# MiniCPM5-2B vs SmolLM3-3B > SmolLM3-3B and MiniCPM5-2B score within a point of each other for open-weight models you run yourself, 61 and 60.2 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b - Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b.md (~5,850 tokens) - Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b.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 SmolLM3-3B and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on reach and 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 - SmolLM3-3B: grade C, 61/100, rank #47 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.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: - Reach, 90 against 85 - Documentation and upkeep, 97 against 54 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ### SmolLM3-3B (C) Good for: Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device Ahead on: - Risk, 65 against 55 - Agent interface, 80 against 75 Watch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json ## Score by category | Category | Weight | MiniCPM5-2B | SmolLM3-3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 85 | MiniCPM5-2B +5 | | Risk | 20% (20 this run) | 55 | 65 | SmolLM3-3B +10 | | Agent interface | 10% (10 this run) | 75 | 80 | SmolLM3-3B +5 | | Documentation and upkeep | 10% (10 this run) | 97 | 54 | MiniCPM5-2B +43 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.2 · C** | **61 · C** | | ## Facts side by side | Fact | MiniCPM5-2B | SmolLM3-3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenBMB | Hugging Face | | 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 | no | | Last release | 2026-09-29 | 2025-09-10 | | 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) | 3.3/5 (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. **SmolLM3-3B.** A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025. ## 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 ### SmolLM3-3B 1. Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text 2. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg 3. Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history 4. Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens 5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling 6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none 7. Validate JSON output yourself or use the runtime's guided decoding ## Questions ### Which is better for AI agents, MiniCPM5-2B or SmolLM3-3B? SmolLM3-3B and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on reach and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-smollm3-3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "smollm3-3b"}`. 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