Claude Opus 4.5 [Too much errors in code]

[Alexteckerz]

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I have problem with Claude Opus 4.5 model. The code its generates has too much syntax errors . So I have to check every piece of code for errors every time (errors like extra spaces, extra characters in the code etc) . Is this the issue known? Or is it just me?

[Quantum-Fiend]

Hello @Alexteckerz ,

Thank you for bringing this up. The issue of Claude Opus 4.5 generating code with too many syntax errors, such as extra spaces or unexpected characters, is a known challenge when working with some AI code generation models.

Here are some key points to consider:

1. Current Model Limitations: While Claude Opus 4.5 is quite advanced, it is not perfect and may occasionally produce code with minor syntax or formatting errors. These can stem from the complexity of the prompt or the context length.
2. Prompt Engineering: Improving the prompt quality and adding specific instructions to avoid extra spaces or characters can help reduce these errors. For example, explicitly asking for clean, syntactically correct code can sometimes improve output quality.
3. Post-processing: It is a common practice to run generated code through formatters or linters (e.g., black for Python, prettier for JavaScript) to automatically clean up formatting issues and catch syntax errors before running the code.
4. Feedback Loop: If possible, providing feedback to the model developers or using newer versions of the model (when available) might help address these issues in the long term.
5. Workaround: As a practical measure, consider implementing an automated validation pipeline that tests generated code snippets for syntax correctness before use.

If your use case demands higher accuracy, you may also want to explore alternative models or newer versions that might have improved in code generation tasks.

Feel free to share sample prompts or code snippets that are causing issues, and I can help review or suggest prompt improvements.

[Alexteckerz]

The thing is I know that models can make errors. But I`ve pointed out that exact this model - Claude Opus 4.5 - made the most mistakes in every (I mean literally every) code generated. So its kind of an issue for such powerful model. And also. When I go direct to Claude and use the exact same Claude Opus 4.5 model - it does not produce such massive amount of mistakes. Its a very very very huge difference.

[Quantum-Fiend]

Thanks for the additional insight. That definitely sounds concerning if Claude Opus 4.5 is consistently generating code with many mistakes compared to other models, especially the core Claude model.

It might be helpful to provide some concrete examples or snippets where you see this behavior, so the community or developers can better understand the scale and nature of the problem. This could help identify whether it’s an issue with that specific version or possibly a usage or prompt-related factor.

In the meantime, I’d also recommend checking if there are any recent updates or patches for Claude Opus 4.5, or if alternative settings or prompt adjustments improve the output quality.

Please feel free to share more details — happy to help troubleshoot further!

[saitejabandaru-in]

You’re not imagining it — this is a known behavior pattern, not something specific to your account.

Claude Opus 4.5 is optimized more for reasoning and long-form generation than for producing perfectly formatted code. When used inside Copilot (or similar tools), it can sometimes introduce small syntax or formatting errors, especially in longer code blocks or when switching languages.

A few things that help reduce this:
– Ask for smaller, more focused code blocks instead of large files at once
– Explicitly ask for “valid, runnable code” or “no placeholders”
– Use Copilot’s “Fix” or “Explain” actions to validate output
– If accuracy matters more than reasoning, switching to a more code-focused model (like GPT-4.1 or similar) often gives cleaner results

So this isn’t a bug in your setup — it’s a tradeoff in how that model is tuned. GitHub is actively refining how different models are used for coding vs reasoning, but for now you’ll get more reliable syntax from models optimized specifically for code generation.