Decoding OpenAI's 04 Mini vs 03 Models: Hype, Flaws & Progress
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In this thrilling episode of AI Explained, the team delves into the latest buzz surrounding OpenAI's 04 Mini and 03 models. Buckle up, folks, as they question the validity of the hype, hinting at potential favoritism in early access distribution. While acknowledging the models' advancements over their predecessors, they slam on the brakes, doubting claims of surpassing genius levels. Revving up their argument with hard evidence from multiple tests, they take these AI wonders for a spin, revealing flaws and errors that put a dent in the AGI dream.
Comparing these cutting-edge models to the likes of Gemini 2.5 Pro and Anthropics Claude 3.7, the team shifts into high gear, casting doubt on the AGI status bestowed upon 03. Despite impressive showings in benchmark tests like competitive mathematics and coding, they slam the brakes on the notion of hallucination-free AI, pointing out major errors that rear their ugly heads. They navigate the winding roads of pricing discrepancies between 03 and Gemini 2.5 Pro, highlighting performance disparities and cost-effectiveness concerns that steer the conversation in a different direction.
Zooming into the models' capabilities, from handling YouTube videos to dissecting metadata with surgical precision, the team peels back the layers of AI sophistication. With a pit stop to discuss training data limits, release notes, and external evaluations, they shift gears, urging viewers to test these AI beasts across various domains to truly gauge their horsepower. With a final lap around the track, they hint at the future of AI performance and scalability, signaling a thrilling race ahead where progress trumps sensational headlines.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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Frequency of video uploads
Comparison between London and San Francisco cultures
Spatial reasoning in models
Performance on benchmarks as a measure of AGI
Early access and hyping up models
Tool use for o3 and o4 mini
Cost comparison between o3 and o1-pro
Concerns about LLMs being a dead end
Gemini 2.5 Pro and potential for o4.1
Benchmark-maximizer as a term and its implications for AI development
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