# Kimi K3 vs Muse Glimmer 30B

> Muse Glimmer 30B scores 70 (BB) to Kimi K3's 62.4 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b
- Markdown: https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.md (~6,050 tokens)
- Slim: https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/kimi-k3-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 Kimi K3's 62.4 (B), and leads in 4 of 5 scored categories. Kimi K3 leads on capability. Both do open-weight models you run yourself.

- Kimi K3: grade B, 62.4/100, rank #29 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.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

### Kimi K3 (B)

Good for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work

Ahead on:
- Capability, 75 against 50

Watch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine

### 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 50
- Risk, 80 against 65
- Agent interface, 75 against 65
- Documentation and upkeep, 65 against 41

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5

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


## Score by category

| Category | Weight | Kimi K3 | Muse Glimmer 30B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 75 | 50 | Kimi K3 +25 |
| Reach | 25% (25 this run) | 50 | 90 | Muse Glimmer 30B +40 |
| Risk | 20% (20 this run) | 65 | 80 | Muse Glimmer 30B +15 |
| Agent interface | 10% (10 this run) | 65 | 75 | Muse Glimmer 30B +10 |
| Documentation and upkeep | 10% (10 this run) | 41 | 65 | Muse Glimmer 30B +24 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.4 · B** | **70 · BB** | |

## Facts side by side

| Fact | Kimi K3 | Muse Glimmer 30B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | Meta |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Kimi K3 License (modified MIT) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-09-02 | 2026-10-20 |
| 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 | 2.3/5 (4) | 3.8/5 (4) |

## Verdicts

**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.

**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

### Kimi K3

1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000
2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect
3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable
4. Thinking is always on; set reasoning_effort to low or high when max is too slow
5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones
6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear
7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page

### 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, Kimi K3 or Muse Glimmer 30B?

Muse Glimmer 30B scores 70 (BB) on agent readiness against Kimi K3's 62.4 (B), and leads in 4 of 5 scored categories. Kimi K3 leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3", "b": "muse-glimmer-30b"}`. From a terminal: `anchor compare kimi-k3 muse-glimmer-30b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3.json and https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json

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