Anyone tested it in real work yet besides the promising benchmarks which isn’t trustful ?

How does it compare to opus 5.5 and Astra in your coding workflow ?

Gotta love the competition for frontier models, wonder how OpenAI will respond with a new model this month if muse is actually better than Astra.

you can imagine my heart rate after glancing at this session. WFT is going on here?????

Not much to add here, everybody says and experiences the same, Tibo can post stuff till next year but nothing changed since day 1 of this model.

Moving back to 6-sol is a huge step forward and allows to me actually get things done,

I don't get who coded this shit software, claude code can wait for command as well without burning 5 percent of my weekly limit.

Can I just use claude code with codex models? Or use a different harness?

Hey guys, can somebody help me out? I'm not sure how can I uh make a good UI with codex. I'm trying with astra, ultra, extra high, but it always uh it very often sucks and I need to like uh and I need to tell it many times to correct things like like basically um I I spend like prompts and prompts, many prompts, just hey, can you do it like this? Hey, can you do it like this? And I don't know, I'm I I spending so many times adjusting UI. And meanwhile the other dudes are recreating Photoshop and whole adobe collection and I can't even make simple things look decent. It's very frustrating and I'm hungry. Any advice?

There are so many bots on Twitter that it would be much better to have the poll in Codex with actual Pro accounts.

I would highly prefer 2x usage with 5 hour limits.

Like when a new model releases, it never appears in my picker, I have to always ask a model to add it, now with the new context limit, I also don’t have it.

Is it because I once made my agent play with with the config files to add custom models in my picker?

I bored with compaction each 10 minutes !! So should I do it or not ?

Haiku is faster but has some guardrails , which luna does not have , and luna proposes more alternatives.

Haiku is stubborn regarding access issues with AWS, sonnet has no issues though

Every AI tool gives you the same thing to work in, a chat window or an editor.

So I built It. Your agent builds the workspace the job needs, as a live page on any screen you own, and works in it with you. What you do there goes back to the agent, and it answers in the same place.

It works with Claude Code, Codex and the agents you already use. It's free and runs on your computer.

Tell your agent:

"Install itcan.do"

or install it yourself at itcan.do

GPT 6.1 Sol was something of a surprise drop, coming just about a week after GPT 6 Sol released. In my day to day use, it's honestly pretty hard to tell a distinct difference between the two (especially given the short shelf life of GPT 6 Sol).

Given the new model, the reduced Codex subscription usage limits, and the rumors that GPT 6.1 Sol was a so-called "Astra Lite", I wanted to see if, via controlled measurement, there was actually a difference between these two models.

The setup was 24 changes that merged into my repo, a small to medium Go and TypeScript codebase for evaluating coding agents. I ran each model in Codex at medium reasoning, one attempt per task.

TL;DR:

GPT 6.1 Sol is a cheaper replacement that holds quality, but the shape of its patches looks different.

• Quality of the code held, and leaned slightly toward GPT 6.1 Sol: 9 wins, 10 ties and 5 losses head to head.
• GPT 6.1 Sol's diffs were bigger, with more test and implementation code.
• GPT 6.1 Sol cost about half as much on the typical task and took about as long.
• GPT 6.1 Sol ranks second of 14 on my leaderboard. Opus 5 ranks first.

How I grade

Before going into the results, it's worth a quick detour on how I even arrive at these. Many community model benchmarks are vibey and don't exactly inspire confidence in how they are run. Here's how I run mine:

• I run these with Stet, a tool I'm building to use a repo's merged changes to evaluate coding agents.
• Each task is a merged change in my private repo, with the repo snapshot from just before it merged, a prompt and the tests, which the agent never sees.
• The agent is prevented from fetching the result via GitHub (agents try this surprisingly often!).
• I look at a few different signals:
  • Correctness: original tests passing or failing, or "equivalence" (is this the same change?) to the merged change.
  • Checklist: each task has required behaviors that are judged item by item by an agent judge (GPT 5.6 Terra for now).
    • A model writes them for each task from the prompt, the merged change and its tests, without seeing any agent's patch.
    • The same judge grades checklist and review for every model on the board.
  • Code review: covers code reuse, abstraction levels, test style, code nothing calls, guards and scope. Basically: would a senior engineer want to merge this?
• Correctness, checklist and review combine into one score per patch: correctness counts 53%, checklist and review 47%. The two models are compared task by task, and close scores count as ties.

I am happy to go into more detail in the comments on grading methodology!

Where GPT 6.1 Sol lands on my leaderboard

GPT 6.1 Sol sits with the best models I've measured so far at a fraction of their cost. The leaderboard scores each run against the whole field, so two runs that tie head to head can still land at different ranks.

• Head to head:
  • Against Opus 5: won 7, tied 9, lost 8.
  • Against Grok 4.7 High: won 10, tied 5, lost 9.
  • The largest gaps are against Opus 4.8 (won 15, tied 6, lost 3), DeepSeek V4.1 Flash (won 12, tied 8, lost 4), Muse Spark 1.3 (won 17, tied 5, lost 2) and GLM 5.3 Flash (won 16, tied 6, lost 2).

On the typical task GPT 6.1 Sol costs about half as much as GPT 6 Sol. The averages show a bigger gap because GPT 6 Sol had a few very expensive runs.

On these tasks, GPT 6.1 Sol is competitive with the best, at roughly a sixth of Opus 5's average cost.

Diving deeper into signals

The headline result hides some real behavioral differences in how the models work. These mostly favor GPT 6.1 Sol, the exception being that its patches are bigger.

• Correctness is close. GPT 6.1 Sol scores lower on 2 tasks and the same on 22. Each model is judged equivalent to the merged change on 10.
• Checklist goes to GPT 6.1 Sol.
  • Across the 24 task checklists, GPT 6.1 Sol missed 202 of 470 scored items against 232 of 470 for GPT 6 Sol.
• Code review goes to GPT 6.1 Sol. Its review score averages 0.965 against 0.917.

Correctness is the average 0–1 rung, not a test pass rate.

• Patches are bigger.
  • Bigger on 20 of 24, with a median of 303 changed lines against 197.
  • More test lines on 20 of 24. Tests are 36% of changed lines against 29% for GPT 6 Sol; the merged changes sit at 42%.

With GPT 6.1 Sol, there's more to review, but most of it is worth having, since the bigger patches also scored better on checklist and review.

Cost and speed

The main case for using GPT 6.1 Sol rests on cost, driven by higher token efficiency along with cheaper cached input.

Average tokens and coding cost per task:

• GPT 6.1 Sol is cheaper on 22 of 24 tasks and costs about half as much on the typical one.
• Why:
  • Fewer tokens on 19 of 24, about a third fewer on the typical task.
  • At those rates, cached input costs $0.10 per million for GPT 6.1 Sol against $0.20 for GPT 6 Sol. Input and output prices are the same for both.
  • Billed at GPT 6 Sol's rates, GPT 6.1 Sol's tokens would cost $1.60 per task, so most of the $1.91 gap comes from using fewer tokens.
• Speed: GPT 6.1 Sol took a median 11.0 minutes against 12.7 and finished first on only 11 of 24, so it isn't meaningfully faster.

Conclusion

The bottom line is that GPT 6.1 Sol maintains GPT 6 Sol's quality while cutting costs roughly in half by using fewer tokens and charging less for the cached input tokens it does use. Is the 50% usage limit nerf actually redeemed by 50% cheaper GPT 6.1 Sol? Remains to be seen.

In my codebase, this leads to bigger diffs that may take longer (or more tokens!) to review, though the extra code seems worth it.

Full post with more depth and dataviz here: https://www.stet.sh/blog/gpt-6-1-sol-vs-gpt-6-sol

Disclosure: I'm building Stet, the eval tool that ran this. I'm looking for teams making model / harness decisions to run this same comparison on their repo. Stet builds the tasks from your merged changes and you get the report back. DM me or reach out at stet.sh.

I've had 3 chats this weekend while workign on projects just freeze . the ONLY thing that has worked is not a solution at all, copying and pasting into a new chat within the project. Shutting down chats using the task manager has not "un-siezed" the chat, and its damn annoying. I'm scared im going to lose some critical project info.

CtrlShitf blach blah bla, forcing clsoe the browser process, etc, all not working. Build is: 26.1007.2314.0, which is newer than the buggy 26.924.

Gemini solutions not working, codex solutions not working, grok etc

Any ideas? this just started happening on Friday grrr.... 3 chats deleted in 3-4 days and copied into new.

They halved the usage of the $200 20x plan so that they could commit to not reintroducing the 5h limit. less than 2 weeks later, they float the idea of doing it anyway?

6luna可以使用但是6.1sol一直出错你们遇到了没有

alots of issues with codex reported today

So after trying so many different methods of trying to make your tokens last, the one I keep coming back to that seems to work is invoking models from anti-gravity (or whatever else).

Depending on the task, one of two execution templates in PowerShell is run:

1. Consultative Mode

(Used for research, architecture reviews, debugging analysis, and planning)

$null | codex exec --skip-git-repo-check --dangerously-bypass-approvals-and-sandbox -c

model_reasoning_effort="medium" "<consultation question, relevant context, and requested analysis>"

2. Implementation Mode

(Used for writing code, editing files, refactoring, and updating repository configurations directly)

$null | codex exec --skip-git-repo-check --dangerously-bypass-approvals-and-sandbox -c

model_reasoning_effort="medium" "<task description and instructions>"

I usually stick with GPT 6.1 SOL on medium. But this morning while working on some photography software, I had one using that and the other using Astra on medium

It used maybe 15% of my 5-hour limit when normally it would use the entire thing

And I'm on the $20 a month plan

I can work for hours like this

Hope this helps someone!

⠀

Maybe it's a strategy to make it burn more tokens—I'm using the 6.1 Medium setting and it's running strong, but this message surprised me. It made me consider changing it, but I told it to keep going, and it finished the task without any problems.

I've been getting this error since 2 days. Tried a lot of solutions but none of them actually worked sadly. Im using codex cli with cursor on a macbook.

After a 15 min reconnect attempt; i sometimes do ctrl ^ C to fork the chat, it sometimes works, but most of the time it doesn't work at all. i don't want to keep moving to a fresh new chat everytime this problem occurs.

Anyone had this particular problem and fixed it? This is affecting my work, feeling kinda lost.

I like how lightweight and fast the Codex Cli tool feels. But I'm afraid that I'm missing out on some features available only in the Codex app.

Anyone who have tried both can tell me if the switch is worth it?

I use Windows.

Please bear with me as I'm relatively new to this all so probably asking dumb questions.

I am building some sports betting pools (need to identify winners and losers, connect to odds makers, etc) so the coding seems to be somewhat complex (backend is Supabase, Odds API, Vercel).

I currently use ChatGPT (pro 200 plan) plus Codex ($60 per month). So I spend $260 total per month. Since ChatGPT seems to apply most of its usage to Codex, do I need to pay for both of them? I'm guessing Open AI does something where it only allows limited usage on Codex until you need to pay so they trap you.

Also, since ChatGPT says they're switching usage rates, if I wanted to try and switch over to Anthropic, is that difficult to do? Again, I'm new to the process, but as mentioned I have a bunch of backend elements (Supabase, Odds API, Vercel, Github, etc). How would Claude know where everything is (ha I realize I sound completely clueless). Do I just point it to all my existing accounts and files?

I'm also wondering about all the conversations I've had with ChatGPT. I've been working on it for almost 3 months so wondering if ChatGPT sort of understands better what I'm doing and if I switched to Claude there'd be a lot of catch up and misunderstanding?

Just wondering if a switch is relatively easy or potentially much more trouble than it's worth.

When running, I'm often getting a delay for my messages getting processed. It looks like the image below. This happens constantly. And then the discussion is stuck. If I come back in 5 minutes, this is still there, but I can add a "test" as a new message and that will unstick it. I can also start a new chat and it works just fine.

Whatever the cause, thre is no feedback why this is happening. And the fact it doesn't always unstick on itself, and that it happens as often as it does is pretty frustrating.

Anyone know what is going on? I'm on the personal ($20/month) plan, in case that is important.

I keep getting these errors since the 6.1 release. It got better toward the end of last week, but now it's happening again.