Earlier in February, Anthropic announced its plans to release a new model, Claude 3.7 Sonnet. The new model will display a visible thinking process, with an “extended thinking” toggle for complex prompts. Developers can even assign a ‘reasoning budget’ between 1-128k tokens for how long Claude spends on each problem.
Anthropic’s drive toward longer-thinking models represents a movement in the AI industry toward models that prioritize reasoning and accuracy over speed. Long-thinking AI takes its time to reason with complex problems — generating more insightful responses and reducing hallucinations and other errors while decreasing the time to data. Long-thinking AI, therefore, is designed for accurate outputs for challenging problems, including science, math or coding.
Long-Thinking AI Explained
Long-thinking AI is a form of AI model that prioritizes analysis and accuracy when solving a task rather than speed. Where short-thinking AI models like ChatGPT attempt to solve problems quickly, long-thinking AI aims to generate more thoughtful responses and reduce errors to address more complicated coding challenges. An example of a long-thinking AI model is Anthropic’s Claude 3.7 Sonnet.
Outside of Anthropic, other AI leaders are developing competitive, long-thinking models. For example, Amazon is currently working on a hybrid-reasoning model that supports extended thinking. Google DeepMind’s AlphaGeometry successfully solves complex math problems by combining intuitive guesses (System One) with logical verification (System Two).
The drive toward long-thinking AI has a number of repercussions for both the development and the perception of AI. Let’s break it down.
The Significance of Long-Thinking AI
Long-thinking AI models emulate a dichotomy in human thought identified by Nobel Prize-winning psychologist Daniel Kahneman. System One “operates automatically and quickly with little or no effort.” System One thinking is instantaneous and used for everyday, automatic decisions and thinking. Driving a car on autopilot would leverage System One thinking, for example.
System Two, however, “allocates attention to the effortful mental activities that demand it, including complex computations.” It is logical, detailed, and analytical. Ideally, the human mind should balance both: selecting the type of reasoning required for the task at hand.
The same principle applies to AI. So far, AI models have been developed around System One thinking. When ChatGPT was released in November 2025, users were impressed at the speed at which ChatGPT could produce responses. With GPT-3.5-Turbo generating hallucinations (albeit rapidly) at a rate of 1.9 percent, its applications in precise, sensitive arenas like legal and scientific research were limited.
Hybrid reasoning represents the flexibility to switch between System One and System Two. Therefore, long, or System Two, thinking offers a lower hallucination rate and more accurate, well-reasoned outputs.
Accuracy is only one benefit of long-thinking AI. Incorporating System Two reasoning, next-generation AI models can enhance trust, accuracy and explainability. Resolving the black box problem engulfing AI also makes it easier to sell, as well as contributing toward solving complex world issues, such as sustainability and supply chain issues in the developing world, that require complex and well-reasoned responses.
Diving into the Technicalities of Long-Thinking AI
Long-thinking leverages deep learning techniques, such as transformers and large language models (LLMs), to recognize patterns and generate responses to natural language.
Alongside deep learning, long-thinking AI uses symbolic AI to employ rule-based or knowledge-based systems for structured problem-solving.
Long-thinking AI combines neural and symbolic methods using knowledge graphs, formal logic and probabilistic reasoning to make AI data logic-driven. Claude 3.7 Sonnet integrates rapid responses with extended, step-by-step reasoning within a single framework.
Its hybrid reasoning model excels in coding and complex problem-solving, offering users autonomy over the depth of reasoning applied to tasks.
Proof?
Hybrid reasoning models Claude 3.7 and xAI’s Grok 3 are currently leading in reasoning and coding, ahead of dense transformer-based neural network models like OpenAI’s o3-mini and DeepSeek-R1.
Managing Long-Thinking AI’s Limitations
Long-thinking AI is not an infallible framework. Like other AI paradigms, long-thinking AI’s benefits must be carefully weighed against its (potential) limitations, including computational costs, overfitting and user experience.
1. Computational Costs
The resources needed to power complex synthesis present a problem with computational costs on multiple fronts:
- The energy consumption increases could be unprecedented. According to NVIDIA CEO Jensen Huang, reasoning AI costs “100 times more” computing power than current AI models.
- Consequently, long-thinking AI could foster a detrimental environmental impact. The International Energy Agency suggests data centers could be responsible for 3-to-4 percent of global electricity consumption by 2030, largely due to the rapid expansion of AI technologies.
- From a consumer perspective, smaller businesses may not have sufficient funds to train long-thinking AI models.
In other words, the computational costs of long-thinking AI could be problematic on a large scale, i.e., in terms of the environmental impact of AI, but it could also be challenging for small companies looking to develop or cultivate long-thinking AI solutions with limited resources. For the developers of long-thinking AI, it is highly likely that usage will need to be rationed across platform users.
2. Overfitting
To enhance elaborate reasoning, long-thinking AI systems rely on complex architectures with billions of parameters. While their structural complexity allows for more sophisticated decision-making, theoretically, it also amplifies the risk of overfitting.
3. User Experience (UX)
To new AI users, reasoning customization may be confusing. For example, they may opt for maximum reasoning capacity without realizing that it may compromise their usage limits or output speed. Consequently, developers must ensure that they package long-thinking AI products that are suitable for both amateur and expert use.
Obviously, not all these limitations are made equal, justifying long-thinking AI’s environmental impact is far more challenging than addressing a disappointing user experience, for example. But it’s important to recognize that long-thinking AI is not a band-aid for the deficiencies of current systems, rather, it’s a new and distinct subset of AI with its own limitations.
The next generation of hybrid reasoning models offers a more thoughtful and accurate alternative to speed-focused systems. Rather than simply extending the LLM context window, hybrid reasoning models use long thinking to generate complex, well-reasoned responses.
Advocates of long-thinking AI, such as Anthropic, NVIDIA, and Google DeepMind, are at the beginning of their long-thinking AI journey. Long-thinking AI’s advanced cognitive abilities should be deployed responsibly to promote responsible innovation.
