# Function Calling

Tether AI Research released BotPsy, a 0.8B-parameter on-device personal assistant built on Qwen3.5-0.8B and post-trained in four stages for tool calling. Against its base model, BFCL v4 rises from 23.4 to 44.0, ACEBench from 30.1 to 58.7 and τ-bench from 23.2 to 51.5; it leads five of six tool-use and agentic benchmarks, trailing only on Claw-Eval behind the 2.5-times-larger Qwen3.5-2B-Think.

Tether AI Research released BotPsy, a 0.8B-parameter on-device personal assistant built on Qwen3.5-0.8B and post-trained in four stages for tool calling. Against its base model, BFCL v4 rises from 23.4 to 44.0, ACEBench from 30.1 to 58.7 and τ-bench from 23.2 to 51.5; it leads five of six tool-use and agentic benchmarks, trailing only on Claw-Eval behind the 2.5-times-larger Qwen3.5-2B-Think.

Tether AI Research released BotPsy, a 0.8B-parameter model built on Qwen3.5-0.8B for function calling and agentic workflows that runs on-device. Trained through supervised fine-tuning, multi-teacher on-policy distillation and two reinforcement-learning rounds, it lifts BFCL v4 from its base's 23.4 to 44.0 and τ-bench from 23.2 to 51.5, and beats Qwen3.5-2B-Think, a model 2.5 times its size, on five of six tool-use and agentic benchmarks, trailing only on Claw-Eval (46.5 vs 48.9). It still makes mistakes, targets low-to-medium difficulty tasks and is not intended for high-stakes use.

Tether AI Research released BotPsy, a 0.8B-parameter model built on Qwen3.5-0.8B for function calling and agentic workflows that runs on-device. Trained through supervised fine-tuning, multi-teacher on-policy distillation and two reinforcement-learning rounds, it lifts BFCL v4 from its base's 23.4 to 44.0 and τ-bench from 23.2 to 51.5, and beats Qwen3.5-2B-Think, a model 2.5 times its size, on five of six tool-use and agentic benchmarks, trailing only on Claw-Eval (46.5 vs 48.9). It still makes mistakes, targets low-to-medium difficulty tasks and is not intended for high-stakes use.