Tether Releases QVAC BotPsy for Tool-Using AI on Personal Devices

What happened

Tether AI Research announced the release of QVAC BotPsy-0.8B on November 11, 2026, an open-weight (a model whose trained parameters anyone may download) (published so anyone may download the trained model), 0.8-billion-parameter model for tool calls on personal devices, and reported that it outperformed every tested model of up to 1.2 billion parameters across six agentic (AI that carries out multi-step tasks rather than answering one question) benchmarks. That test measures whether an agent can keep working as new information arrives, such as checking availability, handling a conflict and updating a calendar.

Built on Alibaba’s Qwen3.5-0.8B, BotPsy specializes in function calling and multi-step agentic workflows for local deployment. The surrounding application supplies service connections, permissions and execution controls.

In Tether’s ACEBench example, a message failed because Wi-Fi was off and the inbox was full. BotPsy enabled Wi-Fi and freed inbox space before sending the message, while its base model repeated the failed action.

The model can also recognize when none of the available tools fits a request, an important behavior for an assistant acting inside real applications. Specialization Drives the Benchmark Gains In Tether’s model-card evaluation , BFCL v4 rose from 23.4 for the Qwen3.5-0.8B-Think base to 44.0 for BotPsy, ACEBench from 30.1 to 58.7, and ToolSandbox from 30.5 to 64.6.

BotPsy also beat Qwen3.5-2B-Think on five of the six agentic benchmarks (a standard test used to compare systems); the larger model led on Claw-Eval, 48.9 to 46.5. All five models were evaluated in thinking mode with a shared harness, and the scores are percentages.

The largest gain concerns continuity across turns. Tether reports a BFCL v4 multi-turn score of 46.1 for BotPsy, against 7.5 for its base and 23.8 for Qwen3.5-2B-Think.