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In this episode of the Colaberry AI Podcast, we dig into the performance of GPT-4o and other multimodal foundation models on traditional computer vision tasks and how they stack up against specialized vision systems. 

Key highlights from the discussion:
 🔍 How researchers used prompt chaining to test models on CV tasks
📊 GPT-4o leads among non-reasoning models, but still trails behind specialized systems
📐 Major gaps in geometric understanding and spatial accuracy
🧠 Reasoning-based models showed promise in 3D vision tasks
📈 Why prompt chaining consistently outperforms direct prompting

Is GPT-4o ready for vision-critical tasks? Let’s explore what the evidence says.

🧾 Ref:
How Well Does GPT-4o Understand Vision – Vlad Bogo

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