1M context is now generally available for Opus 4.6 and Sonnet 4.6

I’ve had thing like a system that has a collection of procedural systems. I would say “replace the following set of defaults that are passed all around for system X (list of files) and in the managed (file) by a config” and it would do that but I’d suddenly see it be like “wait mu and projection distance are also present in system Y and Z. Let me replace that by a config too with the same values”. When system Y and Z uses a different set of optimized values, and that was clearly outside of the scope.

Never had that kind of mistakes happen when dealing with small contexts, but with larger contexts (multiple files, long “thinking” sequences) it does happen sometimes.

Definitely some times when I though “oh well my bad, I should have clarified NOT to also change that other part”, all the while thinking that no human would have thought to change both

If the model requests tool calls one-by-one (e.g. because it needs to see the response from the previous call before deciding on the next) then you have to pay for each back-and-forth.

If you look at popular coding harnesses, they all use careful prompting to try to encourage models to do the former as much as possible. For example opencode shouts "USING THE BATCH TOOL WILL MAKE THE USER HAPPY" [1] and even tells the model it did a good job when it uses it [2].

[1] https://github.com/anomalyco/opencode/blob/66e8c57ed1077814c... [2] https://github.com/anomalyco/opencode/blob/66e8c57ed1077814c...

It'll remain a human job for quite a while too. Separability is not a property of vector spaces, so modern AIs are not going to be good at it. Maybe we can manage something similar with simplical complexes instead. Ideally you'd consult the large model once and say:

> show me the small contexts to use here, give me prompts re: their interfaces with their neighbors, and show me which distillations are best suited to those tasks

...and then a network of local models could handle it from there. But the providers have no incentive to go in that direction, so progress will likely be slow.

About 80% of my code is AI-generated, with a controlled workflow using dev-chat.md and spec.md. I use Flash for code maps and auto-context, and GPT-4.5 or Opus for coding, all via API with a custom tool.

Gemini Pro and Flash have had 1M context for a long time, but even though I use Flash 3 a lot, and it’s awesome, I’ve never needed more than 200k.

For production coding, I use

- a code map strategy on a big repo. Per file: summary, when_to_use, public_types, public_functions. This is done per file and saved until the file changes. With a concurrency of 32, I can usually code-map a huge repo in minutes. (Typically Flash, cheap, fast, and with very good results)

- Then, auto context, but based on code lensing. Meaning auto context takes some globs that narrow the visibility of what the AI can see, and it uses the code map intersection to ask the AI for the proper files to put in context. (Typically Flash, cheap, relatively fast, and very good)

- Then, use a bigger model, GPT 5.4 or Opus 4.6, to do the work. At this point, context is typically between 30k and 80k max.

What I’ve found is that this process is surprisingly effective at getting a high-quality response in one shot. It keeps everything focused on what’s needed for the job.

Higher precision on the input typically leads to higher precision on the output. That’s still true with AI.

For context, 75% of my code is Rust, and the other 25% is TS/CSS for web UI.

Anyway, it’s always interesting to learn about different approaches. I’d love to understand the use case where 1M context is really useful.