# ARC-AGI-3 ![Category](https://img.shields.io/badge/Category-Reasoning%20%2F%20Vision-1f6feb) ![Metric](https://img.shields.io/badge/Metric-Accuracy-2da44e) ![Saturation](https://img.shields.io/badge/Saturation-Low-d29922) ![Created](https://img.shields.io/badge/Created-2026-555) [![Leaderboard](https://img.shields.io/badge/Leaderboard-arcprize.org-0969da)](https://arcprize.org/) > **Quick answer:** ARC-AGI-3 is the third generation of the Abstraction and Reasoning Corpus benchmark, designed as an interactive reasoning evaluation that measures fluid, novel problem-solving ability in AI systems. It is far from saturated — the current leader, GPT-5.6 Sol, scores only 7.8%, making it one of the hardest publicly tracked benchmarks for frontier models. ## At a Glance **What it tests:** Fluid, novel problem-solving through abstract visual reasoning tasks — the same grid-transformation format as ARC-AGI and ARC-AGI v2 but at a level of difficulty that current frontier models barely clear. **Why it matters:** ARC-AGI-3 serves as the frontier of abstract reasoning evaluation. With top scores below 10%, it provides maximum headroom to distinguish progress in genuine reasoning capability over the coming years, without the saturation that affected earlier ARC generations. **Known limitations:** Very few models have been evaluated (3 as of July 2026), all from OpenAI. The benchmark is early-stage and evaluation methodology is still being established. ## What ARC-AGI-3 Measures ARC-AGI-3 is the successor to ARC-AGI v2, further increasing task difficulty to maintain a meaningful challenge as AI reasoning capabilities improve. Like its predecessors, it uses visual grid transformation tasks where models must infer a hidden rule from a small set of input/output examples and apply it to a novel test case. The "interactive" framing suggests ARC-AGI-3 may involve multi-turn evaluation where models can probe or query aspects of the task — a departure from the static example-based format of previous versions. This makes it a closer proxy for how AI systems reason through genuinely novel problems in practice. Current results show a dramatic difficulty increase: while GPT-5.5 scores 85% on ARC-AGI v2, the same class of models scores below 8% on ARC-AGI-3, indicating a fundamental leap in required reasoning capability. ## Benchmark Specifications | Field | Value | |---|---| | Task category | Abstract reasoning / Vision | | Metric | Accuracy (fraction of tasks solved, 0–1) | | Saturation | Low — top score 7.8% | | Created by | ARC Prize Foundation | | Modality | Multimodal | | Parent benchmark | [ARC-AGI v2](/benchmarks/chollet-et-al-arc-prize-foundation/arc-agi-v2) | ## How ARC-AGI-3 Is Scored Tasks are scored on a 0–1 binary scale — either the model produces the correct output (1) or it does not (0). The overall score is the fraction of tasks solved. A score of 0.078 means 7.8% of tasks were correctly solved. ## ARC-AGI-3 on Benchgen > No Benchgen results yet — [be the first to run ARC-AGI-3](/benchmarks/francois-chollet/arc-agi-3). ## ARC-AGI-3 vs Other Benchmarks | Benchmark | What it tests | Saturation | Top score | |---|---|---|---| | **ARC-AGI-3** | Interactive fluid reasoning (gen 3) | Low | 7.8% | | [ARC-AGI v2](/benchmarks/chollet-et-al-arc-prize-foundation/arc-agi-v2) | Visual grid transformation (gen 2) | Medium | 85.0% | | [ARC-AGI](/benchmarks/francois-chollet/arc-agi) | Visual grid transformation (gen 1) | High | ~95% | | [GPQA Diamond](/benchmarks/rein-et-al/gpqa-diamond) | Expert science reasoning | Low | ~80% | ARC-AGI-3 is the most challenging publicly tracked abstract reasoning benchmark as of mid-2026, with essentially all current frontier models clustered near zero. ## Run ARC-AGI-3 on Your Model Track ARC-AGI-3 performance on Benchgen to get a regression-tested view of your model's abstract reasoning frontier over time. Given the extremely low current scores, even marginal improvements are significant signals. ## Related Models & Benchmarks - Top performers: [GPT-5.6 Sol](/models/openai/gpt-5-6-sol) · [GPT-5.6 Terra](/models/openai/gpt-5-6-terra) · [GPT-5.6 Luna](/models/openai/gpt-5-6-luna) - Related benchmarks: [ARC-AGI v2](/benchmarks/chollet-et-al-arc-prize-foundation/arc-agi-v2) · [ARC-AGI](/benchmarks/francois-chollet/arc-agi) · [GPQA Diamond](/benchmarks/rein-et-al/gpqa-diamond) ## Frequently Asked Questions

What is ARC-AGI-3?

ARC-AGI-3 is the third-generation Abstraction and Reasoning Corpus benchmark, created by the ARC Prize Foundation. It uses interactive reasoning tasks to evaluate fluid, novel problem-solving ability in AI systems. It is far harder than ARC-AGI v2 — current frontier models score below 8%.

What does a good ARC-AGI-3 score look like?

Any score above 0 is notable — the current leader is GPT-5.6 Sol at 7.8%. Human performance is expected to approach 1.0. As of mid-2026, ARC-AGI-3 remains essentially unsolved.

Is ARC-AGI-3 saturated?

No. With a top score of only 7.8% and most frontier models near 0%, ARC-AGI-3 is one of the least saturated reasoning benchmarks currently tracked.

How does ARC-AGI-3 differ from ARC-AGI v2?

ARC-AGI-3 features harder task designs and an interactive evaluation format. While frontier models score 49–85% on ARC-AGI v2, the same models score below 8% on ARC-AGI-3, indicating a fundamental jump in required reasoning capability.

Rank	Model	Score
1	gpt-6-astra	99.9
2	gpt-5-6-sol	0.078
3	gpt-5-6-terra	0.008
4	gpt-5-6-luna	0.002

ARC-AGI-3 — Results

ARC-AGI-3 — Evaluations