Why did GPT-4 Turbo get worse at coding

You're not imagining it — GPT-4 Turbo genuinely produces messier code than it used to. Developers across Reddit, Twitter and GitHub have documented the decline since early 2024, with the model now generating incomplete f

Why did GPT-4 Turbo get worse at coding

You're not imagining it — GPT-4 Turbo genuinely produces messier code than it used to. Developers across Reddit, Twitter and GitHub have documented the decline since early 2024, with the model now generating incomplete functions, skipping error handling and repeating itself mid-solution.

What's actually happening

OpenAI updated GPT-4 Turbo multiple times throughout 2024, and each snapshot version behaves differently. The model you're using today (`gpt-4-turbo-2024-04-09` via the API, or whatever ChatGPT Plus currently points to) underwent optimisations that traded some coding precision for speed and cost efficiency. OpenAI hasn't published detailed changelogs explaining these shifts, but the pattern is clear: newer snapshots hallucinate package names more often, truncate long code blocks and struggle with multi-file refactoring tasks that earlier versions handled cleanly.

The issue intensified after the `gpt-4-0125-preview` release. That version introduced "lazy loading" behaviour where the model would write comments like `# ... rest of the function remains the same` instead of completing the code. By April 2024, users reported GPT-4 Turbo defaulting to Python 2 syntax unprompted, inventing non-existent library methods and producing functions that failed basic linting. If you're comparing against GPT-4 from mid-2023, you're comparing against a fundamentally different training run with different reinforcement learning from human feedback.

Temperature settings also matter. ChatGPT Plus uses higher default temperatures than you might set via API, which increases randomness. Even small bumps from 0.2 to 0.5 cause the model to "improvise" more, leading to untested code patterns and speculative fixes.

How to fix it

  • Switch to GPT-4o or o1-preview in ChatGPT. Click the model selector dropdown at the top of a new chat, choose GPT-4o (or o1-preview if you have access). These models use newer architectures that handle structured outputs better. GPT-4o particularly excels at sustained coding sessions without degrading mid-conversation.
  • Pin a specific model version via API. If you're building through platform.openai.com/api-keys, stop using `gpt-4-turbo` as your model string. Use `gpt-4-0613` (the last stable GPT-4 snapshot before Turbo) or `gpt-4o-2024-08-06`. Check model behaviour changes for current version strings. This prevents OpenAI from auto-switching you to newer snapshots.
  • Lower your temperature to 0.1 or 0. In API calls, set `"temperature": 0.1` in your request parameters. In ChatGPT, you can't control this directly, but using Custom Instructions to say "Always complete code fully without placeholders" helps. Colder temperatures reduce creative guessing.
  • Break requests into smaller chunks. Don't ask GPT-4 Turbo to refactor an entire 800-line class at once. It'll cut corners after 200 lines. Instead, request one method at a time: "Refactor the `parse_user_input` method to handle JSON arrays." Then move to the next method. This keeps the model focused.
  • Use system prompts that demand completeness. Start every coding session with: "You are an expert programmer. Always write complete, runnable code with no placeholders. Include all imports, error handling and edge cases. Never write comments like '... rest remains the same'." This explicitly overrides the lazy summarisation behaviour.

If GPT-4 Turbo still produces broken code after these steps, copy the exact error message and broken output into a new chat using GPT-4o. The fresh model context often corrects what the previous version mangled.

If that doesn't work

Document specific examples where GPT-4 Turbo fails your coding task. Save the exact prompt, the model's response and the error you encountered. Submit this through the feedback thumbs-down button in ChatGPT or email it to OpenAI via the official support contact process. Include your account email, the approximate date/time of the conversation and whether you're using Plus, Team or Enterprise.

OpenAI doesn't promise fixes for individual reports, but aggregated feedback about coding regressions does influence future model updates. Realistic response time for acknowledgment: 3-7 days. Actual model improvements: weeks to months.

Questions people actually ask

Q: Will OpenAI fix GPT-4 Turbo or is it permanently worse now?

A: OpenAI continues releasing new snapshots, but there's no guarantee they'll restore old coding quality. The trend points toward newer model families (4o, o1) rather than updating Turbo. Switch models rather than waiting for a fix.

Q: Does this affect ChatGPT and the API equally?

A: Yes, they use the same underlying model weights. The only difference is ChatGPT's higher default temperature and built-in system prompts you can't see. API users have more control to compensate.

Q: Is GPT-4o actually better at coding or just newer?

A: GPT-4o genuinely outperforms GPT-4 Turbo on most coding benchmarks — faster, more accurate completions, better at following instructions across long conversations. It's not just recency bias.

What to remember

  • GPT-4 Turbo degraded noticeably after January 2024 updates — incomplete code and hallucinated functions are common
  • Switch to GPT-4o in ChatGPT or pin `gpt-4-0613` via API for more reliable output
  • Set temperature to 0.1, break large tasks into small requests, explicitly forbid placeholder comments
  • If issues persist, check ChatGPT troubleshooting steps and document failures for OpenAI feedback
  • Newer model families replaced Turbo — don't expect retroactive quality improvements

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*openai-support.com is an independent resource, not affiliated with OpenAI Inc.*

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