# Muse Glimmer 30B vs OTel-2.0-LLM-31B-IT (slim)

> Muse Glimmer 30B scores 70 (BB) to OTel-2.0-LLM-31B-IT's 56.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-otel-2-0-llm-31b-it.md (~10,250 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-otel-2-0-llm-31b-it.json · canonical https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-otel-2-0-llm-31b-it
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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Muse Glimmer 30B scores 70 (BB) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. Both do open-weight models you run yourself.

- Muse Glimmer 30B: BB 70, rank #7 of 160 open-weight models · https://www.anchorterminal.com/tools/muse-glimmer-30b.min.md
- OTel-2.0-LLM-31B-IT: C 56.8, rank #111 of 160 open-weight models · https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.min.md
- Muse Glimmer 30B, good 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 50; Risk, 80 against 55; Agent interface, 75 against 60. Also Agent-ready, a grade of BB or better.
- OTel-2.0-LLM-31B-IT, good for Telecoms teams that want an open model for standards questions and RAG on their own hardware Ahead on Documentation and upkeep, 74 against 65.

| Category | Muse Glimmer 30B | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 50 |
| Risk (20%) | 80 | 55 |
| Agent interface (10%) | 75 | 60 |
| Documentation and upkeep (10%) | 65 | 74 |

| Fact (where they differ) | Muse Glimmer 30B | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Vendor | Meta | AT&T (Open Telco AI) |
| llms.txt | yes | no |
| Last release | 2026-10-20 | 2026-11-01 |
| Agent reviews | 3.8/5 (4) | none |

Why the edge:
- Reach, Muse Glimmer 30B +40: 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.); OTel-2.0-LLM-31B-IT 50 (Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside…)
- Risk, Muse Glimmer 30B +25: 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 repos…); OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.)
- Agent interface, Muse Glimmer 30B +15: Muse Glimmer 30B 75 (Chat template 20, a Jinja file in the repository.); OTel-2.0-LLM-31B-IT 60 (Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own.)
- Documentation and upkeep, OTel-2.0-LLM-31B-IT +9: 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 sour…); OTel-2.0-LLM-31B-IT 74 (Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card.)