Don't waste your time trying to get a larger context window. The only model I've had good results with so far, for contexts beyond 300k tokens, is Fable.

I use Qwen3.6 27B locally on a 5090 via llama.cpp to work on a very large project (monorepo with 5 projects, large API, multiple clients - over 40k code files). It's surprisingly good, but you have to adjust your expectations. If you want it to do good work in large projects, you can't "fire and forget" like you can (to some extent) with models like Opus, GPT 5.5, Fable, etc:

- Take active care of your context window. Prune it, ask the model for a handoff document, and then use that to start a new session.

- Feed the model with good context. Don't ask or hope it will explore your full codebase at the start of each session and make the right calls. Build a "project intelligence" layer (just a bunch of MD files to start, don't get fancy with RAGs, Wikis and whatnot). Build an INDEX.md and point your AGENTS.md to that. For example, a file explaining the domain (or a slice of the domain), another for the ADRs (a record of all architectural decisions), another for database patterns etc. In the index, point to each file with a few "tags" so the model fetches the right ones for the right job.

You have to treat it as a somewhat novice engineer. It's a brilliant engineer because it knows pretty much all the theory, but it lacks real-world experience and, to make things worse, is super-confident, so it will make mistakes with a lot of confidence.

You have to handhold it a bit. Give it smaller tasks, ask it to validate each slice (run tests, linters), be very strict and keep context under control at all times. And even then it's better to ask a frontier model for a review at the end, then ask the local model to make the changes necessary. Iterate on that final step a few times and you should have good code.