LFM2.5-2.6B vs Qwen3.5-9B

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.

Qwen3.5-9B

A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.

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

Qwen3.5-9B

1. Smallest start is ollama run qwen3.5:9b (6.6 GB, q4_k_m)
2. On vLLM run vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, or tool calls come back as text
3. Tool calls use the XML style <tool_call><function=name><parameter=x>, not Hermes JSON, so use the qwen3_coder parser and not hermes
4. Thinking is on by default; pass chat_template_kwargs with enable_thinking false for short tasks, since /nothink isn't supported
5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without
6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding