Which LLM is actually better for refactoring legacy Python 3.7 code

Claude 3.5 Sonnet outperforms GPT-4o at refactoring legacy Python 3.7 code, according to a developer's three-day migration of a Django 2.2 project to Django 4.2 and Python 3.12, because Claude 3.5 Sonnet respects context-window nuance without forgetting original variable names in 500-line files. The developer reported that GPT-4o hallucinated nonexistent library versions and truncated code with placeholder comments, while Claude 3.5 Sonnet produced async replacements for synchronous I/O blocks that ran on the first attempt. The account recommends constraints-based prompts that state exact language and library versions and forbid placeholder comments, and rates DeepSeek Coder V2 for local scripting and Cursor with Claude for whole-project context.

Which LLM is actually better for refactoring legacy Python 3.7 code Claude /en/tags/claude/ 3.5 Sonnet handles type-hinting and async migrations better than GPT-4o because it respects the context window's nuance without "forgetting" the original variable names in 500-line files. I spent three days last month migrating a messy Django 2.2 project. The goal was to move to 4.2 and Python 3.12. I tried GPT-4o first. It was fast, but it kept hallucinating library versions that didn't exist yet or suggesting deprecated methods from 2021. Claude 3.5 Sonnet, on the other hand, actually "read" the existing codebase and suggested async replacements for synchronous I/O blocks that actually worked on the first run. The difference isn't just in the logic. It's in how they handle the "diff." GPT-4o often truncates code with // ... existing code here ... , which is a nightmare when you're trying to apply changes to a specific line in a 1,000-line file. Claude tends to be more precise with block replacements. Why do most AI-generated refactors break in production? Because LLMs optimize for "looking correct" rather than "being compilable." The mechanism is simple: the model predicts the most likely next token based on common patterns. In a legacy codebase, your patterns are often weird, non-standard, or based on old hacks. The AI tries to "correct" these into standard patterns, which breaks the hidden dependencies. I hit this hard with a celery task migration. The AI suggested a "cleaner" way to handle the task state that looked perfect in the chat window. I pasted it in, ran it, and got a TypeError immediately. Why? Because the AI assumed I was using Celery 5.x, but my environment was locked to 4.x. To stop this, stop asking "Refactor this code." Instead, use a constraints-based prompt: - State the exact version of the language and libraries. - Forbid the use of // ... placeholders. - Demand a "before and after" comparison for any logic change. If you're struggling to find prompts that actually constrain the AI's imagination, checking out Prompt Sharing /en/category/prompts/ is a good way to see how others force the model to stay within version boundaries. How to actually use a Deep Learning Forum for debugging Most people use forums to ask "Why is my code not working?" This is a mistake. You get generic answers. The right way to use a technical community is to provide a "minimal reproducible example" MRE . If you post a 200-line script and ask for help, you'll get three people telling you to use a debugger. If you post a 10-line script that reproduces the bug and the exact traceback, you get a solution in ten minutes. Last Tuesday, I was fighting a CUDA out-of-memory error that made no sense. I had 24GB of VRAM, but the model crashed at 12GB. I didn't ask "Why is my VRAM full?" I posted my batch size, my model architecture, and the exact line where the crash happened. Someone in a specialized deep learning community pointed out that I had a tensor leaking in my validation loop. Fixed in two lines. Comparing the big three for coding tasks I don't believe in "all-purpose" models. I use different tools depending on the task. | Task | Preferred Tool | Why? | | :--- | :--- | :--- | | Boilerplate/Scaffolding | GPT-4o | Fast, knows every library's basic API | | Complex Refactoring | Claude 3.5 Sonnet | Better reasoning, respects context | | Local Scripting/Quick Fixes | DeepSeek /en/tags/deepseek/ Coder V2 | Insanely fast, great for small logic loops | | Entire Project Context | Cursor /en/tags/cursor/ with Claude | Indexing the whole folder is a game changer | The wild part is that using an AI-powered IDE like Cursor reduces the need for a PromptCube homepage /en/ -style prompt manager because the IDE handles the context RAG /en/tags/rag/ for you. You just @ a file, and the AI knows what you're talking about. Dealing with the "hallucination loop" We've all been there. The AI gives you an error. You paste the error back. It apologizes and gives you a new version that creates a different error. You repeat this for 20 minutes. Stop. When you hit the third loop, the AI is just guessing based on the conversation history, not the code. The "context" is now polluted with wrong answers. The fix: 1. Start a brand new chat. 2. Paste the original code and the current error. 3. Tell it: "Your previous attempts failed. Do not suggest X or Y . Analyze the stack trace and explain the root cause before writing any code." This forces the model to reason before it generates. I found that this reduces the "debugging loop" from 10 prompts down to 2. Setting up a professional AI workflow If you're still copying and pasting from a browser into VS Code, you're wasting hours. The current "meta" for high-velocity coding is an AI-native IDE paired with a structured knowledge base. I use a combination of Cursor for the heavy lifting and a curated set of Resources /en/category/resources/ to keep track of the best MCP /en/tags/mcp/ Model Context Protocol servers. MCP is the real deal. Being able to give an AI tool a "tool" to read my local Jira tickets or query my database directly means I don't have to manually explain the business logic. The AI just sees the ticket, sees the code, and suggests the fix. Cost-wise, I spend about $20/month on a Pro subscription. It's a rounding error compared to the 15+ hours of manual debugging it saves me every month. The only risk is laziness. If you let the AI write everything, you'll wake up in six months with a codebase you don't actually understand. Read the diffs. Every single one. Next My data drift detector hit 55/56 on a fault-injection benchmark, but failed the one → /en/threads/9314/