The Rise of Reasoning Models: Unlocking the Next Phase of AI

In the fast-moving world of artificial intelligence, it’s easy to feel like the goalposts are constantly shifting. Just as we grow familiar with one generation of large language models (LLMs), a new wave of innovation arrives. The latest? Reasoning models — tools designed not just to generate language, but to think through problems with greater depth and precision.

While “reasoning” might sound like a vague or buzzword-laden concept, it’s at the heart of what makes these models different from their predecessors. Unlike traditional LLMs, which predict text based on patterns in data, reasoning models are designed to walk through a sequence of logical steps — much like how humans solve complex problems. And that shift has opened new doors for AI applications.

This article is heavily based on insights from a recent episode of the ODSC Ai X Podcast with Ivan Lee of Datasaur. Listen to the full episode on Spotify, Apple, and SoundCloud.

From Prediction to Process: What Are Reasoning Models?

Reasoning models build on the architecture of LLMs but introduce a new behavior: structured thinking. One common technique that underpins these models is called chain-of-thought prompting. Instead of asking a model to provide an answer outright, you ask it to “show its work.” That might sound simple, but it often results in more accurate outputs, especially on complex tasks.

Why does this work? Because when a model spells out its steps, it can catch mistakes mid-process and self-correct. The shift transforms LLMs from one-shot predictors to iterative problem-solvers.

While the idea of reasoning in AI has been around — evident in techniques like reinforcement learning and planning frameworks — this new generation makes it more accessible, integrated, and powerful at scale.

Why Now?

The emergence of reasoning models coincides with a key moment in generative AI: the plateauing of performance from traditional model scaling. For years, gains in model quality came primarily from throwing more data and compute at the problem. But with the release of models like OpenAI’s GPT-4.5 and Anthropic’s Claude 3.7, the returns from sheer size alone appear to be diminishing.

As Ivan Lee put it, “This marks the end of a generation of what we can see as the core foundation models themselves.” The implication? If simply scaling up isn’t enough, we need new paradigms — and reasoning offers one.

Benefits (and Tradeoffs)

The appeal of reasoning models lies in their ability to tackle multi-step, high-stakes, or nuanced problems — whether that’s analyzing legal documents, planning a strategy, or diagnosing complex technical issues. They offer clearer interpretability, better factual grounding, and the potential to reduce hallucinations.

But that power comes at a cost — literally. These models tend to be significantly more expensive and slower to run. A single “reasoned” response may require dozens of internal model calls, each incurring compute time and cost. For now, that makes them better suited to scenarios where accuracy and depth matter more than speed.

As Ivan noted, “If a regular inference costs 10 cents, a reasoning model might cost $2. And what used to take 15 seconds could now take three to five minutes — or even run overnight.”

Enter the Hybrid Model

One of the most exciting developments is the emergence of hybrid models that combine fast, low-cost “worker” models with slower, more thoughtful “planner” models. The concept is directly inspired by Thinking, Fast and Slow, the behavioral psychology classic by Daniel Kahneman. Just as humans alternate between instinctive and deliberate thinking, hybrid systems use lightweight models for routine tasks and heavier reasoning models for deeper problems.

It’s a practical response to the cost challenge. By designing GenAI workflows where planner models set high-level strategy and worker models execute it, teams can balance performance, latency, and budget. These approaches also lend themselves well to agentic frameworks, where AI systems autonomously break down and pursue complex goals.

Applications and Use Cases

So where are reasoning models being used today — and where are they heading?

Some clear early use cases include:

• Market research and competitive intelligence: Deep dives into domains where nuance and synthesis matter.
• Strategic planning: Assessing multiple variables and risks in uncertain scenarios.
• Code generation: Especially when paired with chain-of-thought techniques, reasoning models have shown promise in producing more accurate, functional code.
• Scientific discovery: Think drug development, climate modeling, and bioinformatics — domains where long-range inference is key.

Ivan also mentioned that reasoning models could shine in use cases where users expect the model to “think hard,” such as exploring financial markets, planning R&D investments, or writing thorough analytical reports.

That said, many applications still don’t require reasoning models. For everyday tasks — summarization, rewriting, quick answers — traditional LLMs (or even fine-tuned smaller models) remain perfectly suited.

Pitfalls to Watch

While the progress is impressive, reasoning models are not magic. Some key limitations include:

• Causal reasoning: These models often still struggle with cause-and-effect relationships, a critical aspect of true reasoning.
• Increased hallucination risk: Longer chains of inference can sometimes compound errors.
• Opacity: Even when showing their “thinking,” models might provide plausible but incorrect reasoning paths.
• Tooling complexity: New paradigms bring new workflows. Developers must manage memory, token budgets, and cost controls more carefully than ever.

These challenges are being tackled by an emerging ecosystem of tools — from observability platforms to specialized evaluation benchmarks — but we’re still early in the lifecycle.

What Comes Next?

Looking ahead, the landscape is likely to fragment, not consolidate. Rather than a one-size-fits-all universal reasoning model, expect a wave of domain-specific, task-optimized reasoning agents. Whether tuned for legal workflows, scientific research, or financial forecasting, these niche models will outperform general-purpose systems in their respective arenas.

This trend mirrors broader shifts in AI: from monolithic to modular, from general to specific, and from centralized to customizable. It also explains why companies like Datasaur are focused on simplifying access to these tools — abstracting away infrastructure so teams can focus on outcomes.

As Ivan put it, “If we paused all research today, we’d still have years of value to unlock from the models already available. But the reality is, we’re not pausing — we’re accelerating into an entirely new era.”

Final Thoughts

Reasoning models represent a leap forward — not just in what models can do, but how we think about interacting with them. As costs drop and tooling improves, their reach will expand. But they also demand a more careful approach: understanding when to use them, how to guide them, and how to evaluate their outputs.

The age of AI that can think through problems isn’t just coming — it’s already here. The question is how we’ll use it.