Qwen3.6-35B-A3B vs SmolLM3-3B

Qwen3.6-35B-A3B

A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.

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.

Qwen3.6-35B-A3B

1. Start it with ollama run qwen3.6:35b (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)
2. On the reference machine use vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, BF16, since vLLM doesn't document FP8 compute on gfx1151
3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none

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