Create README.md

README.md

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+---
+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)