# Muse Glimmer 30B vs Nemotron 3 Ultra 550B A55B

> Muse Glimmer 30B scores 70 (BB) to Nemotron 3 Ultra 550B A55B's 68.8 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b
- Markdown: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b.md (~6,800 tokens)
- Slim: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Muse Glimmer 30B scores 70 (BB) on agent readiness against Nemotron 3 Ultra 550B A55B's 68.8 (B), and leads in 2 of 5 scored categories. Nemotron 3 Ultra 550B A55B leads on capability and agent interface. Both do open-weight models you run yourself.

- Muse Glimmer 30B: grade BB, 70/100, rank #7 of 100 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
- Nemotron 3 Ultra 550B A55B: grade B, 68.8/100, rank #9 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Muse Glimmer 30B (BB)

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 55
- Risk, 80 against 70

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5

Watch for: One tool call per turn and no parallel calls

### Nemotron 3 Ultra 550B A55B (B)

Good for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs

Ahead on:
- Capability, 73 against 50
- Agent interface, 90 against 75

Watch for: The smallest build is 352 GB and needs at least four B200-class GPUs


## Score by category

| Category | Weight | Muse Glimmer 30B | Nemotron 3 Ultra 550B A55B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 50 | 73 | Nemotron 3 Ultra 550B A55B +23 |
| Reach | 25% (25 this run) | 90 | 55 | Muse Glimmer 30B +35 |
| Risk | 20% (20 this run) | 80 | 70 | Muse Glimmer 30B +10 |
| Agent interface | 10% (10 this run) | 75 | 90 | Nemotron 3 Ultra 550B A55B +15 |
| Documentation and upkeep | 10% (10 this run) | 65 | 65 | even |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **70 · BB** | **68.8 · B** | |

## Facts side by side

| Fact | Muse Glimmer 30B | Nemotron 3 Ultra 550B A55B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Meta | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-10-20 | 2026-06-04 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 3.8/5 (4) | 2.8/5 (4) |

## 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.

**Nemotron 3 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.

## Before you call either

### 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

### Nemotron 3 Ultra 550B A55B

1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead
2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more
3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register
4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse
5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens
6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets

## Questions

### Which is better for AI agents, Muse Glimmer 30B or Nemotron 3 Ultra 550B A55B?

Muse Glimmer 30B scores 70 (BB) on agent readiness against Nemotron 3 Ultra 550B A55B's 68.8 (B), and leads in 2 of 5 scored categories. Nemotron 3 Ultra 550B A55B leads on capability and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "muse-glimmer-30b", "b": "nemotron-3-ultra-550b-a55b"}`. From a terminal: `anchor compare muse-glimmer-30b nemotron-3-ultra-550b-a55b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json

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