On Sierra's official τ-bench, in the latest version we checked, Qwen3.5-397B-A17B led τ² (retail, airline, telecom) at 87.9%, ahead of Gemini 3.0 Pro (85.4%) and Claude Opus 4.5 (85.3%). On the τ³ Banking cut, an agent that consults about 700 documents and applies policy, Qwen 3.8 Max led with 55.2% pass^1, ahead of Claude Opus 5 (48.7%) and Grok 4.5 (47.9%). The board does not include every model from the period, the simulated user is itself a model, and 55% still means missing almost half of the tasks in that cut.
An independent replay helps me calibrate the noise. ZenML ran 40 τ² Telecom tasks: GLM-5.3 with 39 out of 40, Qwen3.8 27B with 36, DeepSeek V4.1 Flash with 35, GPT-5.6 Sol with 33 and GPT-5.2 with 27. GPT-5.2, run twice, matched the same total and still won 6 and lost 6 different tasks. Every model swap has to fit inside variation of that kind.
The official BFCL board was not read in this pass. What exists, self reported, is Qwen3.8-Max at 72.5 on Toolathlon, in Qwen's table, and MiniMax M3 at 74.2 on MCP Atlas: a signal, not a verdict. The test that decides remains yours, with your tools, your audit trail and your sandbox.