LFM2.5-2.6B vs Qwen3.6-35B-A3B

LFM2.5-2.6B

A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.

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.

LFM2.5-2.6B

1. Quickest start is ollama run LiquidAI/lfm2.5-2.6b, 1.7 GB, or llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF
2. With vLLM or SGLang pass --tool-call-parser lfm2; for vLLM add --reasoning-parser qwen3 so the thinking comes back separately
3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set
4. Put tools in the system prompt as List of tools: [...] JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead
5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought
6. Earlier thinking is dropped from the prompt unless preserve_thinking is true
7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid
8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

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