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AI vs. Human Performance in Technical Tasks
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The gap between human and machine reasoning is narrowing—and fast.
Over the past year, AI systems have continued to see rapid advancements, surpassing human performance in technical tasks where they previously fell short, such as advanced math and visual reasoning.
This graphic visualizes AI systems’ performance relative to human baselines for eight AI benchmarks measuring tasks including:
- Image classification
- Visual reasoning
- Medium-level reading comprehension
- English language understanding
- Multitask language understanding
- Competition-level mathematics
- PhD-level science questions
- Multimodal understanding and reasoning
This visualization is part of Visual Capitalist’s AI Week, sponsored by Terzo. Data comes from the Stanford University 2025 AI Index Report.
An AI benchmark is a standardized test used to evaluate the performance and capabilities of AI systems on specific tasks.
AI Models Are Surpassing Humans in Technical Tasks
Below, we show how AI models have performed relative to the human baseline in various technical tasks in recent years.
| Year | Perfomance relative to the human baseline (100%) | Task |
|---|---|---|
| 2012 | 89.15% | Image classification |
| 2013 | 91.42% | Image classification |
| 2014 | 96.94% | Image classification |
| 2015 | 99.47% | Image classification |
| 2016 | 100.74% | Image classification |
| 2016 | 80.09% | Visual reasoning |
| 2017 | 101.37% | Image classification |
| 2017 | 82.35% | Medium-level reading comprehension |
| 2017 | 86.49% | Visual reasoning |
| 2018 | 102.85% | Image classification |
| 2018 | 96.23% | Medium-level reading comprehension |
| 2018 | 86.70% | Visual reasoning |
| 2019 | 103.75% | Image classification |
| 2019 | 36.08% | Multitask language understanding |
| 2019 | 103.27% | Medium-level reading comprehension |
| 2019 | 94.21% | English language understanding |
| 2019 | 90.67% | Visual reasoning |
| 2020 | 104.11% | Image classification |
| 2020 | 60.02% | Multitask language understanding |
| 2020 | 103.92% | Medium-level reading comprehension |
| 2020 | 99.44% | English language understanding |
| 2020 | 91.38% | Visual reasoning |
| 2021 | 104.34% | Image classification |
| 2021 | 7.67% | Competition-level mathematics |
| 2021 | 66.82% | Multitask language understanding |
| 2021 | 104.15% | Medium-level reading comprehension |
| 2021 | 101.56% | English language understanding |
| 2021 | 102.48% | Visual reasoning |
| 2022 | 103.98% | Image classification |
| 2022 | 57.56% | Competition-level mathematics |
| 2022 | 83.74% | Multitask language understanding |
| 2022 | 101.67% | English language understanding |
| 2022 | 104.36% | Visual reasoning |
| 2023 | 47.78% | PhD-level science questions |
| 2023 | 93.67% | Competition-level mathematics |
| 2023 | 96.21% | Multitask language understanding |
| 2023 | 71.91% | Multimodal understanding and reasoning |
| 2024 | 108.00% | PhD-level science questions |
| 2024 | 108.78% | Competition-level mathematics |
| 2024 | 102.78% | Multitask language understanding |
| 2024 | 94.67% | Multimodal understanding and reasoning |
| 2024 | 101.78% | English language understanding |
From ChatGPT to Gemini, many of the world’s leading AI models are surpassing the human baseline in a range of technical tasks.
The only task where AI systems still haven’t caught up to humans is multimodal understanding and reasoning, which involves processing and reasoning across multiple formats and disciplines, such as images, charts, and diagrams.
However, the gap is closing quickly.
In 2024, OpenAI’s o1 model scored 78.2% on MMMU, a benchmark that evaluates models on multi-discipline tasks demanding college-level subject knowledge.
This was just 4.4 percentage points below the human benchmark of 82.6%. The o1 model also has one of the lowest hallucination rates out of all AI models.
This was major jump from the end of 2023, where Google Gemini scored just 59.4%, highlighting the rapid improvement of AI performance in these technical tasks.
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