# MiniCPM5-2B vs Nemotron 3 Nano 4B > MiniCPM5-2B scores 60.2 (C) to Nemotron 3 Nano 4B's 56.8 (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-nemotron-3-nano-4b - Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-nano-4b.md (~6,200 tokens) - Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-nano-4b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-nano-4b.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 MiniCPM5-2B scores 60.2 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 5 scored categories. Nemotron 3 Nano 4B leads on risk. 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 - Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.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: - Agent interface, 75 against 70 - Documentation and upkeep, 97 against 55 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ### Nemotron 3 Nano 4B (C) Good for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English Ahead on: - Risk, 65 against 55 Watch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository ## Score by category | Category | Weight | MiniCPM5-2B | Nemotron 3 Nano 4B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 27 | 25 | MiniCPM5-2B +2 | | Reach | 25% (25 this run) | 90 | 90 | even | | Risk | 20% (20 this run) | 55 | 65 | Nemotron 3 Nano 4B +10 | | Agent interface | 10% (10 this run) | 75 | 70 | MiniCPM5-2B +5 | | Documentation and upkeep | 10% (10 this run) | 97 | 55 | MiniCPM5-2B +42 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.2 · C** | **56.8 · C** | | ## Facts side by side | Fact | MiniCPM5-2B | Nemotron 3 Nano 4B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenBMB | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | NVIDIA Nemotron Open Model License | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-09-29 | 2026-03-16 | | 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. **Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March. ## 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 ### Nemotron 3 Nano 4B 1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build 2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file 3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3` 4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead 5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks 6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says 7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented ## Questions ### Which is better for AI agents, MiniCPM5-2B or Nemotron 3 Nano 4B? MiniCPM5-2B scores 60.2 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 5 scored categories. Nemotron 3 Nano 4B leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-nano-4b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "nemotron-3-nano-4b"}`. 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