I have been doing this for years and honestly, DeepSeek Coder V2 236B is a beast for deep architectural logic. Its way more reliable for complex reasoning than standard models. I also suggest GitHub Copilot with GPT-4 Model for legacy refactoring since it tracks local dependencies better. Just watch out for race conditions; AI still struggles with thread safety logic, so dont trust it blindly.

I'd look at OpenAI GPT-4o Omni Model or Phind-70B Code Model. GPT-4o is solid for architectural logic but can be lazy with long refactors. Phind is great because it searches live docs, which helps avoid those SQLAlchemy hallucinations. GPT-4o is 20 dollars a month, but Phind has a solid free tier if you wanna test it first.

I totally agree with you on the hallucination stuff, it is honestly the most frustrating thing when you are deep in an async project. Niche SQLAlchemy extensions are basically a coin toss with the standard models because they keep mixing up the old legacy syntax with the newer stuff. Its a huge time sink to have to double check every single method it suggests... half the time the library doesnt even have the function it claims to. One thing I have noticed that really helps is moving away from the basic tools and looking for models that support a much larger context window. Most of these complex architectural issues happen because the AI starts forgetting the constraints of your pipeline after a few prompts. I had a similar issue with multi-threaded race conditions a while back where the tool just couldnt keep track of the lock logic I had already built. Basically, the smaller models just dont have the reasoning capacity for deep architectural work. If you find a model that is specifically tuned for coding and has a massive context window, you can actually feed it your whole project structure. That way it doesnt just guess based on generic Python patterns, but actually sees how your ETL pipeline is tied together. It saved me so much headache with dependencies when I finally moved to a model that could actually think through the logic rather than just predicting text.

I totally get the frustration with those niche SQLAlchemy extensions. Like the previous guy said, mixing up 1.4 and 2.0 syntax is a total nightmare and a recipe for disaster. If you are doing heavy lifting with async logic and race conditions, honestly, Anthropic Claude 3.5 Sonnet has been way more reliable for me than GPT-4 lately. It seems to follow complex logic trails without tripping over itself as much. One quick tip: when you are refactoring legacy stuff, try using Cursor Code Editor IDE. Its basically a fork of VS Code but the way it indexes your whole codebase helps it actually understand your local dependencies instead of just guessing. Just a heads up though, even with the best AI, always verify those async blocks manually. Hallucinations in race conditions are super hard to debug later. Better safe than sorry.

This ^

Wait really?? Thats actually super helpful. I always thought it was the other way around.

Like someone mentioned, the hallucinations are just exhausting and I am honestly so over it. I am still pretty new to Python and I thought these tools would make it easier, but instead, I am just stuck fixing fake code all day. It is really disappointing when you expect a boost and get a headache.

• It is kinda like my old car that I have been driving for ten years.
• The check engine light has been on since 2021 and every mechanic tells me something different.
• Last winter the heater stopped working right when we had that massive snowstorm.
• I ended up driving to work in a full parka and gloves just to stay warm while the windshield stayed half-frozen. Anyway lol sorry I kinda went off on a tangent there.