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First identify what is happening: a long visible <think> trace, an overlong final answer, repeated phrases, or an empty final response. Qwen documents different controls for reasoning mode and repetition, so changing one setting at a time is more reliable than applying a generic “shorter answers” fix.
Identify which output problem you have
- Visible reasoning: The response includes a long
<think>...</think>section before the final answer. Qwen3.8-27B uses thinking mode by default, and its model card identifiesxhighas the default reasoning effort. Qwen’s model card describes the available modes and controls. - Overlong final answer: The final response is coherent but longer than needed. Try asking for a specific format or length, and evaluate reasoning effort or mode separately from sampling parameters.
- Repetition loop: The final response repeats phrases or sentences instead of progressing. Test a repetition-related parameter only after confirming your serving framework passes it through.
- Empty final response: Check whether the endpoint returned a final-answer field, what finish reason it reported, and whether reasoning content was preserved. An empty final answer is a different symptom from ordinary verbosity.
Where your API or runtime exposes them, capture the final content, finish reason, token usage, and reasoning content for a few affected calls. Those details help distinguish a generation setting from an issue specific to a model-serving path.
Reduce visible reasoning or deliberation
Try a lower reasoning effort
Qwen documents low, medium, and xhigh reasoning effort, with xhigh as the default. If responses spend too long deliberating, compare medium or low against the current setting using the same prompts. Lower effort can reduce reasoning depth and cost, but may be less suitable for complex tasks; check answer quality as well as length.
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Test the instruct or non-thinking path
If you want a direct answer rather than visible thinking content, use the model’s documented instruct/non-thinking path for your serving setup. This changes the requested behavior, not merely how the answer is displayed, so validate it on tasks where analysis quality matters. Qwen’s model card includes usage examples for disabling thinking content; follow the syntax supported by your actual endpoint.
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Use Qwen’s sampling suggestions as a baseline
Qwen publishes different suggested sampling sets for thinking and instruct/non-thinking mode. Treat them as starting points, not guaranteed optimal values for every framework or prompt.
| Mode | Suggested settings from Qwen |
|---|---|
| Thinking | temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0 |
| Instruct/non-thinking | temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0 |
These are Qwen’s recommendations in its model documentation. Avoid combining several changes at once: otherwise you will not know which adjustment helped or hurt.
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Address repeated phrases with a penalty change
Qwen’s model documentation says: “For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetition.” It also warns that higher values may occasionally cause language mixing and a slight decrease in model performance. If your current setting is already nonzero, do not assume that raising it further will help.
The separate Qwen Cloud DashScope chat-parameters reference documents an accepted presence-penalty range of -2 to 2 and lists 1.5 as a Qwen3.8 non-thinking default. These figures refer to that API documentation and mode; they are not a universal setting for every local runtime or serving endpoint. Use the specific endpoint’s documentation and avoid stacking presence and repetition penalties without testing their effects individually.
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Verify that your runtime applies the settings
A client accepting a parameter does not prove the model server uses it. Qwen notes that framework support varies. Check the effective generation configuration and the documentation for the endpoint or local inference framework you are using. Qwen’s model page lists vLLM, SGLang, and TokenSpeed as serving options and recommends current framework versions for compatibility.
- Confirm the parameter name, accepted range, and mode-specific defaults for your actual endpoint.
- Check whether the server forwards the setting to generation or silently ignores it.
- Use the mode’s appropriate chat template and request format; a mismatch can make a setting ineffective or change the output behavior.
If calls return an empty final answer
A QwenLM GitHub issue dated 2026-08-19 reports empty final content with finish_reason: stop and repeated reasoning in one reporter’s Qwen3.8-27B setup. The report describes a limited experiment, not a general failure rate or an official confirmed fix. Its author suggests comparing lower reasoning efforts; the reported repetition-penalty workaround is preliminary. See issue #216 for the setup and qualifications.
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If this matches your symptom, compare low or medium effort with xhigh on the same requests, and record whether the final content appears. If only one endpoint or framework is affected, investigate its template, parameter forwarding, and finish handling before treating the behavior as a model-wide problem.
Quick Recap
Run a controlled before-and-after check
- Choose a few representative prompts, including one that reliably produces the problem.
- Save the current mode, effort, sampling settings, final-answer content, finish reason, and token usage where available.
- Change one setting only: for example, lower effort, switch to the documented non-thinking path, or adjust presence penalty for a repetition loop.
- Rerun the same prompts through the same endpoint and compare usefulness, answer length, repeated text, finish reason, and token consumption.
- Keep the change only if it improves the symptom without unacceptable losses in answer quality.
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