GPT Astra 6.1+: HLE-Diamond Debut?

根据「GPT Astra 6.1+: HLE-Diamond Debut?」的最新预测市场数据，交易者已形成强烈共识。

目前，60%+ 以压倒性的 63% 获胜概率主导市场；55%+ 以 59% 位居第二，65%+ 以 25% 排名第三。该市场的下注量已达 $114.2K，反映出市场的高度关注。

为了更好地评估各潜在结果的位置，可依据隐含概率与合约定价将市场划分为三个明显的交易梯队：

🥇 第一梯队：绝对领跑者

• 60%+ (63%)：60%+ 目前拥有最高概率，深受订单簿青睐。看好该结果的交易者面对的「Buy Yes」合约价为 63¢，显示出市场的高度确信。仅该合约就已产生 $23.5K 的成交量。

🥈 第二梯队：主要挑战者

• 55%+ (59%)：作为最可行的替代选项，55%+ 保持着 59% 的成真概率，其「Buy Yes」份额目前成交价为 59¢。
• 65%+ (25%)：以 25% 的概率位列第三，市场对 65%+ 持谨慎怀疑态度，除非势头转变，否则视其为外围黑马。

🥉 第三梯队：长尾选项（合计约 0%）

在前三名之外，还有大量宏观变量与冷门结果被持续追踪。尽管单个概率偏低，但它们是投机交易者的重要对冲：

• 替代选项：包括 70%+ (19.5%)。
• 投机成交：尽管统计概率偏低，像 70%+ 这类长尾合约仍吸引着可观的关注。

This market will resolve to "Yes" if the next OpenAI GPT Astra model (6.1+) added to the Humanity's Last Exam results at https://agi.safe.ai/ has an HLE-Diamond Accuracy of at least the specified percentage at 12:00 PM ET on the calendar date following the date on which it first appears on the site. Otherwise, this market will resolve to "No".

A qualifying model must be an OpenAI model designated as version 6.1 or higher that includes "Astra" in its name or model identifier, regardless of capitalization or surrounding prefixes, suffixes, dates, or descriptors. For example, GPT-6.1 Astra, Astra 6.1, or GPT Astra 7 would all qualify. GPT-6 Astra, including any rollout, promotion, Pro or Fast mode, or snapshot of it, will not qualify. Tier-labeled models such as GPT-5.6 Sol, GPT-5.6 Terra, or GPT-5.6 Luna and their successors, and task-specific or modality lines such as GPT-5.6-Cyber, Daybreak models, Codex, GPT Image, or Realtime models, will not qualify.

The percentage displayed as "HLE-Diamond Accuracy" for the model in its result card on the "AI Progress on HLE-Diamond" chart at https://agi.safe.ai/ will be used to resolve this market. This market will resolve solely based on the HLE-Diamond Accuracy, regardless of the model's Calibration Error, chart position, other configuration scores, any note that a score was computed on a sample of questions, or any underlying granular or unrounded data presented elsewhere.

If the Center for AI Safety (CAIS), which maintains the official Humanity's Last Exam results, publishes a qualifying score (defined below) for the model, and the model has not been displayed on the chart by 11:59 PM ET on the third calendar day (ET) after the first such publication, that score will be used instead, rounded to one decimal place. If multiple qualifying scores for the model have been published by that time, the highest will be used.

A qualifying score is one issued under CAIS's name (for example, on https://agi.safe.ai/, https://lastexam.ai/, or https://dashboard.safe.ai/, including jointly with Scale AI) that is clearly identified as overall accuracy on the full HLE-Diamond question set (1,000 questions, released September 22, 2026), without tools, at the reasoning setting labeled "high" in HLE-Diamond's main results.

No score other than the chart value or a qualifying score will count, even if no qualifying score is published. This includes scores on the original Humanity's Last Exam or HLE-Rolling, "reasoning max" or "with tools" results, Reasoning or Knowledge partition scores, values available only through an API, data file, or page source, scores from Scale AI's separate leaderboards, other evaluators, or the model's developer, and any score where there is reasonable doubt that it meets the conditions above.

A model first appears on the site on the earlier of the date (ET) it is first displayed on the chart and the date (ET) a qualifying score for it is first published. If the chart value is used, the check described in the first paragraph takes place at 12:00 PM ET on the calendar date following the model's first display on the chart; if a qualifying score is used, it takes place at the end of the three-day period described above.

If multiple qualifying models are added to the Humanity's Last Exam results on the same calendar date (ET), the model with the highest HLE-Diamond Accuracy will be used for resolution. Models added to the results on the calendar date following the initial qualifying model's first appearance will not be considered.

If a model is first added to the Humanity's Last Exam results but is subsequently removed and not re-added, such that no HLE-Diamond Accuracy for it is available under these rules at the time of the check, its appearance will not qualify as added to the Humanity's Last Exam results.

A qualifying model must be newly added to the Humanity's Last Exam results. Whether the model was previously released, publicly accessible, in beta, or otherwise available before appearing on the site is irrelevant for this market.

In the case that the Resolution Source publishes data, sufficient to settle a Market, that is clearly erroneous and the result of technical or operational error, or of unauthorized interference with the Resolution Source or its data, the Market may remain open for a period of up to seven (7) calendar days (ET) from the time of the original release, in anticipation of a correction by the Resolution Source. If the Resolution Source publishes a corrected value within that period, that value will be eligible for resolution. If no corrected value is published within that period, Polymarket will issue a Clarification to determine whether the originally published value may be eligible for resolution, or whether other action is required to preserve the orderly functioning of the Market.

The primary resolution source for this market is the official Humanity's Last Exam website found at https://agi.safe.ai/. If this resolution source is unavailable at the time of the check, this market will resolve based on the first subsequent instance at which the model's HLE-Diamond Accuracy becomes available. If it remains unavailable through the end of the seventh day after the qualifying model first appears on the site or if no qualifying model is added by June 30, 2027, 11:59 PM ET, this market will resolve to "No".

人群共识与投机成交塑造了更宏观的预测市场，而我们的量化算法提供了数据驱动的反向视角。通过分析基本面信号、底层趋势与历史分布，我们的 AI 估值模型为每个结果独立测算出一个「公允价值」概率。

将该公允价值与当前交易价值对比，可揭示出重大背离——即期望值（EV）差。正 EV 差代表统计上被低估的结果，而负 EV 差则提示市场可能存在反应过度。

顶级 AI Alpha 与错误定价套利机会

根据最新一轮数据模型测算，以下几个关键合约存在显著偏离：

• 最被高估的结果：70%+ 当前交易价为 19.5%，但我们的 AI 测算其公允价值仅为 12.1%，形成 -7.4% 的较大负 EV 差，表明人群可能过度炒作该结果、把溢价推得过高。
• 最佳价值标的（最高 EV）：我们的模型将 55%+ 识别为盘面上最具价值的机会。市场仅给予其 59% 的交易概率，而我们 AI 的公允价值评估为 85.2%——形成可观的 +26.2% EV 差。
• 被忽视的黑马：其他值得注意的偏离包括 65%+（EV 差：+7.9%）。尽管我们的预测模型给予更强的统计支撑，这些长尾机会仍被实时订单簿大幅低估。