nd o4-mini, as well as DeepSeek R1, all belong to this category. Compared with ordinary models, they can often give more accurate and better-organized answers when handling complex logic, math, programming, and multi-step inference.
Grab It in One Sentence First
A reasoning model "thinks a bit" before answering, whereas an ordinary model is more like an instinctive reaction that outputs the answer directly.
To draw an analogy with concepts from psychology, an ordinary large language model is closer to "System 1" thinking—fast, fluent, and reliant on pattern matching; a reasoning model is closer to "System 2"—slow, deliberate, checking each step of the logic one by one. Of course, this is just a metaphor; the model is still fundamentally a statistical model, except its training method taught it to generate intermediate reasoning steps.
How It Works
The core mechanism of a reasoning model is called Chain of Thought (CoT). Before generating the final answer, the model first produces a "thinking trace" that isn't necessarily visible to the user, and within this trace it analyzes the problem step by step, tests assumptions, corrects errors, and only then outputs the answer.
This intermediate process is sometimes called Extended Thinking. It may consume far more tokens than the final answer, and therefore brings higher cost and longer latency. OpenAI's and Anthropic's reasoning models usually collapse this thinking process, and the user can choose to expand and view it, but it does exist and it affects output quality.
How It Differs from Ordinary Models
After receiving a question, an ordinary large language model usually generates its answer token by token, without explicitly "looking back to check" internally. This approach is fast and low-cost, and suits everyday conversation, copywriting, content summarization, and similar tasks.
A reasoning model's advantage lies in tasks that require multi-step inference: solving math problems, debugging code, logic puzzles, and strategy analysis. In these tasks, even if one intermediate step goes wrong, the subsequent conclusions may all veer off. By extending the "thinking" process, a reasoning model can, to a greater degree, catch and correct mistakes along the way.
But a reasoning model isn't always the better choice. For simple problems, its thinking process is a waste; for creative writing, excessive caution may actually suppress the spark; for applications that need fast responses, its latency is also a problem.
When to Use a Reasoning Model
Scenarios well suited to a reasoning model:
- Math and scientific computation: multi-step solving, formula derivation, data analysis
- Complex code tasks: debugging logical errors, architecture design, reviewing code for defects
- Logical inference: tasks that require verifying whether premises and conclusions are consistent
- Strategic planning: decision-type problems that require weighing multiple options
Scenarios that don't really need a reasoning model:
- Ordinary Q&A and casual chat
- Content summarization and rewriting
- Creative writing and brainstorming
- Applications with strict response-speed requirements
The Trade-off Between Cost and Speed
The API call cost of a reasoning model is usually significantly higher than that of an ordinary model, for two reasons: first, the thinking process itself consumes a large number of tokens, and these tokens are billed the same as output tokens; second, the model's parameter scale is usually larger.
Take OpenAI's pricing as an example: the per-million-token cost of the o3 series is several times that of GPT-4o. Because of its open-source nature, DeepSeek R1's cost can be substantially reduced when self-hosted.
In practice, a common strategy is model routing: have a routing layer judge the task's complexity, send simple tasks to an ordinary model, and only escalate genuinely complex tasks to a reasoning model, thereby striking a balance between quality and cost.
Common Misconceptions
Is slower always better? No. A reasoning model is suited to logic-dense tasks, but on simple tasks its advantage is close to zero and it instead adds waiting time and cost.
Does longer thinking mean a more accurate answer? Not entirely. An overly long chain of thought can sometimes veer off midway, forming "over-reasoning." A good reasoning model's training goal is effective thinking, not longer thinking time.
Can a reasoning model replace an ordinary model? Full replacement isn't recommended. The two types of models each have their applicable scenarios, and using them in combination is usually more reasonable than choosing just one.