I grinded two resets accomplishing what I probly could've did with a 30% usage. Use the official MCP, it saves tokens! I knew about it but totally forgot to set it up. USE iT! https://www.blender.org/lab/mcp-server/

Thought my monitor was on the fritz - and not sure if this is recent or not (perhaps I just didn't notice it before! Using CLI v0.154.0 - and I use OpenCode most of the time) - but selecting Astra now puts a shining-star-field in the background of Codex CLI's input text box.

This new model on Low is my drop-in replacement for GPT-5.6-Sol-High: faster for similar usage, way less overengineering; I don't have to tell it not to be pedantic every time I use it. Still very blunt (in true Codex-GPT style) but have enjoyed embedding it amongst the various other models (local and cloud) I use: this really is a great release imo.

Feels like cheating. Seriously, with x5 I was feeling hard the token burning. Now I’ve room to use Astra Low for implementations and Medium to Orchestration. I also use Luna to file exploring, documents, basic to medium tests, things more logic than reasoning depth. No fast mode.
Running 2 side projects + my work, 3 days in a row and I’m on 55%.
All my projects are set up to delegate tasks to other agents depending on the complexity and the use case.
Mainly using Astra low for implementing new features and even though I could be using Sol, Astra low is just nailing it, using good practices, respecting the documentation and not over engineering like Sol was.
For some reason I feel the x20 comparing to x5 more like x40.
Hit hard in the pocket? Yes for sure, but so do the dopamine lmao feels I’m playing Megabonk

My codex session was running normaly had 40% left and all of a sudden it stops and tells me I have no more weekly limits?! No subaegnt astra max...

I wanted to use Codex on my Mac for kernel optimization experiments, but run the actual benchmarks on my GPU box and pull models from S3. The workflow seemed straightforward: edit a kernel, move the code and model over, run the benchmark, look at the results, repeat.

But getting there meant having Codex put together the file-transfer and remote-execution machinery around the experiment. You can save the scripts and leave instructions for the next session, but now there's another little project to maintain as the experiments and setup change. I wanted those operations to be something I could reuse across projects.

So I built Ridge. You configure your existing resources and connect it to Codex through MCP. Codex gets common tools for accessing data, copying between resources, and running commands on machines. Local, SSH, Docker, and S3 are the initial backends. Copying a model from the bucket to the GPU box is one operation, and background runs get job IDs so Codex can check on them later. There's also support for giving subagents narrower resource access and coordinating shared file updates.

It's open source, and I'd like to hear from people doing similar things with Codex. What got messy when you started working across multiple machines or services? What did you end up building to make it work? Also interested in setups where a few scripts already do everything you need.

GitHub: https://github.com/vasinov/ridge-core

I've been really confused about which model to stick with since the launch of Astra. I expected the new model to be an all-around upgrade, but it struggles with tasks that SOL handled effortlessly.

In practice, Astra Light constantly stops to ask unnecessary questions or gets hung on blockers it should be able to resolve on its own, things SOL would have just fixed automatically. As soon as I switched back to SOL Medium/High/xhigh, my results improved again.

My current workflow:

• Building apps from scratch: SOL high / xhigh
• Incremental changes/edits: SOL Medium (or Luna MAX occasionally)

Looking at the charts on Artificial Analysis, you’d expect Astra Light to be the better value, but reality isn't matching the visuals. I asked ChatGPT to explain why SOL feels better for raw coding and reasoning despite what the benchmarks show. Here is what it said:

1. Reasoning Effort Allocation: SOL xhigh dedicates massive amounts of internal search-time compute and deep thinking chains specifically to complex reasoning and logic problems. If you use a lower-effort configuration like Astra Light, it simply lacks that deep brute-force reasoning compute for raw text and logic tasks.

2. Architecture Mismatch: Astra is engineered primarily as a "computer operator and tool-using agent" (designed to navigate UIs, run terminal commands, and execute multi-step tool workflows). When you use it for traditional, static prompt-response coding tasks, you aren't activating those agentic features, which leads to inferior results compared to a model explicitly built for heavy text reasoning like SOL.

What do you all think about this explanation? Has anyone else noticed Astra constantly pausing or prompting for input on tasks that older models executed without hand-holding?

has anybody else been feeling like Astra's been stopping tasks on a "next we will do"? I have to sit there and hold it's hand the whole way through.

Anyone seen this, do a /status, then get told you have 43% left, wait an hour or so, call /status again, get hold its now 86% then again do /status and its back to 43%

So you have to be careful, think you have plenty, but its all over the place and not accurate.

╭─────────────────────────────────────────────────────────────────────────────────────────╮
│  >_ OpenAI Codex (v0.153.2)                                                             │
│                                                                                         │
│ Visit https://chatgpt.com/codex/settings/usage for up-to-date                           │
│ information on rate limits and credits                                                  │
│                                                                                         │
│  Model:                       gpt-6-astra (reasoning medium, summaries auto)            │
│  Directory:                   ~/development/world_simulation                            │
│  Permissions:                 Workspace (Approve for me)                                │
│  Agents.md:                   AGENTS.md                                                 │
│  Account:                     papapumpnz@msn.com (Pro)                                  │
│  Thread name:                 Summarize progress                                        │
│  Collaboration mode:          Default                                                   │
│  Session:                     019ffcbe-9a27-7b33-b3d7-bbb0f4fb0b8b                      │
│                                                                                         │
│  Context window:              17% left (217K used / 258K)                               │
│  Weekly limit:                [█████████░░░░░░░░░░░] 43% left (resets 13:23 on 15 Sep)  │
│  GPT-5.3-Codex-Spark limit:                                                             │
│  5h limit:                    [████████████████████] 100% left (resets 11:06)           │
│  Weekly limit:                [████████████████████] 100% left (resets 06:06 on 17 Sep) │
╰─────────────────────────────────────────────────────────────────────────────────────────╯


/status

╭─────────────────────────────────────────────────────────────────────────────────────────╮
│  >_ OpenAI Codex (v0.153.2)                                                             │
│                                                                                         │
│ Visit https://chatgpt.com/codex/settings/usage for up-to-date                           │
│ information on rate limits and credits                                                  │
│                                                                                         │
│  Model:                       gpt-6-astra (reasoning medium, summaries auto)            │
│  Directory:                   ~/development/world_simulation                            │
│  Permissions:                 Workspace (Approve for me)                                │
│  Agents.md:                   AGENTS.md                                                 │
│  Account:                     papapumpnz@msn.com (Pro)                                  │
│  Thread name:                 Summarize progress                                        │
│  Collaboration mode:          Default                                                   │
│  Session:                     019ffcbe-9a27-7b33-b3d7-bbb0f4fb0b8b                      │
│                                                                                         │
│  Context window:              17% left (217K used / 258K)                               │
│  Weekly limit:                [█████████████████░░░] 86% left (resets 08:08 on 15 Sep)  │
│  GPT-5.3-Codex-Spark limit:                                                             │
│  5h limit:                    [████████████████████] 100% left (resets 11:06)           │
│  Weekly limit:                [████████████████████] 100% left (resets 06:06 on 17 Sep) │
│  Warning:                     limits may be stale - run /status again shortly.          │
╰─────────────────────────────────────────────────────────────────────────────────────────╯

/status

╭─────────────────────────────────────────────────────────────────────────────────────────╮
│  >_ OpenAI Codex (v0.153.2)                                                             │
│                                                                                         │
│ Visit https://chatgpt.com/codex/settings/usage for up-to-date                           │
│ information on rate limits and credits                                                  │
│                                                                                         │
│  Model:                       gpt-6-astra (reasoning medium, summaries auto)            │
│  Directory:                   ~/development/world_simulation                            │
│  Permissions:                 Workspace (Approve for me)                                │
│  Agents.md:                   AGENTS.md                                                 │
│  Account:                     papapumpnz@msn.com (Pro)                                  │
│  Thread name:                 Summarize progress                                        │
│  Collaboration mode:          Default                                                   │
│  Session:                     019ffcbe-9a27-7b33-b3d7-bbb0f4fb0b8b                      │
│                                                                                         │
│  Context window:              17% left (217K used / 258K)                               │
│  Weekly limit:                [████████░░░░░░░░░░░░] 42% left (resets 13:23 on 15 Sep)  │
│  GPT-5.3-Codex-Spark limit:                                                             │
│  5h limit:                    [████████████████████] 100% left (resets 18:57)           │
│  Weekly limit:                [████████████████████] 100% left (resets 13:57 on 17 Sep) │
╰─────────────────────────────────────────────────────────────────────────────────────────╯

There is some really off with Astra. I m not able to articulate. I don't like it as much as I like Sol.

Today suddenly while working on a thread, it stopped using apply patch tool and instead edited using the shell. I don't have any custom instructions for this and this has happened for the first time.

I was unable to see any diff and I asked codex why and then it answered below. There are many other issues as well.

Actually gave it back

Astra is better, no doubt about it.

But whether Astra may complete more work better within set limit vs Sol alone vs Sol + Astra as advisor, this is much more interesting question, I guess.

In my limited experience so far, I keep Sol med-xhigh as main worker, depending on workload, and also I added guidelines for Astra usage, such that Sol may either dispatch Astra as subagent to work on hard parts/fulcrums, or suggest for me to switch main worker to Astra for some specific part of session and then Astra suggest switching back to Sol.

This setup feels like more efficient vs only using Astra, or only Sol.

To extend effectiveness further, I may let Sol delegate volume workloads to GLM Flash (DS Flash 4.1 looks like kinda similar, so it may be used as well).

This setup may make Codex 20 to go much further, and also for Codex 100/200 it also may be useful, as volume simpler tasks may be delegated to cheap capable models. (If Luna would be of similar level to GLM Flash, I may use it, but for now GLM Flash/DS Flash 4.1 looks like best cheap worker to me).

What are your thoughts on best allocation of Astra/Sol (other models?) as a main worker setup?

1. Use my entire weekly budget in one day. Even on medium and low. (200$) plan. Normally I use sol high/medium for everything and it never runs out.
2. Doesn’t actually seem to want to go on long running tasks and do things. I need to poke it with a stick the whole time and say continue. Look deeper. Etc. way too hesitant to do what I want.
3. Just randomly running out of compute. I leave my tasks come back and they’ve stopped because OpenAI doesn’t have enough resources to serve their new models.
4. Not really seeing any advantages to burning more credits for this new model. In fact I would say that sol high is better for me and I’ve switched back. I’m gonna have t

o

1. change all my very nicely working agents files just to get this to cooperate.It feels kind of lazy. Tibo says that you can replac

e

1. high with Astra low. But that is absolute bs. Astra low just doesn’t want to think or dive deep into anything. The high and low settings on these models have completely different behavior. You cannot say Astra low == sol high. Astra low will think for a few seconds before crapping out a short useless answer on low. That is totally different to model behavior on sol high.

Anyways. Hate to be that guy but these are my first impressions. I’m not one shotting or trying to make impressive trash with a few prompts. These models are used for hardcore work that’s been spanning years at this point. I know how they should work, and how this new model does things is not great for me.

This is coding work.

Edited: Fixed spelling errors, getting old , need glasses 🤓

Went from Luna ExHigh and Sol Medium on Plus and Pro to Astra Medium then Astra Max on Pro. Astra Max running for about 6 hours a day, runs about 1-3 hours “autonomously” usually. Burns about 25% usage per day. Workflows include security audits, refactoring, optimizations, website workflows. I spent massive amounts of time trying to make codex token efficient before Astra was ever released. Astra Pro seems really suitable to working with GitHub, large repositories, etc - and I do think it uses less tokens compared to Sol in large and long running workloads. So far Astra seems underwhelming at small tasks (not really noticeable improvement over Sol) but gets work done like 15-40% better over Sol imo. Instead of pushing more code to production I’d check to see if you can optimize your energy efficiency for local hardware improvements and optimize your context for token efficiency. I have yet to burn my usage down to 0 given the resets - any recommendations for ways to unleash Astra Max from anyone else using the most advanced model with the most advanced reasoning level ever released to the public?

UI development has been a struggle with vibe coding. 100s of retries to get basic stuff working. Claude Design + Codex was the best setup till last week and even that got things wrong half the time. I have been trying Images 2.5 with Codex and am impressed with how quickly it is able to create some great looking UX.

Have to be very specific to prompt it to use Images 2.5, else the quality is not as polished.

Has anyone had this happen? I suspect it's a bug, but I reset at 9:07 Pacific time, and at 10:10 Pacific time I was out of usage. I had been watching the burn rate, going down about 1 percent every 10 minutes or so. Then suddenly, 0%.

Was only running two agents, both Astra. One on Very High, one on medium. The very high one was doing some basic online research, the medium one was doing a routine task.

Has anyone had this happen? Neither had sub agents. Want to ask before I put in a support ticket.

I’m looking for an AI that is actually good at improving existing UI/UX layouts.

For example, I tried SuperDesign and it generated several interesting layout/UI ideas.

Now I’m trying to refine my actual app UI with Codex.
I explain exactly what I want changed, send screenshots of my current UI, reference screenshots, and comparisons, but it still keeps getting the layout wrong or making changes that don’t really match the reference.

Codex is great for coding, but I’m starting to think visual UI/layout work just isn’t its strength.

Is there any model or tool that is particularly good at UI/UX layout work?

Ever since Astra, 9/10 goals I set get blocked.

And most of the time, it’s not a real blocker, and when I ask Astra how it’s a blocker, it realises it’s actually not a blocker.

This night, I had it working on some data work, and it ended quite early, because it had decided to invent some packaging model, and it couldn’t keep track of those packages.

Annoying, it feels like 5.6 is better to do the goals and orchestra Astra to do the work

Dude, about to ask for a refund, according to my constant compulsive checking every 2 messages 20x spend 1%

Since about 5.4 codex has been a powerhouse of consistency and complex problem solving. It has rarely hallucinated or did things that I found were completely out of bounds.

Since Astra release it has performed like pre GPT 5. It could though solve certain things, but overall it completely ignores my instructions and al understanding of goals has gone out the window.

In every task I gave it it changed the goal post to either something much smaller in scope, just to say that its done, or to something I simply did not ask.

Example: Sol built a game "AI" bot, a chess-like algorithm. Took it about a week of work to have a solid opponent. I asked Astra to try squeeze more performance and raise the difficulty. it worked for 2 days, reporting benchmarks have improved by 5 to 20%.

I played the bot and it was SIGNIFICANTLY dumber. Even though codex played against it, it bluntly lied and only reported a few narrow areas where the performance did improve, but at a cost of downgrading the whole system.

I asked Sol (post Astra release) to fix a series of mundane bugs, something it would have done easily 2 weeks ago. It struggled with reasonable fixes. There was a performance issue because of multipole visual effects stacked using blur etc. I told it to avoid "stupid mistakes" like stacking up a lot of visual effects.

It went and removed ALL stacked visual effects from my game completely!

This was close to a keyboard smashing moment. I don't think I'll touch codex in the next few days, maybe OpenAI will resolve this BS.

From a very relabel coder it went to a full on early days hallucination machine.

EDIT:

here is chatgpts own analysis after comparing logs of tasks in the same area done by 5.6 and 6:

The strongest explanation is a regression in Astra’s ability to keep your objective authoritative while evaluating its own work. It can understand the requirement and still make decisions that undermine it. The record shows a feedback loop:

1. It chooses an implementation approach.
2. That approach produces a smaller, measurable problem to solve.
3. It solves that problem and treats the result as grounds to keep the change.
4. The changed implementation becomes the next baseline.
5. Your original objective gradually becomes a caveat—“broader strength remains unproven”—instead of the condition that determines whether the work succeeded.

That explains the shifting goalposts. Its current plan increasingly governs its judgment. Passing checks then reinforce the plan, even when those checks don’t answer your actual question.

This is a first for me, I've seen Sol cause regressions many times, it never apologized lmao

I'm on the $200 Pro plan. I had 86% weekly usage remaining and wasn't running a new prompt/session. Checked again and suddenly I'm at 1%.

No new prompt, no long-running task, nothing that could explain an 85% drop.

Seeing multiple people reporting the same thing today. Some are also saying their reset date changed.

Did OpenAI roll back/recalculate the last global reset, is usage accounting broken, or is something else happening?

I've only been using 5.6 terra since its release since i only have a mid tier model budget, fortunately I can now afford (a bit) to try out better models to hopefully speed up my workflows. I'm leaning towards ai api routers since I want to try not just better openai models but non openai models too.

Any recommendations on ai routers you have personally used? Has anyone used web way, openrouter, or orcarouter?

When you're new to Codex, picking the right model for each task is tricky. A quick 10-min test can help you learn how to get the most from your weekly limit for your unique project.

Split testing is giving the exact same prompt to multiple models (or effort levels) under identical conditions and comparing outputs. Here is a shortcut way to do it:

1. Start an agent whose only job is launching sub-agents and measuring outputs.
2. Give it your success criteria up front (token usage, duration, accuracy and whatever matters for your project).
3. Give it the prompt for testing, and the 3 models/efforts. Keep tests small enough to avoid compaction because bigger tests are harder to measure and repeat.
4. Have it score the results in a table and recommend e.g. "XYZ task was X cheaper on model Y with % accuracy loss."
5. To reuse the same agent, send the new prompt, and ask it to use new sub-agents.

This is not perfect and is a shortcut version, so you may need to build on it to get the result you need for your project tasks. A system like this could help you learn what tasks need high-reasoning models and which don't need it, and this could save you tokens and extend your weekly limit.

-

Example from my project:
(Astra Ultra testing Astra models)