OpenAI o1 has drawn attention because it appears to improve AI performance by spending more computation during inference. The basic idea is straightforward: allow the model more time and more processing power at response time, and it may produce better answers.
But results discussed by researchers suggest that longer output alone does not explain what makes o1-preview stronger. The evidence points toward a more complex mix of training, search, reinforcement learning, and more efficient use of reasoning paths.
More Tokens Help, But Not Enough
OpenAI claims to have found a way to scale AI capabilities by scaling inference processing power. In practical terms, that means giving the system more computational resources and accepting longer response times in exchange for better results.
That idea matters because much of AI progress has historically been tied to scale. If inference-time computation can also be scaled effectively, then AI systems may improve not only by becoming larger or being trained on more data, but also by working harder when they answer.
The source notes that o1 was trained from scratch using the popular step-by-step inference method. That makes it natural to ask whether its advantage comes mainly from producing a larger internal or external reasoning trail.
Researchers at Epoch AI tested that question by trying to match o1-preview on GPQA, the Graduate-Level Google-Proof Q&A Benchmark. They used GPT-4o with two prompting techniques, Revisions and Majority Voting, and generated a large number of tokens in a way intended to resemble o1's "thought process".
The outcome was clear in direction. More generated tokens produced slight gains, but the GPT-4o variants did not approach o1-preview's performance. Even at high token counts, accuracy stayed significantly below o1-preview.
The Cost Test Still Favored o1-Preview
A simple explanation would be that o1-preview performs better because it spends more computation, and therefore more money, per answer. The Epoch AI analysis considered that issue as well.
According to the source, the gap remained even after accounting for o1-preview's higher cost per token. Epoch AI's extrapolation suggests that spending $1,000 on output tokens with GPT-4o would still leave accuracy more than 10 percentage points below o1-preview.
That is an important result because it weakens the idea that brute-force token generation is the whole story. If GPT-4o could match o1-preview simply by producing enough output, then very large token budgets should close the gap. The reported extrapolation says they do not.
This does not mean inference scaling is irrelevant. The source says additional tokens did lead to slight improvements. The narrower point is that more output, by itself, appears insufficient to recreate o1-preview's advantage on the benchmark used in the comparison.
What Might Explain the Gap
The researchers conclude that simply scaling up inference processing power is not enough to explain o1's superior performance. They suggest that advanced reinforcement learning techniques and improved search methods are likely to play a key role.
That interpretation shifts attention from raw length to process quality. A model can generate many reasoning steps, but if those steps are poorly directed, repeated, or built on weak intermediate choices, the extra computation may add little. Better search could help the system explore more useful paths. Reinforcement learning could help it prefer reasoning patterns that more often lead to correct answers.
The study's authors do not claim to have isolated a single cause. The source states that their findings do not definitively prove algorithmic improvements are the only reason o1-preview outperforms GPT-4o. Higher quality training data could also contribute to the difference.
Another possible explanation in the source is that o1 has been trained directly on correct reasoning paths. If that is the case, it may follow learned logical steps more efficiently and reach correct results more quickly. In other words, the model may not only spend more compute; it may spend that compute in a more useful way.
- Inference scaling may improve results by giving a model more processing time.
- Prompting techniques such as Revisions and Majority Voting can help, but did not match o1-preview in the reported test.
- Algorithmic innovation, including reinforcement learning and search, may explain part of the performance difference.
- Training data quality remains a possible contributor, according to the source.
Progress Does Not Mean Perfect Planning
The article also points to separate work from researchers at Arizona State University. They found that o1 shows significant progress in planning tasks, but remains prone to errors.
Their study reported better performance on logic benchmarks, yet noted that o1 offers no guarantee of correct solutions. That distinction is central for anyone evaluating advanced AI reasoning models. A system can improve substantially and still be unreliable in settings where correctness is required.
The comparison with traditional planning algorithms is also important. According to the source, those algorithms achieved perfect accuracy with shorter computation times and lower costs. That does not erase o1's progress, but it does show that general language-model reasoning is not automatically the best tool for every planning problem.
For users, the practical lesson is measured optimism. o1-preview may represent a meaningful step beyond ordinary long-form prompting, especially if its advantage comes from better learned reasoning paths, reinforcement learning, or search. At the same time, the evidence cited in the source warns against treating longer reasoning as a guarantee of truth.
Why This Matters For AI Scaling
The debate around o1 is really a debate about where future AI gains may come from. If better results could be achieved only by asking existing models to write more, then progress would be easier to imitate with prompting and larger token budgets.
The source suggests something more demanding. o1-preview's edge appears to involve more than elaborate step-by-step prompting. The system may benefit from how it was trained, how it searches through possible answers, and how effectively it uses inference-time computation.
That makes o1 relevant beyond one benchmark. It points to a future where AI scaling is not just about bigger models or longer outputs, but about models that can use additional computation more intelligently. The remaining caveat is just as important: improved reasoning performance is not the same as guaranteed correctness.