What’s the best small LLM for function calling?

Qwen3.5-4B Q4_K_M at 97.5% accuracy—runs on 8GB VRAM, crushes tools like weather or doc search.

Does bigger model mean better tool calling?

No—benchmarks show 3.4GB beating 25GB; format skills trump param count.

How do I run local LLM tool calling?

Use llama.cpp with GBNF grammars for JSON enforcement; load Qwen3.5-4B.gguf and pipe tools schema.

Tiny 3.4GB LLM Smokes 25GB Behemoths in Function Calling—Here's Why Size Doesn't Matter Anymore

Forget cramming massive models into your rig. A puny 3.4GB LLM just dominated function calling tests, freeing developers from GPU purgatory. Your next agent runs on a laptop.

theAIcatchup Apr 07, 2026 3 min read

Leaderboard chart: Qwen3.5-4B topping function calling benchmarks over larger models

⚡ Key Takeaways

3.4GB Qwen3.5-4B hits 97.5% tool calling accuracy, beating 25GB models. 𝕏
Function calling favors format obedience over knowledge—small models excel. 𝕏
Run high-precision agents on consumer GPUs with llama.cpp + GBNF. 𝕏

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#Qwen3.5 #function calling #tool use

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Originally reported by dev.to

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