Tether Launches the 800M-Parameter BotPsy Model, Claiming Tool-Calling Performance Exceeds That of Models 2.5 Times Larger

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Tether released its open-weight model, BotPsy, on November 11, built on Alibaba’s Qwen3.5 architecture with 800 million parameters. The model converts user requests into local tool calls on personal devices. Tether claims it outperformed 1.2B-parameter models in tool-calling benchmarks, achieving a score of 46.1 on the BFCL multi-turn test compared to 23.8 for the 2B model. The model is now available on Hugging Face. This update brings fresh on-chain and crypto news for developers and AI researchers.

BlockBeats report: On November 11, Tether announced the launch of the open-weight model QVAC BotPsy, with 800 million parameters, built on Alibaba’s open-source Qwen3.5 architecture. The model enables users to convert everyday requests into local tool calls directly on their own devices. Tether stated that, in its internal testing, QVAC BotPsy achieved the highest tool-use scores among all models under 1 billion parameters and outperformed all tested models up to 1.2 billion parameters. Across four major benchmarks—BFCL v4, ACEBench, τ-bench, and ToolSandbox—it scored approximately twice as high as the base model Qwen3.5-0.8B-Think and surpassed Qwen3.5-2B-Think, a model with 2.5 times its parameter size. In particular, BFCL multi-turn tool use achieved a score of 46.1, roughly double the 23.8 score of the 2B model.


Tether introduces BotPsy, which reduces the need to send personal information such as emails and calendars to centralized service providers, and can also determine when no suitable tools are available, avoiding fabricated tool calls. Tether CEO Paolo Ardoino states that users should decide for themselves what content the assistant can access and which external services it can connect to. The model weights have been published on Hugging Face.

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