AI Benchmark Dashboard
Compare 18+ AI models across 8 standardized benchmarks. Scores sourced from Papers with Code, Hugging Face Open LLM Leaderboard, and official model cards. Updated regularly with the latest results.
ARC-AGI
Abstraction and Reasoning Corpus — tests novel pattern recognition and abstract reasoning. Considered the hardest AI benchmark.
GSM8K
Grade School Math — 8,500 grade school math word problems. Tests basic mathematical reasoning and multi-step problem solving.
LMSYS Chatbot Arena Elo
Crowdsourced blind human preference rankings. Users compare anonymous model outputs — the most democratic LLM benchmark.
SWE-bench Verified
Tests ability to solve real GitHub issues from popular Python repos. The gold standard for agentic coding evaluation.
GPQA Diamond
Graduate-level science questions written by PhD experts. Extremely hard — even domain experts only score ~65%.
MATH
Competition-level math problems from AMC, AIME, and Olympiad. Tests mathematical reasoning and problem-solving.
HumanEval
Evaluates code generation by asking models to complete Python functions from docstrings. Tests practical programming ability.
MMLU
Massive Multitask Language Understanding — measures knowledge across 57 academic subjects including STEM, humanities, and social sciences.
Frequently Asked Questions
What is the best AI model in 2026?
It depends on the task. Claude Opus 4.6 leads on MMLU (92.3%) and HumanEval (93.7%) for general knowledge and coding. OpenAI o3 dominates math with 96.7% on MATH and 87.7% on GPQA Diamond. GPT-4o leads the Chatbot Arena Elo ratings.
What is the MMLU benchmark?
MMLU (Massive Multitask Language Understanding) tests AI models across 57 subjects including STEM, humanities, and social sciences. It measures broad knowledge and reasoning ability. Scores above 90% indicate expert-level performance.
What is HumanEval?
HumanEval measures AI code generation using 164 hand-written Python programming problems. Models must generate correct, functional code that passes test cases. Scores above 90% indicate near-human coding ability.
How are AI benchmarks scored?
Most benchmarks use accuracy percentage (0-100%). Some use Elo ratings (Chatbot Arena) or pass rates (SWE-bench). Scores come from official evaluations by model providers and independent organizations.
Data sourced from Papers with Code, Hugging Face, and official model evaluations. Updated regularly. Scores reflect official reported results only.