AI glossary
Reasoning Model
A reasoning model is a large language model specifically trained to produce an extended chain of intermediate reasoning steps before giving a final answer, typically improving performance on tasks that require multi-step logic, math, or planning compared to models that generate an answer directly.
The difference between a standard large language model and a reasoning model comes down to when the thinking happens. With a standard model, the reasoning is implicit in the weights, and the model jumps straight to the output. A reasoning model makes that thinking explicit, writing out its logic before committing to a result. This shift from implicit to explicit processing changes how developers interact with the system, especially when accuracy on complex problems matters more than raw speed.
Reasoning model vs chain-of-thought prompting
It is easy to confuse a reasoning model with chain-of-thought prompting, but the mechanism is fundamentally different. Chain-of-thought is a technique applied to a regular model: the user asks the model to “think step by step” in the prompt, encouraging it to write out intermediate reasoning before its final answer. You are essentially asking the model to simulate reasoning by forcing it to output its thoughts.
A reasoning model, by contrast, is trained to generate long internal reasoning sequences on its own, without being explicitly prompted to do so. This behavior has been built into the model during training itself. When you send a query to a reasoning model, it doesn’t need a special instruction to start thinking; it naturally allocates compute to work through the problem before providing the solution. This internalized capability often leads to more consistent and deeper logical structures than prompted chain-of-thought, which can sometimes feel like the model is just following a template.
How reasoning models are trained
The training process for these models diverges significantly from standard supervised fine-tuning. Standard fine-tuning teaches a model to match human-written example outputs directly. Reasoning-focused training, however, uses reinforcement learning that rewards the model based on whether its final answer is correct.
This approach, often involving reinforcement learning or RLHF, encourages the model to develop reasoning strategies that reliably lead to correct outputs. The model isn’t just mimicking a human’s style of writing; it is optimizing for the outcome. It learns that taking a specific logical path results in a correct answer, while a flawed path does not. This trial-and-error process during training helps the model discover efficient ways to break down complex problems, effectively “learning how to think” rather than just “learning what to say.”
Examples
Several major providers have introduced reasoning-focused models in recent updates. OpenAI’s o1 and o3 model series, reasoning-focused models introduced starting in September 2024, represent a significant shift toward explicit reasoning capabilities in commercial offerings. These models are designed to tackle complex problems by spending more time thinking before responding.
Another notable example is DeepSeek-R1, released in January 2025, notable for its developer openly publishing details of the reinforcement-learning approach used to train its reasoning behavior. This transparency highlighted how different training regimes can yield distinct reasoning styles. Other major AI model providers have introduced their own reasoning-mode variants of their models, signaling that explicit reasoning is becoming a standard feature rather than an experimental novelty. These models often operate in a “reasoning mode” where the system automatically decides to engage extended thinking for difficult queries.
The cost trade-off
There is a clear trade-off between accuracy and cost when using these models. Reasoning models generally take longer to respond and cost more per response than non-reasoning models on the same task. This is because generating the intermediate reasoning tokens adds substantially to the total length of the model’s output.
Most APIs charge based on the number of tokens generated. Since a reasoning model can generate a lengthy internal reasoning sequence before giving a short final answer, the total token count for a single response increases significantly. For developers building high-volume applications, this can impact both latency and budget. It is essential to evaluate whether the improved accuracy on complex tasks justifies the higher cost per request.
When to use one
Reasoning models tend to show the largest benefit on tasks that require multi-step logical deduction, mathematics, code debugging, or planning. In these scenarios, working through intermediate steps improves the chance of a correct final answer by reducing logical errors that occur when jumping straight to a conclusion.
For simple, direct tasks where extended reasoning offers little benefit, a standard non-reasoning model is often faster and cheaper while achieving similar quality. For example, if you are asking for a translation or a summary of a short text, a reasoning model might overthink the process, adding unnecessary latency. Use reasoning models when the problem is complex enough that a single-pass generation might miss a subtle logical constraint. They are also valuable for agentic AI workflows, where an agent needs to plan a sequence of actions before executing them.
FAQ
What is the main difference between a reasoning model and a standard LLM?
A standard LLM generates an answer directly, often implicitly using its training data. A reasoning model is explicitly trained to output a chain of intermediate reasoning steps before providing the final answer, which improves accuracy on complex tasks.
Do reasoning models require special prompts?
No, unlike chain-of-thought prompting which requires you to ask the model to “think step by step,” a reasoning model naturally generates reasoning steps without explicit prompting because this behavior is built into its training.
Why do reasoning models cost more?
They generate more text because they output intermediate reasoning steps along with the final answer. Since most APIs charge per token, the increased output length leads to higher costs per response.
Are reasoning models better for all tasks?
No. For simple tasks like translation or summarization, a standard model is often faster and cheaper. Reasoning models excel at multi-step logic, math, and planning where intermediate steps reduce errors.