Reasoning Models: How AI Learned to Think Before It Speaks

Have you ever noticed that sometimes a quick answer is the wrong answer? People know this from experience: complex problems require time to think. Until recently, AI didn't work like that - it responded immediately, without "thinking". In 2026, this limitation disappeared. At Altanet Craiova We believe that reasoning models represent one of the most important qualitative leaps in the history of artificial intelligence.

What does "reasoning" mean in an AI model?

Think about the difference between two types of students:

• The student who answers immediately: He reads the problem, writes the first answer that comes to mind. Sometimes he is right, sometimes he is wrong without realizing it.
• The student who thinks step by step: Read the problem, break it down into smaller parts, solve each part in turn, and check the result at the end.

Reasoning models work like a second student. Before giving you an answer, they go through a series of intermediate steps – sometimes hundreds of steps – in which they check their own logic and correct any errors. Only then do they present you with their conclusion.

What are the models that "think" in 2026?

Three big models have made reasoning a central focus this year:

• GPT-5 with extended reasoning (OpenAI): Solve complex problems with 50-80% fewer operations than the previous version. It has demonstrated a 79-fold efficiency improvement in laboratory protocols for molecular cloning – compressing weeks of work into hours.
• DeepSeek-R1 (China): the first open source (i.e. free and downloadable) model that demonstrated a level of reasoning comparable to leading commercial models. Available on HuggingFace and on deepseek.com.
• Claude Opus 4.6 (anthropos): introduces 4 levels of effort that the user can choose: low, medium, high and maximum. For a simple question you use fewer resources and get the answer faster. For a complex problem you activate the maximum level and the model thinks more deeply.

How much does it matter in practice?

The graph below shows the improvements brought by reasoning in different categories of tasks, compared to classic models that respond directly:

The conclusion is clear: reasoning helps the most with complex tasks – math, code, multi-step logic. For a regular conversation, the difference is smaller.

A concrete example: 79 times faster in the lab

GPT-5 with extended reasoning has been tested in research protocols for molecular cloning—a laboratory process involving dozens of interdependent steps, where a mistake in one step affects all the others. The result: weeks of lab work compressed into a few hours.

This is not an isolated case. The reasoning has opened the door to applications where AI could not be used until now: complex medical diagnostics, legal analysis of long contracts, planning of construction projects.

But there is also a limit.

Reasoning comes at a cost: time and computational resources. A model that „thinks” more responds more slowly and consumes more energy. That’s why Claude Opus 4.6 introduced the 4 levels of effort – it doesn’t make sense to use maximum reasoning to ask the AI what the weather is like outside.

Gabe Goodhart from IBM summarizes the situation well: „"We will reach a point where models become a commodity. The competition will be on systems, not individual models."” In other words, it's not just how well a model thinks, but how well it integrates into real workflows.

What's next?

In the second half of 2026, reasoning becomes standard – almost all major models will include it. The next step, estimated for 2027, is multi-modal reasoning: the model will think by combining text, images and structured data at the same time.

If you're using AI in your company for complex tasks – data analysis, report generation, decision-making assistance – reasoning models can make a real difference. The Team Altanet Craiova can help you identify where and how you can apply them. Visit our website contact and let's discuss.

This article is part of Altanet's series on AI trends in 2026. Next article: Multimodal AI in 2026: When the robot sees, hears and understands the world just like you. See also the complete guide to the series.