Reasoning models, explained
Why 'thinking' AI is the biggest shift since the transformer
Reasoning models are the most significant advance since the transformer. Unlike earlier models that answered in one shot, these systems 'think' — generating intermediate steps before producing a final answer, which dramatically improves performance on math, coding and logic.
The trade-off is compute: reasoning consumes more tokens and time. But the gains are so large that labs now train models specifically to reason, and route harder problems to them dynamically.
In practice, reasoning models let AI tackle problems that were previously out of reach, from formal verification to multi-step planning.
Key Takeaways
- Reasoning models generate intermediate steps before answering
- They trade extra compute for major gains on hard problems
- They unlock tasks like verification and multi-step planning
Why It Matters
Step-by-step reasoning is what turns AI from a fast autocomplete into a tool that can genuinely solve hard, novel problems.
What Happens Next
Watch for reasoning to become a default capability across all frontier models, and for cost to fall as inference optimizes.
