Introduction
The ARC Prize is one of the most ambitious initiatives in artificial intelligence today, aiming to accelerate progress toward Artificial General Intelligence (AGI). The ARC-AGI Leaderboard is a public benchmark that tracks how well AI systems perform on tasks requiring fluid intelligence and adaptability—key hallmarks of human-like reasoning. The leaderboard is not just a ranking; it’s a snapshot of where the field stands and what challenges remain.
What is the ARC Prize?
The ARC Prize challenges AI systems to solve abstract reasoning tasks that are easy for humans but difficult for machines. The tasks are designed to test an AI’s ability to generalize, adapt, and reason—skills that are central to AGI. The prize offers substantial rewards for breakthroughs, incentivizing researchers and engineers to push the boundaries of what’s possible.
Understanding the Leaderboard
The leaderboard is divided into two main benchmarks: ARC-AGI-1 and ARC-AGI-2.
ARC-AGI-1 focuses on basic fluid intelligence, while ARC-AGI-2 raises the bar by requiring both high adaptability and high efficiency.
Key Metrics:
- Score (%): Measures how well the AI system performs on the tasks.
- Cost per Task ($): Reflects the computational cost of solving each task, highlighting the efficiency of the system.
- System Type: Differentiates between base language models (LLMs), chain-of-thought (CoT) systems, custom solutions, and refinement approaches.
Top Performers
As of February 2026, the leaderboard is dominated by a mix of cutting-edge models from major AI labs and innovative custom solutions from the research community. Here are some highlights:
1. Human Panel
- ARC-AGI-1: 98.0%
- ARC-AGI-2: 100.0%
- Cost/Task: $17.00 Humans remain the gold standard, but AI is closing the gap.
2. Gemini 3 Deep Think (Google)
- ARC-AGI-1: 96.0%
- ARC-AGI-2: 84.6%
- Cost/Task: $13.62 Google’s advanced reasoning model leads the pack in both performance and efficiency.
3. GPT-5.2 (Refine.) (Johan Land)
- ARC-AGI-1: 94.5%
- ARC-AGI-2: 72.9%
- Cost/Task: $38.99 A custom refinement approach, showcasing the power of iterative reasoning.
4. Claude Opus 4.6 (Anthropic)
- ARC-AGI-1: 94.0%
- ARC-AGI-2: 69.2%
- Cost/Task: $3.47 Anthropic’s latest model excels in balancing performance and cost.
5. NVARC (ARC Prize 2025)
- ARC-AGI-1: N/A
- ARC-AGI-2: 27.6%
- Cost/Task: $0.200 A custom solution from the ARC Prize community, demonstrating cost-effective innovation.
Trends and Insights
Reasoning Systems Trend Line
- Models with extended reasoning capabilities (e.g., CoT, refinement) show significant performance improvements, especially as “thinking time” increases. However, gains tend to plateau, indicating the need for new approaches.
Base LLMs vs. Custom Solutions
- Base LLMs (e.g., GPT-4.5, Claude 3.7) provide a strong foundation but often lag behind custom solutions optimized for ARC tasks.
- Custom solutions, especially those from Kaggle competitions, achieve impressive efficiency, sometimes outperforming much larger models at a fraction of the cost.
Cost-Efficiency Trade-offs
- The scatter plot reveals a clear trade-off: higher performance often comes at a higher cost. The most efficient systems (e.g., NVARC, Icecuber) balance both metrics well.
Why This Matters
The ARC Prize Leaderboard is more than a competition—it’s a barometer for AGI progress. By tracking how well AI systems perform on abstract reasoning tasks, we gain insights into:
- How close we are to human-level intelligence in machines.
- Which approaches (e.g., CoT, refinement, custom architectures) are most promising.
- The importance of efficiency: solving problems with minimal resources is a hallmark of true intelligence.
Looking Ahead: ARC Prize 2026
The ARC Prize is evolving, with plans to make 2026 even bigger. Expect new challenges, higher stakes, and more opportunities for breakthroughs. Whether you’re a researcher, engineer, or AI enthusiast, the ARC Prize offers a unique platform to contribute to the future of AGI.
Explore the Leaderboard: arcprize.org/leaderboard
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