# Muse Glimmer 30B vs WeVisOne-VL-2B (slim)

> Muse Glimmer 30B scores 70 (BB) to WeVisOne-VL-2B's 61 (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-wevisone-vl-2b.md (~9,700 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Muse Glimmer 30B scores 70 (BB) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in every scored category. 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
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.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 70; Risk, 80 against 55; Agent interface, 75 against 70; Documentation and upkeep, 65 against 48. Also Agent-ready, a grade of BB or better.
- WeVisOne-VL-2B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence

| Category | Muse Glimmer 30B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 70 |
| Risk (20%) | 80 | 55 |
| Agent interface (10%) | 75 | 70 |
| Documentation and upkeep (10%) | 65 | 48 |

| Fact (where they differ) | Muse Glimmer 30B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Meta | Tencent (WeChat Vision) |
| llms.txt | yes | no |
| Last release | 2026-10-20 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3.8/5 (4) | none |

Why the edge:
- 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…); WeVisOne-VL-2B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)
- Reach, Muse Glimmer 30B +20: 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.); WeVisOne-VL-2B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB.)
- Documentation and upkeep, Muse Glimmer 30B +17: 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…); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Agent interface, Muse Glimmer 30B +5: Muse Glimmer 30B 75 (Chat template 20, a Jinja file in the repository.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)