OpenAI o1 and Reasoning Models: A New Paradigm?

I wrote about GPT-3 in 2020, when 175 billion parameters felt impossibly large. I wrote about ChatGPT in 2022, when a language model became a product overnight. Now, in late 2024, OpenAI has released o1, and it feels like the start of a different chapter entirely.

The core idea: instead of immediately generating a response, o1 reasons through the problem first using a chain of internal "thinking tokens" before producing its answer. That sounds simple. The implications are not.

What Makes o1 Different

It's not just a bigger GPT-4. Previous scaling wins were about more parameters, more data, more compute during training. o1 represents a shift to scaling compute at inference time. The model spends more effort thinking about each individual problem. That's a fundamentally different tradeoff than training a larger model and hoping it generalizes.

Chain-of-thought is baked in, not prompted. We've known since 2022 that prompting models to "think step by step" improves performance on reasoning tasks. o1 makes this the default behavior, trained through reinforcement learning to produce extended internal reasoning chains. The architecture incentivizes the model to work through problems methodically before committing to an answer.

The benchmark results back it up. o1-preview scored in the 89th percentile on Codeforces competitive programming. It placed among the top 500 students in the USA on the AIME math competition. On PhD-level science questions (GPQA Diamond), it outperformed human domain experts. These aren't marginal improvements. They're capability jumps in domains where previous LLMs struggled.

What "Thinking Tokens" Mean Architecturally

The interesting question is what's happening under the hood. OpenAI hasn't published the full details, but the pattern is clear: the model generates an extended chain of reasoning tokens that the user doesn't see, then produces a final answer conditioned on that reasoning.

This means inference cost scales with problem difficulty. A simple factual question might need a few thinking tokens. A complex math proof might need hundreds. That's a more intelligent allocation of compute than the fixed-cost-per-token approach of standard autoregressive models.

It also raises practical questions. How do you price an API where the compute per request varies by an order of magnitude? How do you set latency expectations when a simple question takes seconds but a hard one takes minutes? These aren't theoretical concerns. They're product design problems that will shape how reasoning models get deployed.

The Bigger Question

I've now watched four generations of OpenAI models in real-time: GPT-3, ChatGPT, GPT-4, and o1. Each one made me recalibrate what I thought these systems could do. But o1 is the first one that made me recalibrate what I think these systems are.

Previous models were sophisticated pattern matchers with remarkable generalization. o1 is doing something that at least resembles deliberate reasoning. The question that keeps nagging me: is this a step toward general intelligence, or is it a very convincing imitation of reasoning built on the same statistical foundations?

I genuinely don't know. And I think anyone who claims certainty in either direction is either selling something or not paying close enough attention.

What I'm Watching

The most important thing about o1 isn't o1 itself. It's that OpenAI has demonstrated a new scaling axis. If inference-time compute can substitute for training-time compute, that changes the economics and capabilities of the entire field. Expect every major lab to pursue this direction.

For engineers like me who've spent years focused on making inference cheaper and faster, there's an irony here: the frontier is now deliberately making inference more expensive to get better results. Edge AI and reasoning models are pulling in opposite directions. Where that tension resolves will define the next era of AI systems.