Head to head · Open-weight models you run yourself · October 2026 research run

LongCat-2.5 vs Muse Glimmer 30B

Muse Glimmer 30B scores 70 (BB) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

LongCat-2.5 D

Best for Operators with NPU clusters, and anyone tracking what the open flagships can do

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

3,562 GB in BF16 and 1,336 GB at 4 bits

Muse Glimmer 30B BB

Best 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

Ahead on

  • Reach, 90 against 35
  • Risk, 80 against 60
  • Documentation and upkeep, 65 against 46

Also in its favour

  • Agent-ready, a grade of BB or better

Watch for

One tool call per turn and no parallel calls

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Reach Muse Glimmer 30B +55

LongCat-2.5 35

Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.

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.

Risk Muse Glimmer 30B +20

LongCat-2.5 60

File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.

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.

Documentation and upkeep Muse Glimmer 30B +19

LongCat-2.5 46

Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.

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.

Score by category

CategoryWeight this runLongCat-2.5Muse Glimmer 30BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%253590Muse Glimmer 30B +55
Risk20%206080Muse Glimmer 30B +20
Agent interface10%107575even
Documentation and upkeep10%104665Muse Glimmer 30B +19
Negative events≤1500
TotalD 50.5/100BB 70/100

Facts side by side

FactLongCat-2.5Muse Glimmer 30B
KindOpen-weight modelOpen-weight model
VendorMeituan (LongCat)Meta
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITApache-2.0
Read-only variant documentednono
llms.txtnoyes
Last release2026-11-102026-10-20
Terms last updatednot read yetno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3.8/5 (4)

Verdicts

LongCat-2.5

Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.

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.

Before you call either

LongCat-2.5

  1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
  2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
  3. Pass chat_template_kwargs with enable_thinking true or false
  4. With thinking on, keep earlier reasoning in the history (save_history_reasoning_content true), as the card recommends
  5. The card's results use temperature 1.0 and top_p 0.95
  6. Its benchmark table is Meituan's own. The unmarked scores are in-house

Muse Glimmer 30B

  1. 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
  2. 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
  3. 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
  4. Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects tool_choice="required"
  5. Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on <|eom|>
  6. Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length
  7. For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth

Questions

Which is better for AI agents, LongCat-2.5 or Muse Glimmer 30B?

Muse Glimmer 30B scores 70 (BB) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories.

Other comparisons with LongCat-2.5 or Muse Glimmer 30B

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