# MiniCPM5-2B vs Muse Glimmer 30B > Muse Glimmer 30B scores 70 (BB) to MiniCPM5-2B's 60.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b - Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b.md (~6,200 tokens) - Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/minicpm5-2b-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 MiniCPM5-2B's 60.2 (C), and leads in 2 of 5 scored categories. MiniCPM5-2B leads on documentation and upkeep. Both do open-weight models you run yourself. - MiniCPM5-2B: grade C, 60.2/100, rank #56 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.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 ### MiniCPM5-2B (C) Good for: On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data Ahead on: - Documentation and upkeep, 97 against 65 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ### 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: - Capability, 50 against 27 - Risk, 80 against 55 Also in its favour: - Agent-ready, a grade of BB or better - Rated higher by the reviewer agents, 3.8 against 3.0 out of 5 Watch for: One tool call per turn and no parallel calls ## Score by category | Category | Weight | MiniCPM5-2B | Muse Glimmer 30B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 27 | 50 | Muse Glimmer 30B +23 | | Reach | 25% (25 this run) | 90 | 90 | even | | Risk | 20% (20 this run) | 55 | 80 | Muse Glimmer 30B +25 | | Agent interface | 10% (10 this run) | 75 | 75 | even | | Documentation and upkeep | 10% (10 this run) | 97 | 65 | MiniCPM5-2B +32 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.2 · C** | **70 · BB** | | ## Facts side by side | Fact | MiniCPM5-2B | Muse Glimmer 30B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenBMB | Meta | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | yes | | Last release | 2026-09-29 | 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 | 3/5 (4) | 3.8/5 (4) | ## Verdicts **MiniCPM5-2B.** A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0. **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 ### MiniCPM5-2B 1. Smallest start is `ollama run openbmb/minicpm5-2b` (1.6 GB, q4_k_m) 2. On vLLM 0.29.0 or later run `vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5`, or tool calls come back as XML text 3. On SGLang 0.5.16 or later add `--tool-call-parser minicpm5 --reasoning-parser qwen3`; SGLang is OpenBMB's recommended backend for tool calls 4. Tool calls are XML, ``, not Hermes JSON 5. Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does 6. Expect a `` block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B 7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented 8. Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix ### 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, MiniCPM5-2B or Muse Glimmer 30B? Muse Glimmer 30B scores 70 (BB) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 2 of 5 scored categories. MiniCPM5-2B leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "muse-glimmer-30b"}`. 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