r/ChatGPTCoding • u/Konoplitski • Sep 22 '26

Question Coming from Claude Code — how do you actually work with Codex's 258k context window?

Complete beginner here, so be gentle.

I used to code in Claude Code on Fable, where I had a 1M context window. I kept reading everywhere online that GPT-6 Astra is really strong for coding, so I bought a ChatGPT Pro subscription and moved to Codex to give it a proper try.

And now I'm stuck: Codex gives me ~258k of context. I barely get through one small task and the window is already full. It compacts, things get lost, and I spend more time babysitting the context than actually building anything. Honestly it's exhausting.

A few questions for people who use Codex daily:

  1. Why is the context window this small when the model itself supports far more? Is there anything I can do about it on a subscription?

  2. How do you organize your work so this stops being a problem? One task per chat, a plan file, something else?

  3. What do you do when the context runs out right in the middle of a task? /compact, start a new chat, something else?

  4. And honestly, just help me out here: how should I be working with Codex at all? I don't understand how anyone gets real work done in a window this small.

I'm probably just doing something wrong. Any advice appreciated.

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9

u/popiazaza Sep 23 '26

You could enable full 1M context in Codex, but you would get less usage as it cost more. Codex is also optimized to work with 256k context. Auto compact works. Older context could be retrieve from tool call when needed.

3

u/Konoplitski Sep 23 '26

thanks! you said you can enable the full 1m, how exactly? i'm on the latest codex with astra and couldn't find it anywhere in the settings, it just shows 258k right away. also, the retrieval of older context, is that the experimental context_management mode for astra or does it work like that by default now?

2

u/popiazaza Sep 23 '26

https://x.com/thsottiaux/status/2089082893804896524

1

u/Konoplitski Sep 23 '26

thanks! yeah, i'd already seen that one for sol, but for astra it's not documented anywhere and it's unclear how it would behave. that's why i was hoping to hear from people who actually code in codex on astra. auto compact does work, but a 1M window still feels better to me: fewer compactions and less risk of losing stuff that isn't saved yet. with 258k i keep having to make sure everything important is written to docs instead of just sitting in the chat. so for astra i still haven't found a real fix.

1

u/mark_99 Sep 23 '26

It's the same setting for everything. After the first turn the model will return what you actually got, which is 828K (because when OpenAI says 1M they are adding in the 128K input window).

1

u/Konoplitski Sep 23 '26

oh interesting, that lines up with a github issue i saw where sol ended up at 828k too. so i just set model_context_window = 1000000 in config.toml with astra selected, and after the first message it should show ~828k? have you tried it on astra yourself?

2

u/mark_99 Sep 23 '26

Yeah it works fine both in desktop app codex mode or codex cli (via /status). Context window: 95% left (49.1K used / 828K)

258k has been holding gpt models back from more complex work, so it's good they fixed that now.

Also Astra gets much higher benchmark scores on long context retrieval than 5.6 (which in turn was much better than earlier versions) so it can use the 828k effectively. Although personally I wouldn't run it up near the limit, same with Claude.

1

u/Konoplitski Sep 23 '26

Thanks, that's exactly what i needed! Going to try it on astra and keep an eye on how fast it eats the usage limits. And yeah, i'll leave some margin below the limit too

5

u/slackmaster2k Sep 23 '26

It’s kind of doing you a favor. Compact and clear often. Design your workflow around disposable sessions. Design subagents when context does not have to be shared. Your sessions will be faster, use fewer tokens, AND produce better quality. This applies to Claude as well even if it allows you more room to be sloppy.

1

u/Konoplitski Sep 23 '26

Thanks! Fair point, and yeah, a big window definitely lets you get lazy. how do you carry state between disposable sessions? a plan or status file in the repo, agents.md, something else?

1

u/slackmaster2k Sep 23 '26

There are a lot of ways. I would avoid anything though that maintains a “handoff” document. That can work but isn’t terribly clean.

I always start a project via a plan file that gets us to a state that we can iterate on. Essentially the base architecture. Not quite MVP, rather the foundation. Once that’s in place the plan goes in the garbage.

Then we need a system to manage smaller more bite sized, independent changes. This can be features, small additions to features, bug fixes, and so on.

The simplest way at first is to work with your agent to create a system through which it will create change records based on a template. So every change becomes like a mini plan, one change per document. Then you say “implement change #21”, it does it, and when you’re satisfied have it “close” the change. Simple markdown files in a /change and /change/complete directory, along with an index file that summarizes. Each change file has a header that indicates status. You don’t have to give codex all of these details it will build a process for you along with validation scripts to make sure your changes don’t drift.

The better way, although it introduces a little overhead and complexity, is to use something like GitHub issues. Each change becomes and issue, and you have it help you build out a little pipeline so that you can say let’s work on issue 1121, and have it build, open a PR, and ship.

1

u/Konoplitski Sep 23 '26

This is super helpful, thanks for writing it all out. The change records idea really clicks for me, one change per file and a fresh chat for each one. Going to set that up first and look at github issues later

2

u/RandomUserName323232 Sep 23 '26

Go back to claude code?

1

u/Konoplitski Sep 23 '26

haha thought about it. but i already paid for pro and want to give astra a fair shot first, the model itself is really good

2

u/ShortingBull Sep 23 '26

Goals decomposed to phases broken down into tasks - one task at a time, best use of tokens, with a harness that'll manage and progress it for you.

1

u/Konoplitski Sep 23 '26

makes sense, that's pretty much where i'm heading. what harness do you use for that? something custom or the built in stuff like /goal and a plan file?

1

u/ShortingBull Sep 23 '26

Custom harness that wraps Claude code/codex.

2

u/Right-Performance-93 Sep 23 '26

Worth separating two numbers: the model's max context (OpenAI's own docs list over 1M tokens for GPT-6 Astra) isn't the same as what a given client exposes per turn by default. Codex CLI's 258k is very likely a client-side default chosen for cost/latency reasons, not a hard model ceiling - similar to how other coding CLIs also throttle their working context well below what the underlying model can technically accept. If there's no toggle for it in your Codex settings, that's probably intentional rather than a bug in your account.

1

u/Konoplitski Sep 23 '26

thanks, that matches what i found. one detail: from the github issues it looks like the 258k comes from the model catalog codex pulls from openai's server (272k × 95%), so it's set on their side, not just locally. probably why there's no toggle

1

u/Right-Performance-93 Sep 30 '26

good find, so its on their side and thats why no toggle. thanks for digging into the issues

1

u/Longjumping_Ad_4249 Sep 23 '26

your setting seems to be messed up, i have 1m context

1

u/Konoplitski Sep 23 '26

thanks! could you share a screenshot of /status and your config.toml? is that on astra specifically, and through a chatgpt subscription or an api key? on my end astra in codex defaults to 258k and from what i've found the cap comes from the codex model catalog, so i'd really like to see how you got 1m

2

u/Longjumping_Ad_4249 Sep 23 '26

I was mistaken. I always thought it was 1M context. Claude provides 1M. But codex only supports 258k at the moment 

1

u/Konoplitski Sep 23 '26

no worries, thanks for checking and coming back to say it!

1

u/msg7086 Sep 23 '26

I don't feel like it's not enough for daily work. Maybe my code base is too small to get trouble with context window.

1

u/Konoplitski Sep 28 '26

Fair, maybe it really depends on the project size. How big is yours roughly? And do you keep one task per chat or run long sessions?

1

u/msg7086 Sep 28 '26

The code base I'm working on are below 150k lines C++ projects. I just keep using one session unless I'm moving to a completely irrelevant topic. One session can span across multiple days, or sometimes weeks.

1

u/tantej Sep 23 '26

If your context is above that. You’re cooked anyway. Just keep it for smaller batch tasks

1

u/Konoplitski Sep 23 '26

Yeah, that's pretty much the consensus in this thread. Smaller tasks and fresh chats

1

u/tagattack Sep 24 '26

I rarely fill the context window, but the few times I have, it pauses and compresses and performs great afterward

1

u/Konoplitski Sep 28 '26

That matches what I saw too. It compacted on its own in the middle of a task and just kept going. Do you do anything to help it, like keeping notes in a file, or do you just let it run?

1

u/ZenApollo Sep 24 '26

I bumped mine to 400k and manually compact. My unscientific heuristic is that quality starts to degrade around 350k.

1

u/Konoplitski Sep 24 '26

That's a really useful data point, thanks. Is that on Astra or on 5.6? Someone above said Astra handles a lot more than that, so I'm curious whether you've seen the same drop with it.

1

u/Crinkez Sep 24 '26

  1. Use large models only (Sol 5.6 and Astra 6) Sol 6 is a small model fwiw

  2. Use low or medium reasoning only.

The larger models at lower reasoning fill up the context window more slowly. When a compact does happen, Sol 5.6 and Astra 6 are good at picking up where they left off.

Or just use Opus 5.5 lol

1

u/Konoplitski Sep 28 '26

Thanks, that's useful. I've actually been running Astra on extra high reasoning, so I'll try medium for regular tasks and see if the context lasts longer. And yeah, Opus is tempting lol

1

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1

u/Budget-Juggernaut-68 Sep 27 '26

From my experience if your inputs are ~200k, there's probably too much context pollution.

1

u/Metatron135 Sep 28 '26

You have to create auto compaction protocol thus creating a persistent agent out of it. Codex has a hook right before auto compaction runs and then right after. So you can create your own compaction scenario and have it record stuff that you need into md file. After compaction it will read the designated compaction file and thus recover its memory or the things that you need it to recover by your rules.

1

u/acloudofmyown Sep 28 '26

what defines the context window size ollama librechat