[GH-ISSUE #881] Incomplete Response Issue with DeepSeek Coder API #522

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opened 2026-10-26 21:22:39 +00:00 by bsakaguchi · 3 comments

bsakaguchi commented

2026-10-26 21:22:39 +00:00

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Originally created by @said7388 on GitHub (May 27, 2025).
Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-V3/issues/881

Hi,

I’m using DeepSeek Coder for code generation and often receive incomplete responses from the API. The output gets cut off mid-function or mid-component, especially when the expected code is longer.

Details:

Model: DeepSeek Coder
Issue: Incomplete responses from the API during longer generation tasks
Impact: Makes it hard to reliably use the generated code

Questions:

Is there any way to increase the token/output limit longer than 8192?
Any recommended workaround to handle or avoid this?
Are there any updates planned to improve response length?

Would appreciate any insights or guidance.

Thanks,
ABU SAID

Originally created by @said7388 on GitHub (May 27, 2025). Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-V3/issues/881 Hi, I’m using DeepSeek Coder for code generation and often receive **incomplete responses** from the API. The output gets cut off mid-function or mid-component, especially when the expected code is longer. **Details:** * Model: DeepSeek Coder * Issue: Incomplete responses from the API during longer generation tasks * Impact: Makes it hard to reliably use the generated code **Questions:** * Is there any way to increase the token/output limit longer than 8192? * Any recommended workaround to handle or avoid this? * Are there any updates planned to improve response length? Would appreciate any insights or guidance. Thanks, ABU SAID

bsakaguchi

2026-10-26 21:22:39 +00:00

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bsakaguchi commented

2026-10-26 21:22:41 +00:00

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@Flink-ddd commented on GitHub (Jun 11, 2025):

Hi @said7388,

I saw your post and thought I'd share my analysis. This is a classic challenge that comes up when working with many Large Language Model (LLM) APIs, where a generation task exceeds the model's maximum output limit.

Based on my research into DeepSeek's documentation and announcements, here’s how I see the situation:

about On the 8192 Token Limit
The 8192 token limit you're hitting is almost certainly specific to the model you're using, which is likely deepseek-chat. For any given model, the provider usually sets a hard max_tokens limit that can't be overridden by the user through an API parameter. So, for that specific model, the short answer is that the limit probably can't be increased.

However, there are several effective ways to work around this without needing to raise the limit on that specific model.

for example:
The Best Solution: Switch to a Newer, More Capable Model
I noticed that DeepSeek recently released a new model called deepseek-reasoner. From the public information available, it seems designed specifically for more complex, long-sequence tasks. It features a much larger context window and its maximum output tokens (max_tokens) can be set as high as 64K. For your use case of generating long code, this seems like the most direct and effective solution. You could try just changing the model field in your API call from 'deepseek-chat' to 'deepseek-reasoner' to see if it resolves the issue.

General Technique: Task Decomposition (Chunking)
This is a common strategy for any LLM. You could try breaking your request down. For example, ask the model to generate the high-level structure first (e.g., class definitions and function signatures). Then, send separate, follow-up requests to get the implementation for each function. This increases the number of API calls but is a reliable way to get around single-response length limits.

The "Continue" Strategy (Continuation)
If the code gets cut off mid-stream, you can feed the incomplete generated code back into the model as part of a new prompt and ask it to continue from there. For example: "Please continue generating the following code, starting from where it was cut off: [paste the incomplete code here]".

I hope can help you

@Flink-ddd commented on GitHub (Jun 11, 2025): Hi @said7388, I saw your post and thought I'd share my analysis. This is a classic challenge that comes up when working with many Large Language Model (LLM) APIs, where a generation task exceeds the model's maximum output limit. Based on my research into DeepSeek's documentation and announcements, here’s how I see the situation: about On the 8192 Token Limit The 8192 token limit you're hitting is almost certainly specific to the model you're using, which is likely deepseek-chat. For any given model, the provider usually sets a hard max_tokens limit that can't be overridden by the user through an API parameter. So, for that specific model, the short answer is that the limit probably can't be increased. However, there are several effective ways to work around this without needing to raise the limit on that specific model. for example: The Best Solution: Switch to a Newer, More Capable Model I noticed that DeepSeek recently released a new model called deepseek-reasoner. From the public information available, it seems designed specifically for more complex, long-sequence tasks. It features a much larger context window and its maximum output tokens (max_tokens) can be set as high as 64K. For your use case of generating long code, this seems like the most direct and effective solution. You could try just changing the model field in your API call from 'deepseek-chat' to 'deepseek-reasoner' to see if it resolves the issue. General Technique: Task Decomposition (Chunking) This is a common strategy for any LLM. You could try breaking your request down. For example, ask the model to generate the high-level structure first (e.g., class definitions and function signatures). Then, send separate, follow-up requests to get the implementation for each function. This increases the number of API calls but is a reliable way to get around single-response length limits. The "Continue" Strategy (Continuation) If the code gets cut off mid-stream, you can feed the incomplete generated code back into the model as part of a new prompt and ask it to continue from there. For example: "Please continue generating the following code, starting from where it was cut off: [paste the incomplete code here]". I hope can help you

bsakaguchi commented

2026-10-26 21:22:41 +00:00

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@github-actions[bot] commented on GitHub (Jul 12, 2025):

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. If you believe this issue is still relevant, please leave a comment to keep it open. Thank you for your contributions!

@github-actions[bot] commented on GitHub (Jul 12, 2025): This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. If you believe this issue is still relevant, please leave a comment to keep it open. Thank you for your contributions!

bsakaguchi commented

2026-10-26 21:22:41 +00:00

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@github-actions[bot] commented on GitHub (Jul 27, 2025):

false

@github-actions[bot] commented on GitHub (Jul 27, 2025): false