Reported in recent model launches

These model reports include this benchmark. Their harnesses, task subsets, scoring policies, and resource budgets can differ; follow each report’s evaluation notes before joining the numbers.

OpenAI · GPT-6 Astra →

What it measures

Few-shot grid transformations test the discovery and application of unfamiliar rules.

How to read the result

Inspect the solver’s representations, search, and allowed attempts rather than treating the model identifier as the complete system.

Limits and comparison traps

Public examples and solutions are widely studied. Keep public and private evaluation results separate and report test-time compute.

Benchmark reference notes

400 public / 100 semi-private abstract grid-transformation visual fluid-intelligence tasks requiring few-shot program induction.

Epoch data and evaluation setting

Epoch AI publishes this benchmark in its capability collection. Imported rows retain the published score column and model configuration. Original authors and evaluation providers remain credited in the result references.

Epoch attribution and score coverage

Data is attributed to Epoch AI under CC BY 4.0, with original evaluation providers credited in the linked source snapshots. Scores are imported only where a published mapping identifies the field and its scale; missing or incompatible records are omitted rather than assigned zero.

Where this fits in an evaluation suite

Ask what the task actually requires: recall, deduction, search, or a mixture. Separate closed-book reasoning from retrieval and tool-assisted problem solving.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Pin the benchmark release, task split, model configuration, tools, sampling budget, and evaluation harness described by the primary source.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.

PRIMARY EVIDENCE & FURTHER READING

  1. Primary benchmark documentation or model evaluation report ↗
  2. ARC-AGI-1 ↗

    Epoch AI

  3. Capabilities & benchmarking — retained external evaluation records ↗

    Epoch AI · 2026

Reviewed 2026-10-26. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.