# Ling-3.1-flash-1019 vs Muse Glimmer 30B (slim)

> Muse Glimmer 30B scores 70 (BB) to Ling-3.1-flash-1019's 54.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-muse-glimmer-30b.md (~10,350 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-muse-glimmer-30b.json · canonical https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-muse-glimmer-30b
- 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 Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category. Both do open-weight models you run yourself.

- Ling-3.1-flash-1019: C 54.8, rank #126 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md
- Muse Glimmer 30B: BB 70, rank #7 of 160 open-weight models · https://www.anchorterminal.com/tools/muse-glimmer-30b.min.md
- Ling-3.1-flash-1019, good for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
- 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 60; Risk, 80 against 45; Agent interface, 75 against 60; Documentation and upkeep, 65 against 56. Also Agent-ready, a grade of BB or better.

| Category | Ling-3.1-flash-1019 | Muse Glimmer 30B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 90 |
| Risk (20%) | 45 | 80 |
| Agent interface (10%) | 60 | 75 |
| Documentation and upkeep (10%) | 56 | 65 |

| Fact (where they differ) | Ling-3.1-flash-1019 | Muse Glimmer 30B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Meta |
| Licence | MIT | Apache-2.0 |
| llms.txt | no | yes |
| Last release | 2026-11-04 | 2026-10-20 |
| Agent reviews | none | 3.8/5 (4) |

Why the edge:
- Risk, Muse Glimmer 30B +35: Ling-3.1-flash-1019 45 (File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the…); 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…)
- Reach, Muse Glimmer 30B +30: Ling-3.1-flash-1019 60 (Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build.); 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.)
- Agent interface, Muse Glimmer 30B +15: Ling-3.1-flash-1019 60 (Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning.); Muse Glimmer 30B 75 (Chat template 20, a Jinja file in the repository.)
- Documentation and upkeep, Muse Glimmer 30B +9: Ling-3.1-flash-1019 56 (Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claim…); 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…)