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Technical Performance · Index

AI MODEL BENCHMARKS 2026

AI capability is improving faster than benchmarks can keep up — SWE-bench, MMLU, GPQA and the frontier-model race, in primary-sourced numbers. Every figure is linked to its source and dated.

By The AI Index · Updated Jun 20, 2026 · 7 min read · 9 sourced figures

Capability is outpacing the benchmarks

AI model capability is improving faster than benchmarks can keep up. On SWE-bench, models went from solving 4.4% of coding tasks in 2023 to 71.7% in 2024 (Stanford HAI). MMLU is now effectively saturated above 92%, and the gap between the top U.S. and Chinese models has stayed in the single digits all year (Stanford HAI 2026). In a single year (2023–2024), scores rose 18.8, 48.9, and 67.3 percentage points on MMMU, GPQA, and SWE-bench respectively.

MMLU launched in 2020 near 32% frontier accuracy; by Q1 2026 every frontier system scores above 92%, near the benchmark’s ~95% ceiling. As classic benchmarks saturate, the open question is no longer whether models can pass the test, but which test is hard enough to still discriminate between them.

Deep dive: why benchmarks keep changing

As classic benchmarks saturate, frontier labs have moved to harder tests. The benchmarks model cards actually report in 2026 include Humanity’s Last Exam (HLE), FrontierMath, ARC-AGI-2, GPQA Diamond, SWE-bench Verified, AIME 2025, and τ-bench. Even these are falling quickly — frontier models gained roughly 30 percentage points on HLE in a single year, a test explicitly designed to be hard for AI (Stanford HAI 2026).

Benchmark	One-year gain (pp)
SWE-bench	+67.3
GPQA	+48.9
MMMU	+18.8

“As the old benchmarks saturate, the frontier has become a race to build a test that AI can’t yet pass.”

The competitive landscape

The frontier is close and contested. In February 2025, China’s DeepSeek-R1 briefly matched the top U.S. model; since then the lead has changed hands repeatedly while staying in the single digits. As of March 2026 the top U.S. model leads by 2.7%. The open-vs-closed gap has also compressed — from 8.0% in January 2024 to 1.7% by February 2025 on the Chatbot Arena Leaderboard (Stanford HAI 2026).

The numbers in full

Metric	Figure	Source
SWE-bench (coding) 2023 → 2024	4.4% → 71.7%	Stanford HAI
GPQA one-year gain	+48.9 pp	Stanford HAI
MMMU one-year gain	+18.8 pp	Stanford HAI
Top US lead over China (Mar 2026)	2.7%	Stanford HAI 2026
Closed–open gap (Jan 2024)	8.0%	Stanford HAI 2026
Closed–open gap (Feb 2025)	1.7%	Stanford HAI 2026
HLE one-year gain	+30 pp	Stanford HAI 2026
Inference cost decline (late ’22–late ’24)	~280×	Stanford HAI 2025

Figures reflect each Stanford HAI AI Index release; one-year benchmark gains are for 2023–2024. See sources below.

Frequently asked

What is the best AI benchmark in 2026?

There is no single benchmark. As MMLU and similar tests saturate above 92%, frontier labs now report on harder ones — Humanity’s Last Exam (HLE), FrontierMath, ARC-AGI-2, GPQA Diamond, and SWE-bench Verified, among others. (Stanford HAI)

How fast is AI improving on benchmarks?

Very fast. In 2023–2024 alone, SWE-bench jumped from 4.4% to 71.7% (+67.3 pp), and frontier models have since gained about 30 points in a year on Humanity’s Last Exam. (Stanford HAI)

Are US or Chinese AI models better?

They are very close. The lead has changed hands repeatedly since early 2025; as of March 2026 the top U.S. model leads China’s best by about 2.7%, per Stanford HAI. (Stanford HAI)

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