Model stringlengths 5 23 | Provider stringclasses 8 values | Score (%) float64 42.5 75 | Score (Raw) float64 1.7 3 | Max Score float64 4 4 | Stability stringclasses 2 values | Rec. Temp float64 0.3 0.3 | Pricing Tier stringclasses 4 values | Cost ($) float64 0 0.06 | Time (s) float64 6.73 87.4 | Acc/$ float64 48.9 2.51k | Acc/min float64 1.37 23.7 | Completion (%) float64 25 100 | Input Tokens (avg/run) int64 1.82k 7.47k | Output Tokens (avg/run) int64 4 4.36k | Status stringclasses 1 value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
claude-sonnet-4.6 | anthropic | 50 | 2 | 4 | ±0.000 | 0.3 | High | 0.014787 | 8.19 | 135.25 | 14.65 | 100 | 4,849 | 16 | completed |
claude-opus-4.6 | anthropic | 50 | 2 | 4 | ±0.000 | 0.3 | Very High | 0.024645 | 12.41 | 81.15 | 9.67 | 100 | 4,849 | 16 | completed |
gpt-5.2 | openai | 75 | 3 | 4 | ±0.000 | 0.3 | High | 0.008537 | 7.59 | 351.43 | 23.72 | 100 | 4,750 | 16 | completed |
Qwen3.5-397B-A17B | qwen | 50 | 2 | 4 | ±0.000 | 0.3 | Medium | 0.007312 | 87.39 | 273.54 | 1.37 | 25 | 3,372 | 1,469 | completed |
grok-4-1-fast-reasoning | xai | 57.5 | 2.3 | 4 | ±1.000 | 0.3 | Low | 0.000917 | 15.89 | 2,507.63 | 8.68 | 100 | 1,816 | 1,108 | completed |
mistral-medium-latest | mistral | 42.5 | 1.7 | 4 | ±1.000 | 0.3 | Medium | 0.002189 | 6.73 | 776.68 | 15.17 | 100 | 5,417 | 11 | completed |
sonar | perplexity | 57.5 | 2.3 | 4 | ±1.000 | 0.3 | Medium | 0.02559 | 11.83 | 89.88 | 11.66 | 100 | 5,595 | 4 | completed |
llama4-maverick | meta | 50 | 2 | 4 | ±0.000 | 0.3 | Low | 0.002023 | 7.59 | 988.82 | 15.8 | 100 | 7,466 | 8 | completed |
gemini-3-pro | gemini | 75 | 3 | 4 | ±0.000 | 0.3 | High | 0.06139 | 65.1 | 48.87 | 2.77 | 100 | 4,535 | 4,360 | completed |
gemini-3-flash | gemini | 67.5 | 2.7 | 4 | ±1.000 | 0.3 | Medium | 0.005999 | 14.67 | 450.04 | 11.04 | 100 | 4,535 | 1,244 | completed |
gemini-3.1-pro | gemini | 75 | 3 | 4 | ±0.000 | 0.3 | High | 0.028054 | 27.59 | 106.94 | 6.52 | 100 | 4,535 | 1,582 | completed |
AI Model Emotion Detection Benchmark
Benchmark results from testing 11 AI models on emotion detection from movie stills, conducted on OpenMark — 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. Full analysis: I benchmarked 10 ai models on reading human emotions
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