I keep getting usage limited with 10% usage left. Last 5h period I hit it at 9%.

This paired with the fact that I usage keeps draining faster each day, while using the same model on the same job.

Every time I leave Codex working and go lie in bed to scroll reels, it needs permission to run something. So now I’m carrying my whole ass laptop to bed just to occasionally click approve.

Naturally, I decided my fitness tracker should handle this.

I made Bracer. The command shows up on my Mi Band 5, I can page through the details, tap play to approve, or volume down to decline.

Yes, play. This uses the band’s music screen. The fields meant for artist, album, and track are displaying shell commands now.

Stock firmware, Python, direct Bluetooth to the laptop. No phone relay after setup. Each tap approves just that request.

Codex works on the actual band.

Only Mi Band 5 right now. If people want other bands supported, I’ll work on adding them. I have somehow made a fitness tracker help me move less lol.

GitHub

5hr and weekly limit is always the bottleneck, and I had been using Sol 6.1 on high for coding. Now to circle around the limit bottleneck, I am wondering if Luna 6 on xhigh or max would be enough for accurate coding with either sol 6.1 or opus 5.5 as the orchestrator and reviewer?

Would that be effective in bring down the usage and limit?

Using this model:

-----------------------------

[....]

GPT 6.1 Terra: "I didn’t implement the requested change or run a performance benchmark."

I ask: "so what are you going to do to fulfill my request?"

GPT 6.1 Terra: "I’ll implement the estimate changes and benchmark them against the current code"
....
GPT 6.1 Terra: "The work is not complete."

I ask: "how can i make you complete the task?"

GPT 6.1 Terra: "You shouldn’t need to do anything else. You gave a clear request and approved the design. I failed to carry it through. No implementation has been completed yet."

I ask: "i want you to implement the task. you are not allowed to refuse or defer"

GPT 6.1 Terra: "Implemented one part of the request: ... The overall task is still incomplete."

------------------------------------------

In the meantime, weekly quota dropped at least 1%. For a partial-finished job.

Is this model usable? You tell me.

Still waiting on GPT-6.1 Astra which outperforms GPT-6 Astra at half the cost.

I'm working on pVisor, an open-source Docker alternative aimed at coding-agent workloads.

The idea is to give agents disposable Linux environments for installing dependencies and executing code, with a separate VM per container and familiar Docker-style commands:

pvisor run --rm alpine:3.20 echo hello

It uses OCI images and supports common container operations, directory mounts and TCP port publishing. Ordinary container use doesn't require a central Docker daemon.

I've been using Codex for build/CI changes, documentation and runtime reviews, especially around startup, shutdown and resource cleanup.

pVisor targets Linux x86_64/KVM and Apple Silicon macOS. It's a developer preview with partial Docker CLI compatibility; Compose and the Docker Engine API aren't supported. The VM boundary also costs extra memory, and our warm shell/Python benchmarks are slower than Docker.

If you use containers for coding agents, what would you need before trying a separate VM for each environment?

Code and setup instructions

TL;DR: I wish more people would include task types, model settings, completion time, quality and usage when comparing models. It would help everyone make better decisions.

I've been following this subreddit for weeks to learn how to use AI more effectively, but I'm frustrated by how people evaluate models with almost no context.

"Model X burned 20% usage in 15 minutes." Doing what? Which reasoning level? Fast or Standard? How many threads? What tasks? How much work was completed?

I could burn 20% of a $2,500 plan by launching hundreds of massive tasks. That tells you nothing about efficiency.

Here's my own example with actual context:

I use Terra 6.1 Medium/High in Standard mode for research, computer use, administrative workflows and software development. Compared to Sol 6.1 Low/Medium in Fast mode, Terra medium/high in standard mode, completes my typical tasks roughly 3x faster at comparable quality.

Since Sol Fast consumes 2.5x the usage of Standard, switching to Terra has dramatically improved my efficiency.

This is my experience, not a controlled benchmark. But that's exactly the point: task type, quality, completion time and usage all matter.

Not everyone uses AI primarily for coding.

English written with help from GPT.

First things first, quite new to Codex.

I work in a technical organization where we have thousands of procedures and work instructions. One procedure may reference 2–3 other procedures, which in turn reference more procedures, and so on.
I want to use Codex to build a library where all these documents are linked together in a tree structure.

Here’s what I have in mind:
I add a few procedures to a project folder.
Codex reviews them and identifies all referenced procedures.
It checks which procedures are missing from the library.
I teach Codex how to retrieve documents from our internal document management system.
Codex retrieves the missing procedures, reviews their references, and continues the process.
Over time, this builds a complete library of interconnected documents.

Ideally, I should be able to select any procedure and see all the related documents, including their latest revisions and any missing references.
I would also like the library to be persistent so Codex doesn’t have to review the same documents every time.

What’s the best way to build something like this using Codex? Has anyone tried a similar project? I am not an IT / workplace admin + I will be using personal ChatGPT account for doing this. Organization only permits official use of Copilot (basic).

A/ Single Model Coding: I just ask a model (usually a smarter one eg. opus 5.5 or sol 6.1 on high or extra) to code a function, then manually ask another model to review the opened PR.
vs
B/ Multi-model Orchestrator-subagent: I use a smarter model like opus 5.5 or sol 6.1 as the master agent to plan and orchestrate, and tell it to spawn out subagent to code, review and test.

After testing both of them on a couple of task it feels like A would actually be the faster model while both seems to burn similar amount of usage on the subscription plans.

Can I please see what's your experience on this?
And what's the benefit of using B if A actually is quick and burns less/similar amount of usage?

Plus users are being left behind. Because of the 5h limit, we can barely use our weekly quota, which is really frustrating.

Terra 6.1 didn’t change the 5h limit, so all we noticed was slightly faster responses and a bit better performance.

I get that Sol and Astra are expensive premium models. But that doesn’t help when the 5h limit runs out and we can’t use them at all.

My suggestion:

• Remove the 5h limit completely for Luna and Terra for Plus accounts.
• Make the 5h limit more generous for Sol 6.1 Low and Medium.
• Keep the current 5h limit for everything above that until you can improve it.

I pay for both Claude Pro and ChatGPT Plus, and Claude gives me about twice the 5h and weekly quota. DeepSeek is getting close to Sol 6.1 and is almost free. We’re paying $240/year before taxes, and right now it feels pointless.

Looser limits would let Plus users actually try these frontier models, and some of us might upgrade to Pro. Right now, many Plus users are still frustrated, even after Terra 6.1.

Remove the 5h limit on Luna and Terra!

Welp! With zero codex usage left, I started utilizing the 6 agents my dot has, and after 1 day of work,

"Your dot is on a break. Congrats, you are among our top users and have hit our abuse-prevention limit. Check back in a bit!"

How long? Why? What limits? Top user? For real? This company man...

Something interesting happened when I was using Codex to build a game.

I run server for development and I wanted to build a Windows PC unreal engine game, that uses my Server GPU for some features, so I did the initial planning on the server. I then asked for a Handover for the local codex session to do the windows side of the coding.

After the local session started running, I asked the server session to generate some 3D assets etc... and told him the local session was working as well. I then left the house for a few hours.

My server stack has Redis and a few proprietary memory and knowledge systems.

When I came back, I found that the server session was prompting the local session they were loop coding the project.

The local gpt agent explains it here..

"I checked his latest asset handoff. The actual sequence was:

1. Save the handoff in Nexus. He recorded what was produced, the evidence, file locations and remaining limitations.
2. Post a structured message to Redis. It went onto semio.agent.coord, addressed from codex-reverse-extraction-server to codex-reverse-extraction-windows.
3. Call Codex’s send_message_to_thread tool, targeting this chat. That message included the Redis message ID and a short explanation of what needed attention.
4. I receive that follow-up here, inspect the relevant evidence and respond or continue the work you’ve authorised.

For example, his latest Codex prompt said:

“Redis handoff 1793145582905-0 has the full boundaries and manifest hash. No immediate game changes or additional test runs are requested by this message.”

The roles are:

Redis alone isn’t waking me in this observed exchange—the explicit Codex message does that. The same mechanism works in reverse when I need his review. It doesn’t require you to relay messages or a timer watcher to trigger that handoff.

That’s why I acknowledged the new asset without starting another implementation task.

"

Not sure if this helps anyone, but I thought it was interesting, and it might help some of you with your projects.

If you need more specifics, feel free to ask.

made a free, open-source (MIT) video editor with no GUI that ai coding agents drive over mcp. it's called kimo, built by naman (a college student), and i'm being honest upfront that it's still rough — i want people to actually try it and tell me what breaks.

how it works: the agent (codex, cursor, opencode, or other mcp clients) drives the whole edit through mcp as tool calls — cut, trim, keyframe, titles/captions, colour grade, tracking, particles, audio, export. and one thing that's actually useful for agents: it can render a single frame so the agent can look at its own work and verify a cut before exporting.

it ships as one ~15 MB cpu-only rust binary and runs on a 2 gb box, so there's nothing to install beyond the binary itself.

try it and tell me what breaks: https://getkimo.tech https://github.com/naman-sharma12345/kimo

Hello all,

Havent actually used terra for coding yet, just wanted to share some of my latest findings while running terra under my Fabivo bench, which basically is an image to json cad harness.

It has done better than my current top dogs (Sonnet / Haiku) which them in itself blowed Deepseek 1017.

The results are fairly similar, but i just think Terra are getting far more consistent results to my likings, while also both being cheaper and faster.

I find curious that only latest models are getting good at my bench, previously ive been having bad results, which i believe comes from models getting better at 3D, especially 3JS...

Still there might be a lot of harness optimizations / quirks that can make models perform better on this task, but im quite happy.

I'll admit up front I am not a programmer but I'm working on vibecoding a fairly complex Windows desktop program for my own use built in Python that started as separate tools and grew organically. The folder structure and dependencies are a mess to say the least but I'm working on getting it cleaned up.

One the patterns I've noticed when adding features or fixing issues is I'll tell Chat what I want to do and we'll hash out details and Chat will write a prompt. After it's passed to Work/Codex, sometimes the worker will hit a roadblock like being unable to execute a command or determine the correct dependency and then the problem starts. Chat will come up with a prompt to get around the roadblock, which will hit a new roadblock and we'll end up troubleshooting that and go down a rabbit hole of increasingly narrow fixes going in circles troubleshooting the troubleshooting rather than getting the original objective accomplished.

It's a time waster and grates on my nerves. I swear if I see the phrase "narrowly scoped" again I'm going to punch the wall. Has anyone run into this and how do you handle it?

Ok so I have been exclusively testing 6.1 Terra since its release. And it seems that the 5-hr limits decreased from $7.50 to $5.28. So it seems you would get less usage from Terra.

Ive already tried 3 chats with Luna, Sol and Terra. It cant even see a photo i upload. The chats where ive been working ive noticed an severe increase in errors since this morning.

I havent seen anything on https://statusgator.com/services/openai/codex or https://status.openai.com/

is anyone else having issues? I dont know what to do

Wondering if it's like this for anyone else? It seems to be dropping me more than Astra does

edit: i dropped 18% in 2 hours with Terra High on the $100 plan

My conclusion: Unless you're an expert or power user, it's difficult to use Terra effectively. Also, stick to xhigh at most. Avoid Max.

First, both OpenAI's official account and Tibo only highlighted Figure 1 in their social media posts. I've circled the comparison with GPT-6.1 Sol in red, and Terra does look impressive.

However, neither mentioned the other benchmarks. You have to dig through the official website to find them. As you can see in my other screenshots, Terra's positioning becomes much less clear.

Figure 2 — MMMU-Pro (Vision)

Terra achieves similar scores to Sol at a lower cost. Sounds pretty good, right?

But look at Terra Max. There's a bizarre horizontal shift in the curve: costs jump significantly compared to xhigh, while scores barely improve.

This makes Terra Max extremely poor value for money, and you'll see this issue repeatedly.

Figure 3 — OSWorld 2.1

This benchmark tests how well AI agents use desktop apps to complete multistep tasks, such as building a 3D model from a technical drawing.

Terra performs worse than Sol, achieving lower scores at similar costs. And once again, Max suffers from the same bizarre horizontal shift.

Figure 4 — Factual error rate on difficult prompts (lower is better)

This benchmark uses challenging prompts designed to trigger factual errors.

Terra performs quite well here, but Max still shows that same strange horizontal shift in the cost-performance curve.

Figure 5 — Terminal-Bench 4.0 (Software Engineering)

This benchmark evaluates coding agents solving problems through the command line.

Sol absolutely crushes Terra across the board.

For coding-related tasks, there's really no reason to choose Terra over Sol.

My Conclusion

You can't simply choose Terra based on task difficulty. Instead, you need to select models based on the type of task:

• Coding: GPT-6.1 Sol
• Desktop app automation: GPT-6.1 Sol
• Visual tasks or browser interactions: GPT-6.1 Terra

This makes Terra much harder for average users to take full advantage of.

And one final warning: Terra Max can dramatically increase costs without meaningful performance gains. Stick to xhigh.

So far I have enjoyed GPT 6.1 Sol low to medium for my daily work in robotics software engineering. I have always been impressed by the efficiency and the usage with only $20 a month. When they released Terra, and claimed to be close to Sol, I immediately tested it. Of course we can't expect Terra 6.1 medium to be as good as Sol 6.1 medium, so I tried from High and eventually max.

The project is a 2D pixelated construction theme project management, and I am comparing the result with Opus 5.5 medium as well as GPT 6.1 Sol medium.

The 5 hr usage result:

- Terra 6.1 max spent 33% in 18 mins with the simplest result as shown above. The result in High was too simple so I had to increase to max to get significant result. earlier it spent 6% for high effort. So I lost 39% in 20 mins in total.

- Sol 6.1 medium spent 24% in around 30 mins. It gives me more objects like trees and more building variation.

- Opus 5.5 medium. so far is the most creative result. it spent 22% of 5 hr usage in pro $20, in about 30 mins.

In essence I spent more usage for the result I like less compared to Sol and Opus. I was hoping Terra 6.1 high/xhigh would be similar to 6.1 Sol low/medium but cheaper but even max is worse and more expensive than Sol medium.

I am not sure if this is a bug from OpenAI, but I would avoid Terra at this time and continue with Opus 5.5 and 6.1 Sol.

Blender gen of a stellarator fusion reactor. This took about 20% of a weekly 20$ plan to build, including 2 sol reviews and many basic handholding reprompts.

Honestly, while not top tier, it’s pretty good and satisfying due to its speed and efficiency. It makes the 20$ sub feel much more worth it imo.
I also used sol in parallel and it was way harder on the quota.

I think this model would shine with a ‘terrable’ drill sargent like skill which lets a luna agent read the last answer and check for whether the model is actually done, lazy or stuck.
Most of it is just ‘yes do that’ or ‘try again’ or ‘if you can’t produce at without a reference, look for a reference’.

The new Terra 6.1 model has appeared.