# The Shift from Single-Turn LLMs to Agentic Inference: Extended Reasoning and Test-Time Compute

> This explainer walks through what may be the defining architectural change of 2026: models that stop answering immediately and instead spend real computation thinking first. Reasoning models like o3 and DeepSeek R1 run extra hidden inference before responding, sometimes on the order of thousands of tokens of internal work, trading latency and cost for accuracy on hard problems. The efficiency curve is moving fast. The piece notes that o3-mini reached parity with the original o1 at roughly 15 times better cost efficiency and 5 times faster inference, which is what makes extended reasoning practical rather than a luxury. The bigger shift, though, is structural. Instead of single-pass text generation, advanced systems now maintain reasoning state across turns, call tools, plan, and revise strategy as they go. That reframes what an AI product even is, from a completion engine into an agent that works through a task with verification steps built in. For anyone building on these models, the takeaway is that test-time compute has become a design lever you can spend, and how you budget it increasingly matters as much as which base model you pick.

_Section: [Interesting AI Articles](https://www.wortins.com/articles) · Source: SkillGen · Published Wednesday, September 9, 2026_

## Wortins' read

This explainer walks through what may be the defining architectural change of 2026: models that stop answering immediately and instead spend real computation thinking first. Reasoning models like o3 and DeepSeek R1 run extra hidden inference before responding, sometimes on the order of thousands of tokens of internal work, trading latency and cost for accuracy on hard problems. The efficiency curve is moving fast. The piece notes that o3-mini reached parity with the original o1 at roughly 15 times better cost efficiency and 5 times faster inference, which is what makes extended reasoning practical rather than a luxury. The bigger shift, though, is structural. Instead of single-pass text generation, advanced systems now maintain reasoning state across turns, call tools, plan, and revise strategy as they go. That reframes what an AI product even is, from a completion engine into an agent that works through a task with verification steps built in. For anyone building on these models, the takeaway is that test-time compute has become a design lever you can spend, and how you budget it increasingly matters as much as which base model you pick.

## Source

[Read the full story at SkillGen](https://skillgen.io/ai-reasoning-models-2026)

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