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

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.

The new Terra 6.1 model has appeared.

I have to say this community is entitled as fuck. There's no pleasing you guys and everyday is the same boring recycled unoriginal post about usage by vibe coders with skill issues just using Astra Ultrafast for every single div-centering maneuver. Git gud you noobs.

Terra is built for computer use and I'm all for it. Not everything has to be Opus 5.5 level. I've had a temp-Pro sub over the weekend for a short hackathon and I could hardly burn tokens fast enough even on Astra.

How many Spider-Man city games do you really need?

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.

Am I the only one who thinks this is actually a big deal?

Forget about speed for a second. If I'm getting anywhere near Astra-level performance for a tiny fraction of the price, that's huge.

I can throw a bunch of tasks at it, have it review work, check things I might have missed, and let it run overnight. I don't really care if it takes 8 hours instead of 20 minutes if the cost difference is big enough.

I probably wouldn't use it for everything, especially when I need something done right away.

But making high-level reasoning this cheap opens up a lot more possibilities

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.

Tibo will not save us.

Hey everyone,

I need to vent and see if anyone else is experiencing severe model laziness with Codex Terra 6.1 . Honestly, it feels completely unusable for complex architecture refactoring when compared to Sol 6.1.

Just to be clear: I'm not running this locally. This was run directly through the server UI, where it has full access to the project context.

I've been trying to get it to implement a "FULL Automatic IoT" routine involving a hybrid architecture (Rust, Kotlin, and a central VPS). Instead of actually writing the code, the model completely gave up, wrote a markdown file documenting why it's hard, and left me this log:

"Implementation not finished. The FULL route still cannot order Kotlin to trigger the alarm: the existing mechanism requires an event capability that is only generated in LIGHT.
To complete it, we need to connect the authenticated VPS → Kotlin order, validate the event and the switch in the box, and deduplicate execution before calling the saved URL. It wouldn't be safe to mark it as ready without that connection and its testing.
In this turn I reviewed the code; I made no changes, builds, or deployments... No code modified, compiled, or tests run."

The Issue:
It diagnosed the bottleneck perfectly (the event capability being locked behind the LIGHT route) but instead of refactoring the Kotlin/Rust code to bridge the gap, handle the VPS authentication payloads, or write the deduplication logic... it just wrote a status report and called it a day. It basically told me: "Hey, I see the problem, I'm not going to fix it, see ya." It didn't modify a single line of code.

When I run this exact same codebase and prompt through Sol 6.1, it actually maps out the reasoning tree, handles the token validation, writes the deduplication logic, and gives me functional blocks of code to deploy.

Has anyone else noticed Codex Terra behaving like a passive code reviewer rather than an actual coding assistant lately on the official servers? It feels like they heavily nerfed its output tokens or jacked up the system constraints to save compute.

openai finally launched GPT 6.1 Terra

GPT-6.1 Terra: delivering fast and capable computer use at a lower price.

GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it.

rolling out now and is available to all Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex.

Developers can also access it through the OpenAI API as gpt-6.1-terra.

anyone tried it in a planner/executor workflow with something like Sol high or xhigh for planning and Terra high for execution?

isn't this supposed to be a lighter model? How many more times are they going to tweak the 6 Sol model architecture instead of making something good? 😭

I've been using OAI and building with AI almost 2 years now.
And I've had a Pro account almost a year.

Ever since 6.1 models, and especially in the last few weeks, OAI has been so slow that I think I'm done entirely. What took 5-15 mins on GPT 5.5 back in April now takes HOURS, and I mean 3-7+ hours to complete the same thing.

It's so slow I've had to leave it overnight to process basic things, then it hits a wall and stops with 'model at capacity' and then I'm even further behind.

I was already turned away by their -50% usage, but what's annoying me even more now is how slow it is. It's so painfully slow I'm just endlessly scrolling Reddit waiting for it to do the most basic of things.

I've loved Codex and been an OAI fanboy vs. most others I know using Claude (which I also use), but at this point it's not even about the intelligence or usage. It's so slow even on fast I can't complete the work I need to and I've been getting more and more behind vs. the pace I've been used to with AI.

Combine that with it's over engineering, over-testing, not listening, poor cycles, and all the usage stuff and competitors or even local LLMs are looking more attractive by the day.

I tested GPT-6.1 Terra in ChatGPT Work today, and honestly, it was one of the strangest experiences I've had with an OpenAI model in a long time.

I'm a fairly experienced user. I regularly work with Codex, ChatGPT Work, and different reasoning models on large writing and document projects. I've successfully completed much more complex tasks using previous models, including GPT-6 and Sol 6.1.

This time, however, I couldn't get Terra to consistently follow even basic instructions.

Here's what happened:

I asked Work to perform a detailed literary evaluation of a novel, reading it chapter by chapter, recording its findings, and delivering a comprehensive assessment at the end.

Nothing particularly unusual for an agentic workflow.

When it seemed to struggle with the task, I even suggested a simpler approach myself:

• Read one chapter at a time.
• Write down the analysis and conclusions in a working document.
• Continue through the remaining chapters.
• Combine everything into a final assessment.
• Don't interrupt me with progress reports. Just deliver the completed work at the end.

That last instruction became a recurring problem.

Despite explicitly repeating it around five times, Terra kept interrupting with messages along the lines of "I've completed chapters 1–5."

Each time, it appeared to acknowledge the correction, only to repeat the same behavior.

What made this particularly strange was that we never really got far enough to evaluate the quality of its literary analysis. The problem was much more fundamental: it seemingly couldn't maintain simple procedural instructions across the task.

I initially used Medium reasoning to test efficiency and token consumption. Eventually, I switched to the maximum reasoning level, hoping it would improve instruction-following.

It didn't noticeably help, and my usage quota took a hit in the process.

For context, I've used Work before without encountering anything remotely this frustrating. I don't remember experiencing this degree of difficulty with basic instructions since the GPT-4o era.

I'm not claiming Terra 6.1 is fundamentally a bad model. It was released today, and I suspect this could be an early rollout issue, possibly involving the interaction between Terra and Work's agent orchestration.

I also can't rule out something specific to my session.

Still, I thought it was worth documenting, especially because instruction-following reliability is critical for autonomous workflows. A model can be extremely capable at reasoning or coding, but if the user has to constantly supervise and repeat elementary instructions, that becomes a serious usability issue.

I'm documenting this as a potential launch-day regression in instruction-following reliability, particularly when using Terra 6.1 within ChatGPT Work. Hopefully, this report provides useful information for investigating possible issues with the initial rollout.

Has anyone else experienced unusually poor instruction-following with Terra 6.1 today, especially in Work?

I'm particularly curious whether others have noticed similar behavior across different reasoning levels, or whether this seems isolated to certain sessions or workflows.

I am officially giving up on following codex reset. My opinion is contradicting with lot of users here, but it is what it is. I use 5x subscription of codex, and I have been burning tokens like anything using sol-6.1. It became my goto option after fable. With more than 11 billion tokens burned,

Migrated a 3 million lines of monorepo from TS 5.9 to 7

Upgraded storybooks

Upgraded nx

Upgraded nextjs

Upgraded nestjs

Tokens keep coming back before I burn. I am shutting down my laptop today and getting some sleep.

I developed Neonbin, a platform where AI agent users can share resources, organize them around specific tasks, and access them directly from agent conversations through MCP. The goal is to make useful content and the knowledge of how to use it easier to discover, combine, and reuse, helping the community build on existing work.

How it works:
1. Resources: content for reference and derivative creation
Users can share, browse, search, and use resources for free. Supported formats include Markdown, PDF, images, and video. Codebases, 3D files, audio, and other content can be packaged and uploaded as ZIP archives.

1. Bins: resources and guidance organized around a purpose
   A bin can reference resources shared by different users. Its creator can add a contents guide and AI instructions explaining the resources’ purposes, relationships, and requirements for use.

For example, a bin for interactive web development could include UI reference images, motion reference videos, component code, and a skill file or design specification. Other users can use the collection or recombine its resources into bins suited to their own tasks.

Using a bin in an agent conversation
After connecting Neonbin’s MCP service, you can ask Codex to use a particular bin:
“Use Neonbin bin [ID] to build a mobile-friendly interactive web app, following its design references and implementation requirements.”

Codex can retrieve the bin’s description, AI instructions, and resource list, then read the files needed for the task. Retrieving a bin does not automatically load every file into context. You can also specify individual resources or ask it to search for relevant bins.
The same workflow is available in other compatible AI applications. Clients need to support the MCP connection and CIMD-based OAuth authorization. The types of content an agent can read and process depend on its available capabilities.

Content use and availability
Creators retain ownership and copyright. Under Neonbin’s content usage rules, users can reference resources and build on them to create new work for personal or commercial use. This does not grant rights to redistribute, resell, or use someone else’s original uploads as standalone assets outside Neonbin.

Bins can be public or private. Uploaded resources are all public; making a bin private keeps the collection private, not the underlying files.

The platform is open to anyone working with Codex and other AI agents, across any field or workflow. Software development, games, images, video, and 3D are just a few examples. You’re welcome to share resources, curate bins, and use them for your own tasks. I also welcome feedback on resource organization, content reuse, and agent integration.

Website: https://neonbin.com/

I used to feel sad and frustrated almost all the time and demotivated almost all of these days with 6.1 Sol because its too slow. It will be 30 Tokens/second even on fast mode. What is the use if we have a fast model that is less intelligent or a slow model that is super fast ? This is where OpenAI is struggling and hopefully Terra 6.1 is the answer.

My struggles with OpenAI subs:

1. I really have never used Astra because of its insane pricing. It drains all your subs in a half day.
2. To handle the complaints for pricing - OpenAI had to release 6.1 Sol at fraction of Astra pricing. But its slow like a snail. As much as I love GPT 6.1 Sol for its value for money and intelligence on par with Astra, its super super slow. It really bothers me that I could see only 30 Tokens/second even on fast-mode.
3. To handle the speed complaints, they had to lauch the Terra 6.1 now. If it is as what they say about speed, intelligence and pricing balance - I would love the launch of Terra 6.1 more than anything now and this is what OpenAI needs too. I wish its 80-100 Tokens/second on fast-mode and I am happy with it.

If Terra 6.1 is what it is and stays consistent with 100 Tokens/second - It will be daily driver for $20 till $200 subs.

Right now its fluctuating between 50 Tokens/second to 90 Tokens/second - It still not great. It must be consistent 100 Tokens/second to be a daily driver.

All the people who mock and make noise for OpenAI for Opus 5.5 challenger or Astra 6.1 model launch are just rich people with Ultrafast subs and with lots of dollars to burn even on API credits

I only care about speed + intelligence + pricing (usage) and would want Terra 6.1 to win on this.

He sort of warned us about the weekend work. I should have been going through my credits Saturday, but I took a break. He announced a reset, and I figured it would land at 10 am PST today, so I put Dot to work and went to bed.

Last night:

weekly remaining rose from 81% at 10:41 p.m. to 98% at 11:10 p.m. PDT last night. It then fell to 17% by 12:14 a.m., and coding hit the usage limit around 12:34 a.m. doh! Hoping for another reset today!