---
license: cc-by-4.0
tags:
  - benchmarking
  - llm
  - model-evaluation
  - vision
  - ai
pretty_name: https://OpenMark.ai AI Model Emotion Detection Benchmark
---

# AI Model Emotion Detection Benchmark

Benchmark results from testing 11 AI models on emotion detection from movie stills, conducted on [OpenMark](https://openmark.ai) — a deterministic AI model benchmarking platform.

## Methodology

- **Task:** Identify emotions from 4 movie stills (varying complexity)
- **Models tested:** 11 (GPT-5.2, Gemini 3 Pro, Gemini 3.1 Pro, Claude Opus 4.6, Claude Sonnet 4.6, Grok 4.1 Fast, Llama 4 Maverick, Qwen 3.5, Sonar, Gemini 3 Flash, Mistral Medium)
- **Runs per model:** 3 (for stability measurement)
- **Scoring:** Deterministic, task-specific evaluation
- **Costs:** Real API costs tracked per task

## Key Findings

- GPT-5.2 and Gemini 3 Pro tied at 75% accuracy
- Claude Opus 4.6 ($0.025/task) scored identically to Llama 4 Maverick ($0.002/task) — 12x price difference
- Half the models showed ±1.000 stability variance (changed answers across runs)

## Source

Data generated using [OpenMark](https://openmark.ai). Full analysis: [I benchmarked 10 ai models on reading human emotions](https://dev.to/openmarkai/i-benchmarked-10-ai-models-on-reading-human-emotions-3m0b)