What are thinking tokens in reasoning models
How it works
Thinking tokens act like internal markers or placeholders that guide a reasoning model to pause and explicitly process intermediate steps before producing a final answer. Imagine solving a math problem by writing down each calculation step on paper; thinking tokens serve as those written steps inside the model's token stream. This forces the model to "think aloud" internally, breaking down complex reasoning into manageable chunks, which reduces errors and improves logical coherence.
Concrete example
Consider a reasoning model tasked with solving a multi-step arithmetic problem. The model generates tokens representing intermediate calculations (thinking tokens) before the final answer token.
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
messages = [
{"role": "user", "content": "Solve: (3 + 5) * 2. Show your thinking steps."}
]
response = client.chat.completions.create(
model="gpt-4o",
messages=messages
)
print(response.choices[0].message.content)Step 1: Calculate 3 + 5 = 8 Step 2: Multiply 8 by 2 = 16 Answer: 16
When to use it
Use thinking tokens in reasoning models when tasks require multi-step logical deduction, complex arithmetic, or chain-of-thought explanations. They improve accuracy and transparency in domains like math problem solving, code generation, and scientific reasoning. Avoid using them for simple queries where direct answers suffice, as they add overhead and latency.
Key terms
| Term | Definition |
|---|---|
| Thinking tokens | Special tokens representing intermediate reasoning steps inside a model's output. |
| Reasoning model | A language model designed to perform multi-step logical or mathematical reasoning. |
| Chain-of-thought | A prompting technique that encourages models to generate intermediate reasoning steps explicitly. |
| Inference | The process of generating output tokens from a model given an input prompt. |
Key Takeaways
- Thinking tokens enable models to break down complex problems into explicit intermediate steps.
- They improve reasoning accuracy by simulating internal thought processes during inference.
- Use thinking tokens for tasks requiring multi-step logic, not for simple direct answers.