The recent introduction of “reasoning tokens” in frontier language models represents a subtle but significant shift in how these systems approach complex problems. For years, we have interacted with AI that provides direct answers. We ask a question, and it generates a response. But what if the most important work the AI does happens before you ever see a single word?
Reasoning tokens are the architecture of that hidden work. They are a special class of token that models use to “think” before they answer, creating an internal chain of thought that remains invisible to the user. This development matters because it fundamentally changes the nature of AI from a probabilistic text generator into a more deliberate problem-solver. It’s the difference between an answer and an insight.
One caveat worth stating plainly: more reasoning does not magically create truth. A model can spend 10,000 reasoning tokens building a beautiful staircase and still climb up to the wrong answer. Reasoning tokens improve the process, not the guarantee. That is why real-world systems still need verification, grounding, and (for high-stakes tasks) human review.
How Reasoning Tokens Work: A Look Under the Hood
When a traditional language model receives a prompt, it begins generating the most probable sequence of output tokens directly. A reasoning model, however, takes a detour. It first generates a series of internal reasoning tokens to break down the problem, explore different angles, and formulate a plan. Only after this internal monologue does it produce the final, visible output. These reasoning tokens are then discarded from the final response, but they are crucial to its quality and accuracy [1].
This process is analogous to showing your work in a math problem. The final answer is important, but the step-by-step process of getting there is what demonstrates true understanding. Reasoning tokens are the model’s way of showing its work to itself.
Consider this practical example. A user prompts the model:
“I have a list of customer feedback emails in a CSV file named
feedback.csv. I need a Python script that reads the file, identifies emails containing the word ‘cancellation,’ and writes the full text of those emails to a new file calledcancellations.txt.”
A traditional model might attempt to generate the Python script directly, and it might succeed. However, a reasoning model would first engage in an internal monologue, using reasoning tokens to plan its approach:
- Deconstruct the Request: “The user wants a Python script. The script needs to perform three main actions: read a CSV, filter for a keyword, and write to a new text file.”
- Identify Necessary Libraries: “For reading a CSV in Python, the
csvmodule is the standard and most robust choice. I’ll need to useimport csv.” - Outline the Logic: “I’ll need to open
feedback.csvfor reading andcancellations.txtfor writing. I’ll loop through each row in the CSV. Inside the loop, I’ll need a conditional statement (if) to check if the string ‘cancellation’ is in the email text. If it is, I’ll write the entire email content to the output file.” - Consider Edge Cases: “What if the file
feedback.csvdoesn’t exist? The script should handle this gracefully. I’ll use atry...except FileNotFoundErrorblock to provide a user-friendly error message. Also, I should usewith open(...)to ensure the files are closed properly, even if an error occurs.”
Only after this internal planning phase does the model generate the final, polished Python script for the user. The reasoning tokens are never seen by the user, but they are the reason the resulting code is more robust, efficient, and handles potential errors.
## What Reasoning Tokens Are Not
- They are not explainability. The internal chain of thought is hidden by design, which means you cannot rely on it as an audit trail.
- They are not a licence to stop fact-checking. Reasoning can reduce errors, but it cannot remove hallucination risk on its own.
- They are not always the right trade-off. Sometimes you want speed, not deliberation. “High effort” everywhere is a fast way to turn your budget into confetti.
If you want trustworthy systems, you measure outcomes and trace actions, not internal monologues.
Reasoning Tokens vs. Chain-of-Thought Prompting
It is important to distinguish reasoning tokens from a related concept: Chain-of-Thought (CoT) prompting. CoT is a technique where the user explicitly instructs the model to “think step-by-step” in its visible output. While effective, CoT is a prompting strategy that co-opts the model’s output space for reasoning. Reasoning tokens, by contrast, are a native architectural feature. The model is designed to think internally, without needing to be explicitly told to do so in the prompt.
| Feature | Chain-of-Thought (CoT) Prompting | Reasoning Tokens |
|---|---|---|
| Mechanism | A prompting technique that guides the model’s output. | An architectural feature of the model itself. |
| Visibility | The reasoning process is visible in the final output. | The reasoning process is internal and hidden from the user. |
| Control | Controlled by the user through prompt engineering. | An intrinsic part of the model’s operation. |
| Efficiency | Can be less efficient as reasoning consumes output tokens. | More efficient as the internal reasoning is separate from the final response. |
While CoT was a clever hack to elicit better reasoning from older models, reasoning tokens represent a more fundamental and powerful solution to the same problem.
## Why This Matters for Agents
Agents live and die by loops: plan, act, observe, revise. Reasoning tokens make the “plan” step more reliable, tools make the “act” step possible, and memory makes the “revise” step coherent across time. Put those together and you get the ReAct pattern formalised: reasoning plus acting, interleaved in a way that reduces hallucination by checking the world mid-flight [2].
This is also why “talking to an assistant” can feel like talking to more than one agent. Under the hood, a system can route between models, call tools, summarise, and maintain state. The user sees one conversation. The system may be running a small choreography.
Practical Implications for Building with Reasoning Models
The shift to reasoning models has several important consequences for developers and researchers:
1. Improved Performance on Complex Tasks: Models equipped with reasoning tokens consistently outperform their predecessors on tasks that require multi-step planning, such as complex code generation, scientific problem-solving, and creating detailed, structured documents.
2. Cost and Context Window Management: While reasoning tokens are not visible in the final output, they do consume computational resources and occupy space in the model’s context window. They are typically billed as output tokens. This means that a prompt that produces a short final answer could still incur significant costs if it required extensive internal reasoning. Developers need to be mindful of this and use parameters like max_output_tokens to control costs and prevent incomplete responses. OpenAI recommends reserving at least 25,000 tokens for reasoning and outputs when first experimenting with these models [1].
3. The Rise of the Responses API: To fully leverage the capabilities of reasoning models, developers are encouraged to use newer APIs designed for them, such as OpenAI’s Responses API. These APIs provide more granular control and better performance for agentic workflows, allowing for more sophisticated interactions than the traditional Chat Completions API [1].
4. New Control Levers (Effort, Summaries, and Incomplete Responses): Reasoning models are not just “smarter chat”. They expose knobs you can use. In the Responses API, you can set reasoning: { effort: "low" | "medium" | "high" } to trade speed for deeper reasoning, and you can use max_output_tokens to cap total generation (reasoning plus visible output) [1]. If you need a human-readable sketch of the reasoning, you can opt into a reasoning summary (for example reasoning: { summary: "auto" }) rather than forcing chain-of-thought into the visible answer. Finally, learn the failure mode: when max_output_tokens is too low or the context window is tight, you can pay for reasoning and receive an incomplete response with little or no visible output. That is not a bug, it is a budgeting and context-management problem you can design around [1].**
The Future is Deliberate
Reasoning tokens mark a pivotal moment in the evolution of artificial intelligence. They represent a move away from the simple, probabilistic generation of text and toward a more deliberate, structured, and reliable form of problem-solving. The most significant advances in AI are no longer just about the quality of the final answer, but about the quality of the hidden process used to arrive at it.
As this technology matures, we can expect to see AI systems that are not only more capable but also more transparent and trustworthy. While the reasoning itself may remain hidden for now, the improved quality and reliability of the outputs are a clear signal that a deeper, more sophisticated form of artificial thought is beginning to emerge.
References
[1] OpenAI. (n.d.). Reasoning models. OpenAI API Documentation. Retrieved from https://platform.openai.com/docs/guides/reasoning
[2] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. Retrieved from https://arxiv.org/abs/2210.03629 (accessed February 1, 2026).
[3] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Retrieved from https://arxiv.org/abs/2201.11903 (accessed February 1, 2026).
[4] OpenAI. (n.d.). Responses API. Retrieved from https://platform.openai.com/docs/api-reference/responses (accessed February 1, 2026).