MiniCPM5-2B vs Qwen3.6-27B

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.

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, <function name=...><param name=...>, 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 <think> 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