# 2026 Is the Year of Agentic Reasoning, Not Multimodal Models

> Hugging Face's summer 2026 survey of open models argues that the field's center of gravity has shifted. Where 2024 was defined by multimodal systems racing to see and hear everything, the story of 2026 is agentic reasoning, models that deliberately think through a problem internally before answering, spending compute on working out the steps rather than on ingesting more kinds of input. The piece credits DeepSeek R1 with popularizing the reasoning first approach, in which a model allocates more of its effort to problem solving and planning. The framing is a shift from breadth to depth, from a model that can take in anything to one that can actually reason its way to a useful result, which is what makes reliable agents possible in the first place. As a state of the field read from a group with a wide view of open models, it is a helpful map of where research energy is flowing. The honest caveat is that reasoning benchmarks can be gamed and internal deliberation is expensive, but the underlying claim, that capability rather than novelty now drives progress, rings true and helps explain why so many recent releases lead with agents.

_Section: [Interesting AI Articles](https://www.wortins.com/articles) · Source: Hugging Face · Published Sunday, August 23, 2026_

## Wortins' read

Hugging Face's summer 2026 survey of open models argues that the field's center of gravity has shifted. Where 2024 was defined by multimodal systems racing to see and hear everything, the story of 2026 is agentic reasoning, models that deliberately think through a problem internally before answering, spending compute on working out the steps rather than on ingesting more kinds of input. The piece credits DeepSeek R1 with popularizing the reasoning first approach, in which a model allocates more of its effort to problem solving and planning. The framing is a shift from breadth to depth, from a model that can take in anything to one that can actually reason its way to a useful result, which is what makes reliable agents possible in the first place. As a state of the field read from a group with a wide view of open models, it is a helpful map of where research energy is flowing. The honest caveat is that reasoning benchmarks can be gamed and internal deliberation is expensive, but the underlying claim, that capability rather than novelty now drives progress, rings true and helps explain why so many recent releases lead with agents.

## Source

[Read the full story at Hugging Face](https://huggingface.co/blog/state-of-open-models-summer-2026)

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