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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 July 2026 · 7 min read · 9 sourced figures
Key takeaways
- On SWE-bench, models went from solving 4.4% of coding tasks in 2023 to 71.7% in 2024 — a +67.3 pp jump in one year. (Stanford HAI)
- MMLU is now saturated above 92%, near its ~95% ceiling — pushing labs to harder tests like HLE and FrontierMath. (Stanford HAI)
- The US-China frontier gap is razor-thin: the top U.S. model leads by just 2.7% as of March 2026. (Stanford HAI)
- The closed-weight lead on Chatbot Arena narrowed from 8.0% (Jan 2024) to 1.7% (Feb 2025).
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 |
One-year benchmark gains, 2023→2024 · Source: Stanford HAI · percentage points
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.
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)
Primary sources
• Stanford HAI — AI Index 2026, Technical Performance (MMLU saturation · US-China gap · HLE) — hai.stanford.edu