# MiniCPM5-2B vs Qwen3.5-397B-A17B > Qwen3.5-397B-A17B scores 64.6 (B) 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-qwen3-5-397b-a17b - Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-397b-a17b.md (~6,200 tokens) - Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-397b-a17b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-397b-a17b.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 Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 1 of 5 scored categories. MiniCPM5-2B leads on reach, agent interface 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 - Qwen3.5-397B-A17B: grade B, 64.6/100, rank #18 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.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 70 - Agent interface, 75 against 70 - Documentation and upkeep, 97 against 39 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ### Qwen3.5-397B-A17B (B) Good for: Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence. Ahead on: - Capability, 72 against 27 Watch for: No build fits a 256 GB machine, and Ollama has no local build ## Score by category | Category | Weight | MiniCPM5-2B | Qwen3.5-397B-A17B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 27 | 72 | Qwen3.5-397B-A17B +45 | | Reach | 25% (25 this run) | 90 | 70 | MiniCPM5-2B +20 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 75 | 70 | MiniCPM5-2B +5 | | Documentation and upkeep | 10% (10 this run) | 97 | 39 | MiniCPM5-2B +58 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.2 · C** | **64.6 · B** | | ## Facts side by side | Fact | MiniCPM5-2B | Qwen3.5-397B-A17B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenBMB | Alibaba (Qwen team) | | 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 | 2026-02-16 | | 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) | 2.8/5 (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. **Qwen3.5-397B-A17B.** Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply. ## 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 ### Qwen3.5-397B-A17B 1. The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory 2. It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B) 3. On Model Studio pass `"enable_thinking": false` directly, while vLLM and SGLang take `"chat_template_kwargs": {"enable_thinking": false}` 4. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without 5. For multi-token prediction in vLLM add `--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'` 6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens 7. Validate any JSON it returns, since structured output isn't documented ## Questions ### Which is better for AI agents, MiniCPM5-2B or Qwen3.5-397B-A17B? Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 1 of 5 scored categories. MiniCPM5-2B leads on reach, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-397b-a17b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-397b-a17b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "qwen3-5-397b-a17b"}`. 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