AI MODEL
BENCHMARKS.
How do the world's top AI models compare on standardized tests? Select a benchmark to explore the rankings.
Massive Multitask Language Understanding
57 subjects from elementary to professional level. Tests breadth of world knowledge.
GPT-5
OpenAI · 2025-07
Claude Opus 4.6
Anthropic · 2026-04
DeepSeek R1
DeepSeek · 2025-01
Gemini 3.1 Pro
Google · 2025-11
GPT-4.1
OpenAI · 2025-04
Gemini 2.0 Pro
Google · 2025-02
GPT-4o
OpenAI · 2024-05
DeepSeek V3
DeepSeek · 2024-12
Claude 3.5 Sonnet
Anthropic · 2024-06
o3-mini
OpenAI · 2025-01
Claude 3 Opus
Anthropic · 2024-03
Gemini 1.5 Pro
Google · 2024-02
Llama 3.1 405B
Meta · 2024-07
Progress Note
Since GPT-3.5 in late 2022, MMLU scores have improved from ~70 to over 92 — a remarkable leap in just 3 years. Every generation of frontier models has pushed the ceiling higher across all major benchmarks.
Data sourced from official model cards, technical reports, and Papers With Code. Last updated June 2026.
What Are Benchmarks?
AI benchmarks are standardized tests designed to measure specific capabilities of language models. Each benchmark targets a different skill: world knowledge, coding ability, scientific reasoning, or mathematical problem-solving.
MMLU (Massive Multitask Language Understanding) covers 57 subjects and is one of the most comprehensive measures of general knowledge. A score of 90+ is considered expert-level performance.
HumanEval tests programming ability through 164 Python problems. Models must generate code that passes unit tests — a strong signal for practical coding assistants.
GPQA (Graduate-Level Google-Proof Q&A) contains biology, chemistry, and physics questions written by PhD experts and verified to be resistant to web search. It represents near-expert human difficulty.
MATH includes competition-level mathematics problems from AMC, AIME, and olympiad papers. Strong MATH scores correlate with reliable quantitative reasoning across domains.
HellaSwag evaluates commonsense reasoning through sentence completion. Despite seeming simple, it remained challenging for years before modern frontier models cracked it.
Why Benchmarks Matter
Benchmarks provide a common vocabulary for comparing models across organizations. Without them, every provider could define "smart" on their own terms. Standardized scores let engineers, researchers, and buyers make apples-to-apples comparisons.
They also track progress over time. The consistent improvement on MMLU from ~70% in 2022 to over 92% today quantifies how much frontier AI has advanced — a data point impossible to appreciate from marketing copy alone.
Limitations to Keep in Mind
Benchmark scores are necessary but not sufficient for model selection. High MMLU does not guarantee good instruction following. High HumanEval does not guarantee safe code generation. Benchmarks measure what they measure — no more.
Contamination is an ongoing concern. Models trained on internet data may have seen benchmark questions during training, inflating scores above true capability levels. The field is actively developing contamination-resistant evaluations like GPQA.
Finally, cost and latency matter in production. A model scoring 2 points higher on MMLU but costing 10x more is rarely the right choice. Use these rankings alongside iOPTERA's pricing data for a complete picture.