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RegretBench (normalized regret)

General QA · 2026-07-24

RegretBench's primary policy metric on the open-domain QA subset: the whole-dialogue reward lost relative to the benchmark's reference clarification planner, divided by the planner's margin over the best immediate-answer baseline. Zero matches the reference planner, one matches answering immediately, and values above one are worse than not clarifying.

Top models (lower is better)

ModelScore
Gemini 3.1 Pro Preview0.6
GPT-4.10.6
DeepSeek-V4-Flash0.6
Grok 4.1 Fast0.7
Llama 4 Maverick Instruct0.7
Qwen3.5-Flash (source unspecified)0.7
GPT-5 Mini0.8
Gemini 3.1 Flash-Lite0.9
GPT-5.4 Nano0.9
Nemotron 3 Super 120B A12B0.9
Llama 4 Scout Instruct1.0
K2-V2-Instruct1.0
K2 Think V28.8
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