The ARC Prize Foundation is a nonprofit organization dedicated to open scientific progress through enduring AI benchmarks.
We make tools that provide empirical data about intelligence capabilities which guide critical industry decisions about research, safety, and policy.
A core tenet of ARC Prize Foundation is to measure progress of new AI models. We do this by keeping an up to date leaderboard which is separate from the competition leaderboard.
This leaderboard has no limitations on internet access or compute. It is intended to test state of the art models and bespoke solutions.
To find out more about the ARC Prize Foundation, please visit our about page.
Our evaluation methodology’s code is open sourced and can be viewed at the Model Baseline repository. This means that anyone can clone, audit or run another instance of our results to reproduce the analysis.
All relevant data points—including model outputs, evaluation durations, costs, and submission details—are publicly available on HuggingFace.
The ARC Prize benchmarks are designed to measure AI progress, not to serve as a target for optimization. ARC Prize tasks are not economically useful to target, instead they are a measure of AI capability. Our goal is to provide an objective assessment of model capabilities rather than influence model training through repeated, iterative testing.
For public evaluation tasks, both the raw submission data and individual task scores will be shared alongside the overall model score.
For semi-private evaluation tasks, only the aggregate model score will be publicly shared—individual task results will remain private.
We test new models as they are publicly released (open weights or API) to provide transparent and standardized evaluations.
Our evaluation process follows these steps:
For models accessible via a public API, the turnaround time from evaluation to result publication is typically within 2 days.
We collaborate with major open-source and commercial model providers to test unreleased models for the community. A model is considered unreleased if its weights are neither open nor available via a public API or service. The models that are tested must be intended for public launch.
Our approach to testing unreleased models:
This approach ensures that ARC Prize remains an independent evaluator while still enabling model providers to understand performance before public launch.
Many researchers will also develop custom solutions to ARC-AGI. Examples include submissions from MIT/Cornell and Jeremy Berman.
Bespoke Solution Rules
Submissions
The ARC Prize team will verify solutions and scores within 2 weeks of submission.
Only new high score claims (e.g., submissions that exceed the current high score by at least 1%) will be verified and published. This is to limit the semi-private evaluation set from leaking into LLM training data.
If you have a solution that is expensive to run, we encourage you to test by randomly sampling 100 tasks from the 400 public evaluation tasks and hold out the remainder for private validation. This can build confidence towards your overall ARC-AGI-Pub score before incurring significant cost with the full 400 task dataset.
Submit an ARC-AGI-Pub high score claim.
Compute and/or provider costs can be significant to run solutions against evaluation sets. To help support those contributing to this initiative, we’ve set up a verification fund.
ARC-AGI submission to be verified can use up to $10,000 in resources over the 500 puzzles (400 public evaluation set + 100 semi-private evaluation set.). We retain the right to refuse verification of any previous benchmarks in the event of future benchmark releases (such as ARC-AGI-2 or 3).
For each new proven SOTA reproduction (i.e., when a solution scores higher than the current highest score on this leaderboard on the public evaluation set), we will reimburse up to $2,500.
This fund is a work-in-progress and we reserve the right to make changes at any time or refuse reimbursement requests upon consideration by the ARC Prize team.
We will continuously add new models and retire old ones. It is not feasible to add every possible model due to the cost and the scalability of our evaluation process.
We want to assess performance across different levels of reasoning. To do this, we will repeat model tests at spaced reasoning levels.
No it doesn't. This is a common misconception. The ARC-AGI-Pub leaderboard is open to all models. Here's a example of a prompt that was used to test on ARC-AGI: Prompt
If a model is open source and not available via API by the model creator we will use another public model provider. This includes companies like Baseten or TogetherAI.
Cost is a critical factor in model evaluation, and whenever possible, we will use retail pricing to assess cost efficiency. For model providers, we will base cost calculations on publicly available retail rates—typically measured in price per million tokens—rather than a provider’s internal margins or raw cost of goods. Costs are generally shared on an average per-test-pair-attempt basis.
We are a nonprofit that seeks to provide transparency and all tools. We invite the community to reproduce our results.
The ARC Prize Foundation is funded exclusively through donations, including financial contributions, cloud credits, and API credits.
We do not accept funding from AI model providers for core benchmark and leaderboard evaluations. Our evaluations are conducted independently, and we take potential conflicts of interest very seriously. Our commitment is to scientific rigor, transparency, and impartiality in AI benchmarking.
If you’d like to support our work, please visit our donation page.
Feel free to contact us at: team@arcprize.org