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Sometimes, on specific tests—but there is no universal winner. Recent AI models have matched or exceeded average human scores on some static facial-expression and forced-choice mental-state tests. Other evidence favors human observers when judging spontaneous expressions. And none of these results proves that AI can reliably know what someone privately feels in everyday life.
What “detecting emotion” can mean
Emotion detection is not one standardized task. A system might label a posed facial expression, choose a mental-state word for a photograph of someone’s eyes, interpret spontaneous behavior, or predict a physiological marker associated with affect. Those tasks use different inputs and different definitions of a correct answer, so their scores cannot be treated as a single contest between AI and people.
- Expression classification: assign a label such as fear or surprise to a face.
- Mental-state tests: select an answer from a fixed set of words for an image.
- Spontaneous-expression judgment: interpret behavior produced in a less controlled interaction.
- Physiological prediction: predict a measured bodily marker associated with affect.
A correct label or test answer is evidence of performance on that task. It is not direct access to another person’s private emotional state.
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A 2025 study by Nelson and colleagues in npj Digital Medicine evaluated three AI models on 672 posed images from the NimStim Set of Facial Expressions. The images depicted actors aged 21–30 and carried one of eight expression labels. On that static-image benchmark, the models’ reported accuracy was:
#1 Best Overall
| Model evaluated | Accuracy on the NimStim study |
|---|---|
| GPT-4o | 86% (95% confidence interval: 84–89%) |
| Gemini 2.0 Experimental | 84% (95% confidence interval: 81–87%) |
| Claude 3.5 Sonnet | 74% (95% confidence interval: 71–78%) |
The study found GPT-4o and Gemini 2.0 Experimental had overall reliability comparable to human observers on this benchmark. The finding applies to the tested models and images, not to all AI systems or ordinary conversations. The authors cautioned that one dataset cannot establish broad generalizability; real interactions also include verbal and auditory context.
Overall accuracy can hide particular mistakes
Fear was often mistaken for surprise. GPT-4o classified 52.5% of fear examples as surprise, while Gemini 2.0 Experimental did so for 36.25%. That matters because an overall accuracy score combines results across labels and can conceal poor performance on a specific expression.
The study did not find significant differences in accuracy, recall, or kappa by the actors’ sex or race in this dataset. That result does not establish broad fairness: the authors noted that the limited stimulus set cannot settle how models perform across larger populations, cultures, or contexts.
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How AI compared with people on mental-state tests
A 2026 Scientific Reports study by Akben, Gude, and Ajjan compared GPT-5 mini with large human response datasets on two standardized forced-choice tests. Both tests use photographs focused on the eye region and ask participants to choose a mental-state answer from options. GPT-5 mini scored 83% on each test.
| Test | GPT-5 mini | Human average |
|---|---|---|
| Reading the Mind in the Eyes Test (RMET) | 83% | 71% |
| Multiracial Reading the Mind in the Eyes Test (MRMET) | 83% | 63% |
These comparisons are against human averages, not every person. On the RMET, the AI advantage narrowed and reversed among the highest human performers: at the 97th percentile, people scored about 3 percentage points higher. On the MRMET, the reported advantage persisted across performance quantiles. The authors also discussed possible benchmark contamination for the long-public RMET.
The scores show performance on standardized, static-image assessments. They do not establish that GPT-5 mini is better at interpreting open-ended conversations or live social interaction, where context is less constrained and there may not be one fixed answer.
What the evidence says about spontaneous expressions
A 2025 Cureus Journal of Computer Science study compared AI facial coding, peer coding, and self-reported expressions during a virtual reflective-learning conversation. It reported that human observers’ coding better approximated participants’ self-reports than the AI’s coding did.
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A 2025 study of six language models across five structured emotional-intelligence tests reported average model accuracy of 81%, compared with a 56% human average reported in the tests’ original validation studies. That is a result about those tests and their comparison data, not proof of superior everyday emotional intelligence.
In a separate 2025 multi-team study, machine-learning models predicted physiological markers of affect above chance on the study’s tests. The teams also highlighted limits in how results could be compared and generalized. Predicting a bodily marker is a distinct task from reading a facial expression or identifying someone’s feelings.
How to assess a new “AI beats humans” claim
Before treating a headline as evidence that AI understands people better, check what was actually measured:
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall- Task and input: Was the system given posed or spontaneous faces, voice, text, movement, physiological signals, or a combination?
- Definition of correct: Was the answer a posed-expression label, a participant’s self-report, an expert judgment, or a forced-choice test response?
- Human comparison: Was AI compared with an average participant, an expert, a crowd average, or top-performing people?
- Error pattern: Which emotions were confused, and how often? A strong average can coexist with weak results for particular labels or people.
- Study setting: Were stimuli static or dynamic, posed or spontaneous, isolated or contextualized? Was the benchmark familiar or were stimuli held out?
- Representation and validation: How large and diverse was the sample, and was performance checked independently beyond the study dataset?
- Exact model and date: Results apply to the model version tested at the time, not automatically to later versions or AI in general.
What these results do—and do not—tell us
AI can score highly on carefully defined tasks that involve labeling expressions or selecting answers from fixed choices, and some studies report scores above human averages. But a visible expression is only evidence about an expression: people can mask feelings, and context changes how behavior should be interpreted. The available comparisons do not establish a general ability to read private feelings or a universal advantage over humans.
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