Visualizing AI vs. Human Performance In Technical Tasks

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, via Visual Capitalist’s Kayla Zhu, 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.

To dive into all the AI Week content, visit our AI content hub, brought to you by Terzo.

To learn more about the global AI industry, check out this graphic that visualizes which countries are winning the AI patent race.