Streaming reasoning content

Watch the model think

grok-build-0.1 exposes its reasoning process via a dedicated field: reasoning_content. This field is available only in streaming mode, letting you see how the model analyzes your code before producing its final response.

How reasoning content works

When the model processes a complex request—refactoring, debugging, or optimization—it goes through an internal reasoning phase. With reasoning_content, this reasoning is exposed in real time during streaming:

for chunk in response:
    # Reasoning arrives first
    if chunk.choices[0].delta.reasoning_content:
        print("[Reasoning]", chunk.choices[0].delta.reasoning_content)

    # Then the final response
    if chunk.choices[0].delta.content:
        print("[Response]", chunk.choices[0].delta.content)

In practice, you first see the model analyze the problem (identifying dependencies, spotting edge cases, planning changes), then the structured solution arrives.

Benefits for developers

Transparent debugging

When the model proposes a bug fix, reasoning_content shows you why it identified that line as problematic. You can validate its reasoning before accepting the change.

Learning

By reading the model’s reasoning, you understand its approach. This is particularly useful when working in a language or framework you’re less familiar with.

Confidence

If the reasoning is superficial or incorrect, you know the response will likely be wrong. This is a warning signal that lets you adjust your prompt before applying the changes.

Difference from non-streaming mode

In non-streaming mode (standard request without streaming), reasoning_content is not available. You receive only the final response. This is why most code editors use streaming mode by default with grok-build-0.1.

In code editors

Most editors integrating grok-build-0.1 don’t display reasoning_content directly to the user—but they leverage it behind the scenes, which is instructive for your own integration. Some validate the reasoning consistency before presenting the suggestion, discarding responses where the reasoning clearly goes off track. Others use it to display an honest progress indicator: rather than a silent spinner, the user sees that the model is actually working. Finally, almost all log it, because when debugging an integration—why did the model propose this absurd change?—the reasoning trace is often the only explanation available.

If you’re developing your own API agent with grok-build-0.1, leveraging reasoning_content is a significant advantage for delivering a better user experience.

Key takeaways

• reasoning_content exposes the model’s reasoning in real time
• It is available only in streaming mode
• You read it via chunk.choices[0].delta.reasoning_content
• Editors use it internally to improve the experience
• It’s a valuable tool for validating suggestions before applying them