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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/232006
Benchmarking Vision-Language Transformer Models Across Diverse Datasets
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This project develops a common benchmark harness for comparing vision-language models across visual question answering, captioning, detection, classification, preference, rating, and prompt-reconstruction tasks. The final evaluation focuses on hosted API models using 20 examples per benchmark and identical execution settings. GPT-4o and Claude Sonnet 4.5 are compared on 44 jointly completed benchmarks; GPT-4o wins 30, Claude wins 10, and 4 are tied. A second comparison covers the 22 Type A/B/C benchmarks completed by GPT-4o, GPT-4.1, GPT-5, Gemini 3.5 Flash, and Claude Sonnet 4.5. Gemini leads this restricted Type A/B/C set, while GPT-4o is the stronger model in the direct GPT-4o–Claude comparison. The results show that model rankings depend on task family and that matched benchmark coverage is necessary for fair aggregation. Because each benchmark uses a small sample and hosted models may change, the conclusions should be interpreted as a reproducible experimental snapshot rather than a permanent leaderboard.
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Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2026, Director: Sergio Escalera Guerrero
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BLANCO PONCE, Samuel. Benchmarking Vision-Language Transformer Models Across Diverse Datasets. [consulted: 14 of October of 2026]. Available at: https://hdl.handle.net/2445/232006