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Taste‑Bench Reveals Wide Gap in AI Agents’ Taste, GPT‑5.6 Sol Tops with 59.7%

1 reports1 sources10/18/2026 updated

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The Taste‑Bench benchmark, released by The Decoder, evaluates 14 AI models on 502 taste‑related tasks. GPT‑5.6 Sol leads with a 59.7% score, closely followed by GPT‑5.5 at 59.5%. All other models score below 25%, falling short of random‑guess performance. A knowledge‑distillation experiment using Qwen3.6‑27B raises a student model’s score from 30% to 47.9% and boosts the success rate on SWE‑bench Pro from 14.6% to 33.7%. These results highlight a significant disparity in long‑term decision‑making quality among current AI agents.

Written with AI from the reporting · updated 10/18/2026

Latest developmentOct 18, 16:49
Taste-Bench benchmark reveals AI agents struggle with long-term decision quality
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  1. 1Unclassified fact stationOct 18, 17:50single sourcereport-time onlyrevised

    Taste-Bench benchmark released

    Read original source ↗ The DecoderReplay this moment in the explorer
    Evidence & revisions · 1 source records
    Subject：
    not recorded
    Action：
    发布 benchmark
    Object：
    Taste-Bench
    Occurred (fact side)：
    not recorded
    The Decoder ↗Report pageRevision 2 · currently revised
    Published (valid side)：
    Oct 18, 16:49
    First seen (known side)：
    Oct 18, 17:50
    Fact stationSingle source · one-sided claimRevisedReport-time only

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Oct 18
  1. The DecoderRelated fact: single sourceFact occurrence time unverifiedSource report revised
    Taste-Bench benchmark reveals AI agents struggle with long-term decision quality

    Report first recorded Oct 18, 17:50

    Taste-Bench tests 14 AI models on 502 long-term decision questions, showing even top models like GPT-5.6 Sol only achieve 59.7% accuracy, barely beating random guessing. The benchmark evaluates whether agents make quality choices at critical 'decision forks' before outcomes are revealed, highlighting a fundamental limitation in agent planning.

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