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Inference-Time Scaling: Why AI Models Now Think for Minutes Before Answering

By Tyler Casey · AI-assisted research & drafting · Human editorial oversight
@getboski

OpenAI's o1 model spends 60 seconds reasoning through complex problems before generating a response. GPT-4 responds in roughly 2 seconds. This isn't a technical curiosity. It signals a fundamental rethinking of how AI systems process information. The industry is pivoting from optimizing for speed to optimizing for accuracy, even when that means making models dramatically slower.

The concept is called inference-time scaling, sometimes called test-time compute. Rather than training larger models with more parameters, researchers have discovered that letting smaller models think longer can match or exceed the performance of their larger counterparts. Snell et al. at UC Berkeley and Google DeepMind demonstrated that optimally scaling test-time compute can be more than 4x more efficient than scaling model parameters alone. This shift has serious implications for everyone building AI systems, from cloud providers managing infrastructure costs to enterprises evaluating the ROI of AI investments.

The Mechanics of Thinking Longer

Traditional language models generate responses token by token, immediately outputting each word as it's predicted. Reasoning models like DeepSeek-R1, o1, and o3-mini operate differently. As Introl's research documents, these models generate "orders of magnitude more tokens" than non-reasoning models. Those additional tokens aren't shown to the user. As we covered in Why Reasoning Tokens Are a Quiet Revolution, they represent internal deliberation: exploring solution paths, checking work, and refining answers before committing to a response. The model essentially talks to itself before talking to you.

The breakthrough came from realizing that computation at inference time can substitute for computation at training time. DeepSeek-R1 achieved o1-level reasoning through pure reinforcement learning without supervised fine-tuning, demonstrating that reasoning behavior emerges naturally when models are incentivized to think through problems. The model learned to generate internal monologues, explore multiple approaches, and self-correct, all without explicit programming of these behaviors. This has concrete implications for AI development: reasoning capabilities don't require specialized training data, just the right reward structure.

A January 2026 paper, Reasoning Models Generate Societies of Thought, found that these internal deliberations aren't just longer versions of standard outputs. The research by Kim et al. shows that reasoning models like DeepSeek-R1 and QwQ-32B generate "societies of thought," running multiple parallel reasoning processes that converge on solutions. Through mechanistic interpretability methods, the authors found that reasoning models exhibit far greater perspective diversity than instruction-tuned models, with distinct personality traits and domain expertise emerging in the reasoning traces. The finding suggests that what appears to be a single model thinking is actually closer to a committee of specialized reasoners coordinating internally, a computational parallel to multi-agent collaboration happening within a single model.

The industry spent a decade optimizing for faster responses. That assumption no longer holds.

The Foundation: Process Supervision

The industry spent a decade optimizing for faster responses. That assumption no longer holds. For problems where accuracy matters, slower is genuinely better.

The theoretical groundwork for inference-time scaling traces back to OpenAI's Let's Verify Step by Step by Lightman et al. (2023). That paper demonstrated that process supervision, providing feedback for each intermediate reasoning step rather than just the final answer, significantly outperforms outcome supervision. Their process-supervised model solved 78% of problems from a representative subset of the MATH test set. The released PRM800K dataset of 800,000 step-level human feedback labels became a foundational resource for training reward models that evaluate reasoning quality, not just answer correctness.

This work established a principle that inference-time scaling builds on: if you can verify each step of reasoning, you can make models better by letting them reason more carefully at test time rather than training them on more data. The Snell et al. paper extended this by showing that searching against process-based verifier reward models is one of the two most effective mechanisms for scaling test-time compute, alongside adaptively updating model distributions given specific prompts.

More recent work has pushed this further. Muennighoff et al.'s s1 introduced "budget forcing," a technique that controls test-time compute by forcefully extending the model's thinking process, appending "Wait" tokens when the model tries to stop reasoning. This simple intervention led their 32-billion parameter model to exceed o1-preview on competition math by up to 27%, using only 1,000 training examples. The result suggests that the returns from thinking longer haven't been fully explored.

The Economic Implications

Inference-time scaling inverts the traditional AI cost equation. Historically, training dominated costs while inference was relatively cheap. A model trained once could serve millions of queries at minimal marginal cost. Reasoning models flip this dynamic. Each query now consumes substantially more compute, and those costs scale linearly with usage volume. For enterprises accustomed to the economics of standard language models, reasoning models represent a fundamental shift in cost structure.

Organizations can't simply swap a reasoning model for a standard model and expect their existing infrastructure to cope. The computational demands differ by an order of magnitude, requiring rethinking of capacity planning, cost allocation, and service level agreements. Cloud providers are already adjusting their pricing models, offering different tiers based on reasoning complexity rather than just token count.

OpenAI charges significantly more for o1 than for GPT-4o, not because the model is inherently more valuable, but because each query consumes dramatically more compute time. Organizations evaluating reasoning models must consider whether the accuracy improvements justify the cost increases, a calculation that varies dramatically by use case. A medical diagnosis application might justify the expense. A chatbot for casual queries probably can't. This mirrors the budget problem facing AI agent deployment more broadly: compute isn't free, and knowing where to spend it matters more than spending more of it.

What appears to be a single model thinking is actually closer to a committee of specialized reasoners coordinating internally.

What the Headlines Miss

The enthusiasm for reasoning models obscures several important limitations. Not all problems benefit from extended deliberation. Simple queries with clear answers gain nothing from longer thinking times. A model that spends 60 seconds reasoning about what to call a function provides no additional value over one that responds instantly but incurs substantially more cost. The overhead of reasoning only pays off for problems that genuinely require multi-step analysis.

The reasoning process isn't always beneficial either. Models can get stuck in unproductive thought loops, exploring dead ends and failing to converge on correct answers. Recent research examining whether o1-like models truly possess test-time scaling capabilities found that longer chain-of-thought outputs don't consistently enhance accuracy. Correct solutions are often shorter than incorrect ones for the same questions, and this phenomenon is closely tied to self-revision behavior. Longer reasoning traces contain more self-revisions, which frequently lead to performance degradation rather than improvement. Sometimes thinking harder makes things worse.

What appears to be a single model thinking is actually closer to a committee of specialized reasoners coordinating internally.

Evaluating reasoning quality remains an open problem as well. We can measure whether a model arrived at the correct answer, but assessing the quality of its reasoning process requires different metrics entirely. A model might reach correct conclusions through flawed logic, or fail despite sound reasoning. Current benchmarks don't capture these distinctions, leaving organizations without reliable ways to evaluate whether reasoning capability is actually improving outcomes for their specific use cases.

Where This Actually Matters

The emergence of inference-time scaling represents a genuine shift in AI development whose implications are still unfolding. The industry spent a decade optimizing for faster responses, treating latency as a cost to be minimized. That assumption no longer holds universally. For problems where accuracy matters more than speed, slower is genuinely better. The challenge for organizations is identifying which problems fall into that category.

Reasoning models excel at complex tasks where the cost of errors exceeds the cost of compute: medical diagnosis, legal analysis, financial modeling, scientific research. They're overkill for simple tasks where speed and cost efficiency matter more than marginal accuracy improvements. The strategic decision isn't whether to adopt reasoning models, but where to deploy them selectively for maximum impact. This is the same matching problem that makes single agents beat swarms on focused tasks while multi-agent systems win on complex, decomposable ones.

The competitive situation is also shifting. OpenAI no longer holds an exclusive advantage in reasoning capabilities. DeepSeek-R1 demonstrated that open-source alternatives can match proprietary systems, and the s1 paper showed that a 32-billion parameter model with the right training data and budget forcing can surpass o1-preview. This competition will likely accelerate innovation while driving down costs. The next year will determine whether reasoning becomes a premium feature or a standard capability across all AI systems. Organizations that understand when to use reasoning models and when to stick with faster alternatives will have a meaningful advantage.

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