# MiniCPM5-2B vs Qwen3.6-27B > Qwen3.6-27B scores 67.2 (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-qwen3-6-27b - Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-27b.md (~6,000 tokens) - Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-27b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-27b.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 Qwen3.6-27B scores 67.2 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 1 of 5 scored categories. MiniCPM5-2B leads on 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 - Qwen3.6-27B: grade B, 67.2/100, rank #11 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.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 36 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ### Qwen3.6-27B (B) Good for: Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms. Ahead on: - Capability, 66 against 27 Watch for: No safety results, prompt-injection guidance or security contact ## Score by category | Category | Weight | MiniCPM5-2B | Qwen3.6-27B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 27 | 66 | Qwen3.6-27B +39 | | Reach | 25% (25 this run) | 90 | 90 | even | | 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 | 36 | MiniCPM5-2B +61 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.2 · C** | **67.2 · B** | | ## Facts side by side | Fact | MiniCPM5-2B | Qwen3.6-27B | | --- | --- | --- | | 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 | 2026-04-22 | | 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) | 3/5 (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. **Qwen3.6-27B.** A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it. ## 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 ### Qwen3.6-27B 1. Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector 2. Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later 3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call 4. Thinking is on by default, pass `"chat_template_kwargs": {"enable_thinking": false}` for short tasks, and `/think` and `/nothink` aren't supported 5. `"preserve_thinking": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt 6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without 7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens 8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented 9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes ## Questions ### Which is better for AI agents, MiniCPM5-2B or Qwen3.6-27B? Qwen3.6-27B scores 67.2 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 1 of 5 scored categories. MiniCPM5-2B leads on agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "qwen3-6-27b"}`. 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How: https://www.anchorterminal.com/agents/#mcp