OpenAI's o3: The Grand Finale of AI in 2024

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A December 2024 Interconnects newsletter post analyzing OpenAI's o3 model capabilities, relevant for understanding the rapid advancement of frontier AI reasoning models and implications for AI safety evaluation benchmarks like ARC-AGI.

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Summary

Nathan Lambert analyzes OpenAI's o3 model release, arguing it represents a step-change in AI capabilities comparable to GPT-4, particularly in reasoning benchmarks. o3 achieves over 85% on ARC-AGI and jumps from 2% to 25% on FrontierMath, signaling rapid progress in reinforcement learning-trained reasoning models.

Key Points

•o3 is the first model to surpass the 85% threshold on the ARC-AGI prize benchmark, though at high compute cost and on the public set.
•Performance on FrontierMath jumped from ~2% to 25%, representing a major step change in mathematical reasoning capabilities.
•The author argues reasoning models (o1/o3 style) will soon transform AI research broadly, not just math/coding domains.
•Progress reflects a shift away from pure pretraining scaling toward reinforcement learning-based reasoning training methods.
•o3-mini expected for public release in late January 2025; seen as setting up a dynamic 2025 for AI development.

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o3: The grand finale of AI in 2024 - by Nathan Lambert 
 
 
 
 
 

 

 

 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 

 

 
 
 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 
 
 

 
 
 
 
 

 

 
 
 
 
 

 

 

 

 

 
 

 
 

 

 

 

 
 

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 A step change as influential as the release of GPT-4. Reasoning language models are the current and next big thing.

 Nathan Lambert Dec 20, 2024 106 4 10 Share Article voiceover 0:00 -17:58 Audio playback is not supported on your browser. Please upgrade. Edit 1 12/20 : I added more context around the quotes for Frontier Math, commented on ARC Prize’s reported token counts for eval., fixed minor typos, and fixed incorrect notation on pass@ referring to majority voting relative to the email version. 

 Today, OpenAI previewed their o3 1 model continuing their recent progress on training language models to reason with o1. These models, starting with o3-mini, are expected to be available to the general public in late January of 2025. As we were wrapping up 2024, many astute observers saw this year as one of consolidation in AI where many players achieve GPT-4 equivalent models and figure out what to use them for. 

 There was no moment with a “ GPT-4 release ” level of excitement in 2024. o3 changes that by being far more unexpected than o1, and signals rapid progress across reasoning models. We knew o1 was coming with the long lead-up — the quick and effective follow-up with o3 sets us up for a very dynamic 2025. 

 While many doubt the applicability of o1-like models outside of domains like mathematics, coding, physics, and hard sciences, these models will soon be used extensively across the entire AI research ecosystem, dramatically increasing progress. An optimistic lens is that there has not been enough time to figure out what to use them for nor public access to RL training methods needed to bring reasoning models into other domains. 

 OpenAI’s o3 shows the industry is beginning to climb its next hill as progress from pretraining only on internet text yields fewer profitable benefits. o3 is a major step change in reasoning evaluations — in summary, it is:

 The first model to surpass the 85% threshold for completing the ARC AGI prize (Note: this was done on the public set, not the test set, and exceeded cost constraints). 

 A step change in state-of-the-art performance on the extremely new Frontier Math benchmark from 2 to 25%. 

 Substantial improvements were made to all of the leading coding benchmarks, such as SWE-Bench-Verified.

 And all of this is only 3 months after the first version of the model was announced. These changes will soon begin by accelerating the rate of progress in AI research. Over time, as the costs of inference decline, it will be another s

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