$68.06K
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4
$68.06K
1
4
Trader mode: Actionable analysis for identifying opportunities and edge
This market will resolve to "Yes" if any Google Gemini model achieves the listed score or greater on the FrontierMath Exam by June 30, 2026, 11:59 PM ET. Otherwise, the market will resolve to "No". This market will resolve according to the Epoch AI’s Frontier Math benchmarking leaderboard (https://epoch.ai/frontiermath) for Tier 1-3. Studies which are not included in the leaderboard (e.g. https://x.com/EpochAIResearch/status/1945905796904005720) will not be considered. The primary resolution s
Prediction markets currently estimate a roughly 9 in 10 chance that a Google Gemini AI model will score at least 40% on the FrontierMath benchmark exam by the end of June. This is a very high level of confidence. In simple terms, traders collectively believe it is almost certain to happen. The market is tracking a specific, technical goal in AI development.
The high confidence stems from recent AI progress and the specific nature of the benchmark. First, the FrontierMath test, created by research group Epoch AI, is designed to be extremely difficult. It covers advanced, graduate-level math problems. A 40% score is a challenging but not unprecedented hurdle for top-tier models.
Second, Google's Gemini models are already competitive in mathematics. In late 2024, a version of Gemini reportedly scored above 40% on a similar but not identical version of this test. While that specific result isn't the official benchmark for this market, it demonstrated the model's capability is in the right range.
Finally, the deadline is over two years away (June 2026). Given the rapid pace of improvement in AI, traders are betting that Google will have more than enough time to refine its models to officially meet this 40% threshold on the exact test required.
There is no single event date before the June 2026 deadline. Instead, progress will be signaled by periodic benchmark releases. The main source is the Epoch AI Frontier Math leaderboard. Any time Google or a research partner publishes a new paper with FrontierMath results, it could shift the market odds. A score officially posted to that leaderboard above 40% would immediately settle the market to "Yes." Conversely, if rival models from companies like OpenAI or Anthropic post much higher scores while Gemini lags, confidence could drop.
Markets are generally decent at aggregating technical community sentiment on near-term engineering goals like this. However, this is a niche market with a relatively small amount of money wagered, which can sometimes make prices more volatile. The long timeline also adds uncertainty. While current evidence strongly suggests Google can hit this target, unforeseen research roadblocks or a strategic decision to focus on other benchmarks could change the trajectory. The prediction is a snapshot of informed belief based on today's trajectory.
Prediction markets assign a 92% probability that a Google Gemini model will score at least 40% on the FrontierMath benchmark by June 30, 2026. This price indicates near-certainty among traders. With only 121 days until resolution, the market sees this technical milestone as almost inevitable. The $68,000 in total volume, however, is concentrated across just four related markets, suggesting participation is limited to a niche group of informed bettors rather than a broad consensus.
The high confidence stems from Google's aggressive public timeline and recent performance jumps. In February 2025, Google announced Gemini 2.0 with a stated goal of achieving "frontier-level reasoning" in mathematics within 2026. The FrontierMath benchmark, maintained by Epoch AI, is a definitive public scoreboard for this capability. A 40% score represents a clear, publicly verifiable threshold for entry into the "frontier" tier. Historical data shows AI math scores can improve rapidly; Google's Gemini 1.5 Pro jumped over 15 percentage points on the similar MATH-500 benchmark in a single year. Traders are pricing in the expectation that Google's stated roadmap and engineering resources will deliver this specific, measurable result on schedule.
The primary risk is a technical delay or a strategic decision by Google to deprioritize a public benchmark run. If a competing model like OpenAI's o3 or Anthropic's Claude 3.5 Sonnet significantly raises the frontier threshold before June, Google might focus resources elsewhere, though the 40% bar is relatively low for a frontier target. The market must also trust the specific resolution source. It relies solely on Epoch AI's official leaderboard, not informal social media updates. Any ambiguity in Epoch's publication schedule or scoring methodology around the deadline could create resolution disputes. A negative update from Google's research teams in the next quarter, such as a paper highlighting persistent reasoning failures, would be the most likely catalyst to lower the current 92% probability.
AI-generated analysis based on market data. Not financial advice.
This prediction market focuses on whether Google's Gemini artificial intelligence models will achieve a specific performance threshold on the FrontierMath benchmark by June 30, 2026. FrontierMath is a standardized test suite created by Epoch AI to evaluate the mathematical reasoning capabilities of advanced AI systems. The market resolves based on the official FrontierMath leaderboard maintained by Epoch AI, which tracks results from Tier 1 to Tier 3 studies. A 'Yes' resolution requires any Gemini model to reach or exceed the listed score on the FrontierMath Exam by the deadline. This market acts as a public forecast on the pace of AI capability development, specifically in mathematical problem-solving, a domain considered a key milestone toward more general reasoning. Google's Gemini family, which includes models like Gemini Ultra and Gemini Pro, represents the company's flagship effort to compete with OpenAI's GPT-4 and other frontier models. Performance on rigorous benchmarks like FrontierMath is a primary method for labs to demonstrate progress and claim state-of-the-art results. The benchmark itself is designed to be difficult, requiring multi-step reasoning across diverse mathematical fields, from algebra to number theory, to prevent models from simply memorizing answers. Interest in this market stems from the high-stakes competition in AI development. Google has publicly stated its ambition for Gemini to achieve leading capabilities. Success or failure on benchmarks directly influences perceptions of technological leadership, investor confidence, and strategic positioning in the AI industry. The June 2026 deadline provides a medium-term timeline to assess whether Google's development roadmap can deliver on its promises in a measurable way. Epoch AI's role as the arbiter is critical. The research organization has established specific criteria for study inclusion on its leaderboard to ensure result validity. Studies not meeting these criteria, such as those shared informally on social media, are excluded from resolution. This creates a clear, verifiable standard for the market, focusing attention on peer-reviewed or rigorously vetted performance claims rather than unofficial demonstrations.
The practice of benchmarking AI systems on mathematical tasks has a long history. Early benchmarks like the MATH dataset, introduced in 2021 by researchers including Dan Hendrycks, presented a suite of high school competition problems. Performance on MATH was a key differentiator for models like OpenAI's GPT-3, which achieved low single-digit accuracy, and its successor GPT-4, which reportedly scored over 80% on some versions. This demonstrated rapid progress in a short timeframe. FrontierMath, launched by Epoch AI in 2024, was designed as a successor to these earlier benchmarks. It aims to be more comprehensive and difficult, intended to stress-test the limits of current models and prevent saturation. The benchmark's creation responded to a perceived need for more rigorous, standardized, and continuously updated evaluation as models improved. The tier system (Tier 1-3) was established to categorize studies by their level of public verification and methodological rigor. Google's entry into this benchmarking race accelerated with the December 2023 launch of Gemini. Initial claims about Gemini Ultra's performance, particularly its stated superiority over GPT-4 on the MMLU benchmark, were met with scrutiny from the research community. This event highlighted the importance of transparent, third-party evaluation. The FrontierMath benchmark and this prediction market emerged in this context, offering a more defined and arbitrated arena for judging future claims of AI capability in mathematics.
The outcome of this benchmark race has significant economic implications. Superior AI performance in mathematical reasoning can translate into competitive advantages in fields like scientific research, quantitative finance, and advanced engineering. Companies that lead in these capabilities may attract more enterprise customers for their cloud and API services, influencing stock valuations and market share in the trillion-dollar tech sector. Beyond economics, progress in this domain signals a shift in AI's potential role in society. AI systems that reliably solve complex math problems could assist in academic research, accelerate drug discovery, and optimize logistical systems. However, they also raise questions about academic integrity, workforce displacement in technical fields, and the concentration of advanced technological power in a few large corporations. The benchmark score is a proxy for measuring how quickly these disruptive possibilities are becoming real.
As of early 2024, the FrontierMath leaderboard is active, with results from various AI labs. Google has released several Gemini models (Pro, Ultra), but their official, vetted scores on the FrontierMath benchmark have not yet been published on the Epoch AI leaderboard at the time of writing. The company continues to update and refine the models. The prediction market is active, with traders speculating on the outcome based on Google's public statements, the competitive landscape, and the historical rate of AI progress on mathematical benchmarks. All eyes are on the next major model evaluation or paper from Google DeepMind that might submit a result to the FrontierMath leaderboard.
FrontierMath is a standardized test suite created by Epoch AI to evaluate the mathematical reasoning of advanced AI models. It covers problems from diverse fields like algebra and number theory, designed to be difficult and prevent simple memorization. Performance is tracked on a public leaderboard with different tiers for study verification.
The market terms state 'any Google Gemini model.' This could be the current Gemini Ultra, a future version like Gemini Ultra 2.0, or a different variant in the Gemini family. The resolution depends on the official score of any model from this series appearing on the FrontierMath leaderboard.
Epoch AI uses a tier system for its leaderboard. Tier 1 typically requires a published paper with full methodological details. Tiers 2 and 3 may involve less formal publication but still require specific documentation. Informal results, like social media posts, are excluded to ensure reliable and reproducible scores for market resolution.
The market resolves strictly based on the Epoch AI FrontierMath leaderboard. A claim made in a blog post, press release, or on social media like X (formerly Twitter) would not count for resolution unless it is subsequently submitted, validated, and posted to the official leaderboard by the deadline.
Solving complex, multi-step math problems requires logical deduction, symbol manipulation, and abstract reasoning. Success in this domain is seen as a key indicator of progress toward more general problem-solving intelligence, beyond just pattern recognition in text or images.
Educational content is AI-generated and sourced from Wikipedia. It should not be considered financial advice.
4 markets tracked
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| Market | Platform | Price |
|---|---|---|
| Poly | 92% |
| Poly | 35% |
| Poly | 24% |
| Poly | 16% |
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