Taste-Bench benchmark evaluates agents' long-term decision-making
What happened
On 18 October 2026, The Decoder reported that researchers have proposed a new benchmark, Taste-Bench, to test AI agents' ability to judge trade-offs in long-horizon tasks. The benchmark contains 502 decision-fork questions. According to the report, the best-performing model, GPT-5.6 Sol, reached only 59.7% accuracy, with other models far below, indicating that AI agents' long-term decision-making judgement is insufficient. So far this is the only report on the matter, with no further developments.
Written by AI from the reports · updated 5 h ago
Timeline
Follow the reports to see the event from each side.
- The DecoderAI agents have bad taste in long-term decisions and a new benchmark proves it
A new benchmark called Taste-Bench tests how well AI agents make strategic choices during long-running tasks. Even the best model, GPT-5.6 Sol, picks the right path on just under 60 percent of simple binary questions. Thinking longer doesn't help.
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