# Apertus v1.5 8B vs Muse Glimmer 30B

> Muse Glimmer 30B scores 70 (BB) to Apertus v1.5 8B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b
- Markdown: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.md (~6,400 tokens)
- Slim: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.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 Apertus v1.5 8B's 61 (C), and leads in 2 of 4 scored categories. Apertus v1.5 8B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Apertus v1.5 8B: grade C, 61/100, rank #35 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json
- 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
- 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

### Apertus v1.5 8B (C)

Good for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.

Ahead on:
- Agent interface, 85 against 75
- Documentation and upkeep, 84 against 65

Watch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers

### 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 40
- Risk, 80 against 75

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


## Score by category

| Category | Weight | Apertus v1.5 8B | Muse Glimmer 30B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 40 | 90 | Muse Glimmer 30B +50 |
| Risk | 20% (20 this run) | 75 | 80 | Muse Glimmer 30B +5 |
| Agent interface | 10% (10 this run) | 85 | 75 | Apertus v1.5 8B +10 |
| Documentation and upkeep | 10% (10 this run) | 84 | 65 | Apertus v1.5 8B +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **70 · BB** | |

## Facts side by side

| Fact | Apertus v1.5 8B | Muse Glimmer 30B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Meta |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-14 | 2026-10-20 |
| Terms last updated |  | 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 | 2.8/5 (4) | 3.8/5 (4) |

## Verdicts

**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.

**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

### Apertus v1.5 8B

1. Accept the terms on the card and set HF_TOKEN before downloading
2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools
4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously
5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent

### 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, Apertus v1.5 8B or Muse Glimmer 30B?

Muse Glimmer 30B scores 70 (BB) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 2 of 4 scored categories. Apertus v1.5 8B leads on agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "apertus-v1-5-8b", "b": "muse-glimmer-30b"}`. From a terminal: `anchor compare apertus-v1-5-8b muse-glimmer-30b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json

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