I switched from ChatGPT to NeonCodex for coding — here's what happened after 3 months

I want to be upfront: I was a ChatGPT Plus subscriber for two years and genuinely didn't think I needed anything else. Then my team at work started using NeonCodex for some bulk data processing tasks and I got curious.

Three months later I've canceled my ChatGPT Plus subscription. This post is my honest account of what changed.

The problem I didn't know I had

My main use case for AI has always been coding help — debugging gnarly production issues, generating boilerplate, reviewing PRs when I'm too close to the code. ChatGPT was fine at this. Not amazing, but fine.

The thing I kept running into: context limits. Our main service is about 40,000 lines of Python. When I'd paste in the relevant files plus the stack trace plus my question, I'd routinely hit the context window. I'd have to carefully trim things, summarize files, leave out the parts I thought weren't relevant — and then half the time the AI would say something that would have been wrong if it had seen the part I removed.

I didn't realize how much mental overhead this was adding until it was gone.

What actually changed

File upload that works for real codebases

The first thing I tested was just uploading our models.py file (2,800 lines) and asking Claude Sonnet 4.6 to explain the relationship between our Order and Transaction models. I half-expected it to time out or hallucinate.

It didn't. It gave me a genuinely good explanation that caught a subtle thing I'd forgotten — a soft-delete pattern we'd implemented inconsistently across three different models. Not what I asked about, but exactly the kind of thing you want the AI to notice.

I then uploaded our entire services/ directory as a zip (about 180KB of Python) and asked it to find all places we were doing synchronous database calls inside async functions. It found 7. Our linter had found 0. We'd shipped a latency bug we didn't know about for four months.

Claude vs GPT-4o on actual debugging

I want to give a real example rather than vibes.

We had a bug where our Celery tasks were occasionally dropping messages under load. The stack trace was:

kombu.exceptions.OperationalError: [Errno 104] Connection reset by peer
  File "celery/backends/redis.py", line 302, in _get_many
  File "kombu/transport/redis.py", line 1104, in _brpop_read

I asked both models with the same context (our Celery config, the error, the Redis config).

ChatGPT-4o told me to increase BROKER_TRANSPORT_OPTIONS timeout and check Redis memory. Reasonable generic advice.

Claude Sonnet 4.6 noticed that our BROKER_POOL_LIMIT was set to None (unlimited) and our Redis cluster had a maxclients config of 1000. It calculated that at our worker count × concurrency, we were hitting ~1100 connections during peak load. It suggested specific values to set and explained why the connection resets were happening specifically during our 9am job bursts.

That was the actual bug. The fix took 5 minutes. We'd been chasing it for a week.

The model routing thing

I was skeptical of "auto routing" — felt like a gimmick. In practice it works better than I expected. Short questions with code go to something fast. Complex multi-file analysis routes to Claude. Research tasks go to Gemini. I don't think about it anymore, which is the point.

What's worse than ChatGPT

Honestly? The UI is less polished in some ways. ChatGPT's conversation history search is better. The mobile experience is weaker. And the free tier is quite limited if you're using it seriously.

The pricing in USD feels steep from India (₹2,499/month converts to about $29) but it's comparable to ChatGPT Plus at $20. The Indian pricing doesn't get a PPP adjustment.

My current workflow

I now run NeonCodex in one window and my IDE in another. The workflow for debugging:

1. Zip the relevant service directory

2. Upload it

3. Paste the error and ask for root cause

4. Get an answer that actually knows the codebase

For writing: I use it for internal documentation, PR descriptions, and architecture docs. The output requires editing but it's usually editing, not rewriting from scratch.

For code generation: still mixed. It's excellent at generating tests once you give it the source function. It's hit-or-miss at generating new API endpoints from a spec — sometimes spot-on, sometimes confidently wrong in ways that take 20 minutes to debug.

Should you switch?

If your main use is casual questions and creative writing, probably not — ChatGPT is fine and you're used to it.

If you work on a real codebase and regularly hit context limits, or you want Claude instead of GPT-4o as your primary model, probably yes.

I was skeptical and I switched. That says something.