AI Benchmark Dashboard
Compare 41+ 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.
LMSYS Chatbot Arena Elo
Crowdsourced blind human preference rankings. Users compare anonymous model outputs — the most democratic LLM benchmark.
MMLU
Massive Multitask Language Understanding — measures knowledge across 57 academic subjects including STEM, humanities, and social sciences.
HumanEval
Evaluates code generation by asking models to complete Python functions from docstrings. Tests practical programming ability.
ARC-AGI
Abstraction and Reasoning Corpus — tests novel pattern recognition and abstract reasoning. Considered the hardest AI benchmark.
GPQA Diamond
Graduate-level science questions written by PhD experts. Extremely hard — even domain experts only score ~65%.
SWE-bench Verified
Tests ability to solve real GitHub issues from popular Python repos. The gold standard for agentic coding evaluation.
MATH
Competition-level math problems from AMC, AIME, and Olympiad. Tests mathematical reasoning and problem-solving.
GSM8K
Grade School Math — 8,500 grade school math word problems. Tests basic mathematical reasoning and multi-step problem solving.
Frequently Asked Questions
What is the best AI model in 2026?
It depends on the task. Qwen3.7 Max leads MMLU (93.7%); Claude Sonnet 4.5 tops HumanEval (97.6%); GPT-5.6 Sol leads SWE-bench (96.2%); Claude Fable 5 leads GPQA Diamond (94.6%) — based on the latest official scores in our dashboard.
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.