Understanding how AI models are evaluated, compared, and ranked. From standardized tests to human preference ratings.
These benchmarks test specific capabilities like reasoning, coding, math, and knowledge. Top models in 2025 are approaching or exceeding human-level performance on many of these tests.
Massive Multitask Language Understanding
Tests knowledge across 57 subjects including mathematics, history, law, medicine, and science. Evaluates both factual knowledge and contextual application.
Harder Endings, Longer contexts, and Low-shot Activities
Evaluates common-sense reasoning through sentence completion. Uses adversarial filtering to create deceptive wrong answers that test true understanding.
OpenAI Human Evaluation
164 programming problems where models must generate Python function bodies. Success is measured by passing all unit tests for each problem.
AI2 Reasoning Challenge
Complex, multi-part science questions at grade-school level. Tests both comprehensive knowledge and advanced reasoning abilities beyond simple fact retrieval.
Grade School Math 8K
8,500 grade school math word problems requiring multi-step reasoning. Tests mathematical problem-solving and chain-of-thought reasoning.
Mathematics Aptitude Test of Heuristics
12,500 challenging competition mathematics problems. Covers algebra, geometry, number theory, counting, and probability at high school competition level.
Truthful Question Answering
Tests whether models generate truthful answers and avoid common misconceptions. Measures resistance to generating false but plausible-sounding information.
Winograd Schema Challenge at Scale
44,000 pronoun resolution problems requiring common-sense reasoning. Tests understanding of context and world knowledge.
Graduate-Level Google-Proof Q&A
Expert-level questions in biology, physics, and chemistry. Designed to be difficult even for domain experts and resistant to simple search.
Multi-Turn Benchmark
Evaluates multi-turn conversational abilities. Tests coherence, instruction following, and maintaining context across dialogue turns.
Mostly Basic Python Programming
974 programming tasks designed to be solvable by entry-level programmers. Complements HumanEval for broader code generation assessment.
BIG-Bench Hard
23 challenging tasks from BIG-Bench where language models previously performed below average human-rater performance.
Beyond standardized tests, human preference ratings provide real-world performance insights. The ELO system, borrowed from chess, ranks models based on head-to-head comparisons.
A crowdsourced, randomized battle platform where users chat with two anonymous models side-by-side and vote for the better one. Over 5M+ user votes are used to compute ELO ratings.
Uses Bradley-Terry model and ELO rating system. Models gain points for wins and lose points for losses. The difference between two models' ELO scores indicates relative performance.
Only the most advanced models like GPT-4, Claude, and Gemini
Approaching leading commercial model quality
Good performance, many open-source models
Conversational ability but frequent limitations
Track the latest model rankings across different evaluation criteria.
As top-tier models approach the upper bounds of static benchmarks like MMLU or HumanEval, these tests become less useful for distinguishing new progress. Hugging Face acknowledged this when launching Open LLM Leaderboard v2, noting that "models began to reach baseline human performance on benchmarks like HellaSwag, MMLU, and ARC."
This has led to growing interest in composite and adversarial benchmarks (like MixEval or MMLU-Pro) that introduce novelty and harder reasoning tasks, as well as concerns about data contamination where models may have seen test problems during training.
Our models are continuously evaluated against these benchmarks. Try FullAI and see the difference.
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