Best LLM for reasoning in 2026: ARC-AGI-2 benchmark results
- BRACAI
- 5 hours ago
- 2 min read
Ever wondered which AI model is best at real reasoning?
Reasoning benchmarks are tests that score AI models on tasks that require learning, not memorizing. Think of them as a leaderboard for fluid intelligence.
Most benchmarks reward knowledge.
ARC-AGI-2 rewards adaptability.
That is why ARC-AGI-2 is one of the most important benchmarks right now for measuring true general reasoning.
Why should you care?
Reasoning benchmarks are not just academic.
They are one of the best proxies we have for:
general problem solving
learning new rules fast
pattern recognition
adapting beyond training data
So if you care about where AI is actually heading, this benchmark matters.
Not sure which AI model to pick?
Read our full guide to the best LLMs
Which LLM is best at reasoning in 2026?
ARC-AGI-2 shows something very clear.
AI is still far from human-level reasoning.
The top score is not impressive by human standards.
But it is the frontier for machines.
The top tier is small
OpenAI’s GPT-5.2 leads with 54%.
That is the highest reported score so far.
But it also shows the ceiling is still low.
Claude and Gemini are far behind
Claude reaches around 38%
Gemini sits near 31%
After that, many systems are close to random guessing.
This is not a tight race. It is a steep drop.
Most models still score near zero
ARC-AGI-2 was designed to be easy for humans and hard for AI.
Pure language models often score 0%.
Even advanced reasoning systems only improve with heavy compute.
That is the point of the benchmark.
What is the ARC-AGI-2 benchmark?
ARC-AGI stands for Abstract Reasoning Corpus for Artificial General Intelligence.
It was first proposed by François Chollet as a test of fluid intelligence.
The goal is simple:
tasks that humans solve easily
tasks that AI cannot brute-force
minimal reliance on training data
ARC-AGI-2 is the second generation of this benchmark, released in 2025.
It is harder than ARC-AGI-1, while staying easy for humans.
Every task was solved by at least two humans in under two attempts.
What makes ARC-AGI-2 different?
Most benchmarks test “PhD-level knowledge.” ARC-AGI tests the opposite.
It focuses on simple puzzles that require:
learning a rule from examples
applying it in a new setting
generalizing quickly
This exposes the gaps that scaling alone does not fix.
ARC-AGI-2 also measures efficiency. The ARC Prize team now reports cost per task, not just accuracy. Because intelligence is not only solving problems. It is solving them efficiently.
Humans solve tasks for roughly $17 each. Some AI systems need hundreds of dollars per puzzle. That gap is the real signal.
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