[GPT-4.1 mini] Corrupted/Garbled "Thought" Process Output: Mixed Languages, Symbols, and Code Fragments

API Bugs
1

Hello everyone,

I’m experiencing an unusual issue with the GPT-4.1 mini model’s internal “Thought” process output. Instead of a coherent chain of reasoning, the output is frequently a long, garbled string of text.

The output includes:

  • A seemingly random mixture of multiple languages (Korean, English, Russian, Arabic, Chinese, etc.).

  • Numerous special characters, symbols, and non-text characters (like \\alpha, \\pi, various script characters).

  • Fragments of code, function names, API calls, and metadata (e.g., Google Search_Functions, Step 1:, [decoded, [(( ultrasonic **!).

Here is the exact example of the corrupted output I encountered:

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My Questions:

  1. Has anyone else observed this specific type of corruption or token bleed in the “Thought” output of GPT-4.1 mini or other similar models?

  2. Does this suggest a potential bug in the model’s internal logging/debugging mechanism or an encoding/decoding error when rendering the raw thought tokens?

  3. Is there a known workaround or setting to suppress or fix this garbled output?

Any insights from developers or experienced users would be greatly appreciated.

Thank you!

2

Hi and welcome to the community!

I wasn’t able to reproduce issue immediately.
Gpt 4.1 is not a thinking model and there is no thought process before returning with a reply.

Are you using ChatGPT or the API?
In the second case, check if your temperature setting is reasonably low. A value of 1 should help confirm this issue.

3

Thank you for your feedback and suggestion regarding the temperature!

I can confirm that I was not using a dedicated “Thinking Model.” The garbled output was the result of the internal Thought log generated during an Agent/ReAct workflow where the model attempts to use tools via the API.

Test Results and Confirmation

  • The issue occurred when the temperature parameter was set to 2.0.
  • After changing the temperature to 1.0 (as you implied by suggesting a “reasonably low” value), the corrupted/garbled Thought process output no longer occurs.

It appears the extremely high temperature setting (2.0) is causing the model to generate chaotic, low-probability tokens when writing its internal Thought log for the tool-use mechanism.

The Core Question

While setting the temperature to 1.0 resolves the issue, my specific task requires the higher level of randomness that 2.0 provides in the final output.

Is there any known way (e.g., a specific API parameter, a header, or a prompt engineering technique) to stabilize the internal Thought stream—ensuring it remains clean and uncorrupted—even when the overall API temperature parameter is set to 2.0?

Thank you again for helping me diagnose the root cause!

1 Like
4

There is almost no application of “temperature”: 2.0 to be had. I don’t know where you came up with that need. It lowers the probability of likely tokens so far that extremely low value tokens emerge as potential selections by the random sampling, and the output becomes even more of a nonsense token generator than the model already is. It’s not going to even be able to call functions reliably, and were it to do so, you’d get server errors. Then the AI wouldn’t be able to properly predict an end token either, and just would continue generating non-stop.

The only way such a high temperature setting could be used is in conjunction with a top_p constraint such as 0.8, so that the tailing input of poor tokens to the sampler is eliminated. Then from the remaining tokens, you still get essentially “random” tokens, but at least ones that could work as language.

Create a story: two bananas are arguing about a suspect orange wanting to become friends.

Running on the edge: top-p: 0.95, temperature: 2 (max 200 tokens)

Certainly! Here’s a whimsical and lighthearted short story about two bananas debating about an orange who wants to befriend them.

The Banana Dispute over the Orange

In a cozy fruit bowl on the kitchen counter, two bananas leaned against each other, peeling slightly in the warm afternoon sun. Bernard was the older, wiser banana — sturdy, firm, and rather serious. His companion, Beryl, was younger and a bit more open-hearted, bright yellow with a cheeky grin.

One day, an orange rolled in—a suspect looking orange named Oscar.

Bernard scrunched his peel and said, “I don’t trust Oscar. He’s different from us. He’s round and bumpy. How do we even know he wants to be friends and not just monopolize the fruit bowl?”

Beryl sighed, shaking her stem a little. “But Bernard, aren’t differences what make life interesting? He seems sincere. I overheard him saying he’d like to get to know us.”

Bernard folded

Lift the constraint of top-p:

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