other listings; Meta added its own MMI evaluation results file on 22 October. Responsiveness 5, a Meta member replied to 1 of the 40 most recent non-PR discussions (re-counted on 1 November). Version policy 5, a first release. File health 5, the files agree but a chat template bug report from 11 August still has no reply.\n- SenseNova-Loop-9B-Preview (44): Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44.\n\n### Agent interface: Muse Glimmer 30B +15\n\n- Muse Glimmer 30B (75): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Meta documents declaring tools, the ATEM block that comes back and the parser for each runtime. Trained tool calling 10, one call per turn and no parallel calls. Structured output 0, nothing documented for JSON or JSON Schema. Reasoning control 10, reasoning can't be turned off but is budgeted per request with four strength levels and returned as reasoning_content. Context 10, 131,072 tokens trained in.\n- SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n## Score by category\n\n| Category | Weight | Muse Glimmer 30B | SenseNova-Loop-9B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 40 | Muse Glimmer 30B +50 |\n| Risk | 20% (20 this run) | 80 | 50 | Muse Glimmer 30B +30 |\n| Agent interface | 10% (10 this run) | 75 | 60 | Muse Glimmer 30B +15 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 44 | Muse Glimmer 30B +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **70 · BB** | **46.8 · D** | |\n\n## Facts side by side\n\n| Fact | Muse Glimmer 30B | SenseNova-Loop-9B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Meta | SenseTime (SenseNova) |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-10-20 | 2026-11-02 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.8/5 (4) | none |\n\n## Verdicts\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**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.\n\n## Before you call either\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### SenseNova-Loop-9B-Preview\n\n1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it\n2. Pin transformers 5.18.0, the version the card checked\n3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size\n4. Pass enable_thinking=False to apply_chat_template for a direct answer\n5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser\n6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API\n\n## Questions\n\n### Which is better for AI agents, Muse Glimmer 30B or SenseNova-Loop-9B-Preview?\n\nMuse Glimmer 30B scores 70 (BB) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"muse-glimmer-30b\", \"b\": \"sensenova-loop-9b-preview\"}`. From a terminal: `anchor compare muse-glimmer-30b sensenova-loop-9b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json and https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json\n\n## Other comparisons with Muse Glimmer 30B or SenseNova-Loop-9B-Preview\n\n- [Agents-A1-4B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b.md)\n- [Agents-A1-4B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview.md)\n- [Apertus v1.5 8B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.md)\n- [Apertus v1.5 8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview.md)\n- [Apodex-1.1-mini vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-muse-glimmer-30b.md)\n- [Apodex-1.1-mini vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-sensenova-loop-9b-preview.md)\n- [Arete-27B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b.md)\n- [Arete-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.md)\n- [BigBang-v1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b.md)\n- [BigBang-v1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-sensenova-loop-9b-preview.md)\n- [Carbon-X vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/carbon-x-vs-muse-glimmer-30b.md)\n- [Carbon-X vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/carbon-x-vs-sensenova-loop-9b-preview.md)\n- [Cola2 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/cola2-vs-muse-glimmer-30b.md)\n- [Cola2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/cola2-vs-sensenova-loop-9b-preview.md)\n- [Cosmos-Reason2-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-muse-glimmer-30b.md)\n- [Cosmos-Reason2-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-sensenova-loop-9b-preview.md)\n- [Darwin-36B-Opus vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b.md)\n- [Darwin-36B-Opus vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek-V3.2 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-muse-glimmer-30b.md)\n- [DeepSeek-V3.2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek V4.1 Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.md)\n- [DeepSeek V4.1 Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek-V4-Flash-0731 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.md)\n- [DeepSeek-V4-Flash-0731 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-sensenova-loop-9b-preview.md)\n- [Devstral Small 2 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/devstral-small-2-vs-muse-glimmer-30b.md)\n- [Devstral Small 2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview.md)\n- [DiffusionGemma 26B A4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-muse-glimmer-30b.md)\n- [DiffusionGemma 26B A4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-sensenova-loop-9b-preview.md)\n- [EXAONE 4.5 33B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-muse-glimmer-30b.md)\n- [EXAONE 4.5 33B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 12B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 12B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 26B A4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 26B A4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 31B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 31B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 E2B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 E2B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 E4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 E4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-sensenova-loop-9b-preview.md)\n- [GLM-4.5-Air vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b.md)\n- [GLM-4.5-Air vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-sensenova-loop-9b-preview.md)\n- [GLM-4.7-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-muse-glimmer-30b.md)\n- [GLM-4.7-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-sensenova-loop-9b-preview.md)\n- [GLM-5.3-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-muse-glimmer-30b.md)\n- [GLM-5.3-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-sensenova-loop-9b-preview.md)\n- [GLM-5.3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-vs-muse-glimmer-30b.md)\n- [GLM-5.3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-sensenova-loop-9b-preview.md)\n- [gpt-oss-120b vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-muse-glimmer-30b.md)\n- [gpt-oss-120b vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-sensenova-loop-9b-preview.md)\n- [gpt-oss-20b vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-muse-glimmer-30b.md)\n- [gpt-oss-20b vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 30B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-muse-glimmer-30b.md)\n- [Granite 4.2 30B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 3B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-muse-glimmer-30b.md)\n- [Granite 4.2 3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 8B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-muse-glimmer-30b.md)\n- [Granite 4.2 8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-sensenova-loop-9b-preview.md)\n- [Hy-Memory-30B-A3B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-muse-glimmer-30b.md)\n- [Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.md)\n- [Hy3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b.md)\n- [Hy3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/hy3-vs-sensenova-loop-9b-preview.md)\n- [Inkling-Small vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/inkling-small-vs-muse-glimmer-30b.md)\n- [Inkling-Small vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-small-vs-sensenova-loop-9b-preview.md)\n- [Inkling vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/inkling-vs-muse-glimmer-30b.md)\n- [Inkling vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-vs-sensenova-loop-9b-preview.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-muse-glimmer-30b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-sensenova-loop-9b-preview.md)\n- [Kimi K3.1 Pro vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-muse-glimmer-30b.md)\n- [Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.md)\n- [Kimi K3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.md)\n- [Kimi K3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-vs-sensenova-loop-9b-preview.md)\n- [Kolibri-1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b.md)\n- [Kolibri-1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kolibri-1-vs-sensenova-loop-9b-preview.md)\n- [Kronos vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b.md)\n- [Kronos vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kronos-vs-sensenova-loop-9b-preview.md)\n- [Laguna M.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.md)\n- [Laguna M.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna S 2.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b.md)\n- [Laguna S 2.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna XS 2.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b.md)\n- [Laguna XS 2.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-sensenova-loop-9b-preview.md)\n- [LBS-27B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lbs-27b-vs-muse-glimmer-30b.md)\n- [LBS-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.md)\n- [LFM2.5-2.6B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.md)\n- [LFM2.5-2.6B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-1.7B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-muse-glimmer-30b.md)\n- [LFM3-1.7B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-Audio-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-muse-glimmer-30b.md)\n- [LFM3-Audio-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-Omni-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-muse-glimmer-30b.md)\n- [LFM3-Omni-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-VL-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-muse-glimmer-30b.md)\n- [LFM3-VL-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.md)\n- [Ling-3.0-tiny vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-muse-glimmer-30b.md)\n- [Ling-3.0-tiny vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-sensenova-loop-9b-preview.md)\n- [Ling-3.1-flash-1019 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-muse-glimmer-30b.md)\n- [Ling-3.1-flash-1019 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-sensenova-loop-9b-preview.md)\n- [LongCat-2.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/longcat-2-5-vs-muse-glimmer-30b.md)\n- [LongCat-2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-muse-glimmer-30b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-sensenova-loop-9b-preview.md)\n- [MiMo-V2.6-Flash-RL vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-muse-glimmer-30b.md)\n- [MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-o 4.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b.md)\n- [MiniCPM-o 4.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-V 4.7 1B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-muse-glimmer-30b.md)\n- [MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-V 4.7 35B-A3B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-muse-glimmer-30b.md)\n- [MiniCPM-V 4.7 35B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM5-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b.md)\n- [MiniCPM5-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm5-2b-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M2.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-muse-glimmer-30b.md)\n- [MiniMax-M2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M2.7 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b.md)\n- [MiniMax-M2.7 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M3.1-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-muse-glimmer-30b.md)\n- [MiniMax-M3.1-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-sensenova-loop-9b-preview.md)\n- [Mistral Large 4 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/mistral-large-4-vs-muse-glimmer-30b.md)\n- [Mistral Large 4 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mistral-large-4-vs-sensenova-loop-9b-preview.md)\n- [Muse 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[SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.md)\n- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md)\n- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.md)\n- [SenseNova-Loop-9B-Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": 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