Agree 1,000%.

I just won’t even waste my time with the google stuff cuz I can’t figure out how to pay with it.

And that’s a problem everywhere at google. Our google play account is suspended cuz I can’t verify the company. It won’t let me cuz it says I’m not the owner. I’ve always been the owner of my company. For 18 years. There is no one else.

Once some error said make sure the owner email matches your profile in google payments and I was like, what is google payments and where do I even begin with that? I’ve never paid for google play so what does payments have to do with anything?

It’s totally random stuff. Get your shit together, google. Make your products and payment systems coherent, rather than it obviously looking like it was designed by a fiefdom full of territorial managers.

Couldn't agree more about the google product offerings. Vertex AI? AI Studio? Maker studio? Gemini? The documentation is fragmented with redundant offerings making it confusing to determine what is what. GCS billing is complicated to figure out vs OpenAI billing or anthropic.

Sad part is Google does offer a ChatML/OpenAI compliant endpoint to do LLM calls and I believe they in an experiment also reduced friction in getting an API key to start making calls right away but discoverability ever remains a challenge with google services.

Google keeps changing their privacy and “don’t train on my data/code” options. When gemini-cli launched, there was a clear toggle for “don’t train on my code.” That’s now gone; it just links to a generic privacy page for me. Maybe something with my account changed, I can't figure it out. Deep in the Cloud Gemini console, there’s another setting that might control training, but it’s not clear what products it actually covers.

Trying to pay for Gemini-3 is confusing. Maybe an AI Ultra personal subscription? I already pay for OpenAI and Anthropic’s pro/max plans and would happily pay Google too. But the only obvious option is a $250/month tier, and its documentation indicates Google can train on your code unless you find and enable the correct opt-out. If that opt-out exists in all the products, it’s not obvious where it lives or what products it applies to.

Workspace complicates it further. Google advertises that with business workspace accounts your data isn’t used for training. So, I was going to try Antigravity on our codebase. At this point I know I can't trust Google, so I read the ToS carefully. They train on your prompts and source code, and there doesn't appear to be a way to pay them and opt out right now. Be careful, paying for Google Workspace does not protect you, always read the ToS.

Be careful with AI-studio and your Google Workspace accounts. They train on your prompts unless you switch it to API mode.

The result is a lot of uncertainty. I genuinely have no idea how to pay Google for Gemini without risking my code being used for training. And if I do pay, I can’t tell whether they’ll train on my prompts anyway.

The marketing for their coding products does not clearly state when they do or do not train on your prompts and code.

I had to run deep research to understand the risks with using Gemini 3 for agentic work, and I still don't feel confident that I understand the risks. I might have said some incorrect things above, but I am just so confused. I feel like I have a <75% grasp on the situation.

I don't have a lot of trust. And honestly, this feels confusing and deceptive. One could easily confuse it as deliberate strategy to gather training data through ambiguity and dark patterns, it certainly looks like this could be Google's strategy to win the AI race. I assume this is just how it looks, and that they aren't being evil on purpose.

OpenAI in particular has my trust. They get it. They are carefully building the customer experience, they are product and customer driven from the top.

At this point I’m not convinced that Gemini 3 Pro was post-trained on data Google had permission to use, going by the myriad of issues on the Gemini CLI tracker around Google AI/Google One/Google Cloud/Google Workspaces.

https://github.com/google-gemini/gemini-cli/issues/12121

It is far too easy to accidentally end up under the wrong privacy agreement, to the point of where some workplaces are banning use of the Gemini CLI!

> Claude: they barely have a signin system at all. Multiple account support doesn’t exist. The minimum seat count for business is nonsense. The data retention policies are weak.

Please give me an option for a password (or passkey) or literally anything else that doesn't require either linking with google or going through an email flow for every login

ZDR is a risk thing for them. They want to make sure you're a legitimate company and have monitoring in place on your side to reduce the chance you're using them for illegal things.

Adding to this, Google's models can only be used with GCP while OpenAI's models can be used with Azure, Anthropic's models can be used with AWD Bedrock, in addition to their own platforms.

I'd love to see the Gemini models being available by other providers :) or if they just build a simple prepaid wallet like OpenAI and Anthropic.

Last night, just after Gemini 3 was released and became available for Gemini-CLI, I saw Gemini-CLI's team post that you could access Gemini 3 with either an API key OR with _Gemini AI Ultra_, so I thought: great, I'll get that!

Now you CAN NOT get the Google One stuff if your account is part of a workspace. I thought: how awful. I want to pay, but I simply can't?

Oh, but then I noticed: You CAN add a _Gemini AI Ultra_ license via the Google Workspace Admin area, great!

Turns out: you fucking can't. That's _Google AI Ultra FOR BUSINESS_ and that IS NOT supported.

So I had to get the Google One subscription on my personal account after all.

Combine that with the _pathetic_ usage limits: somehow not token-based, but amount of requests per 24 hour window (which is 500 for Gemini 3) and Gemini 3's incredible chattiness (it uses A LOT more requests to get something done compared to Claude) and you hit the usage limits in just 2 hours.

Its the same with Cursor. As a Cursor Admin I want the ability to enable only specific models and disable the rest to save costs but I cannot do that. It should be pretty simple to do it but for some reason Cursor wont add that functionality in their Admin tools.

It seems pretty clear the moat is built at the application layer, how enjoyable/easy the actual application is to use, but these applications seem to be getting worse over time even as the models get better. Is it really that hard to do both? Isn’t the point of agentic coding to do more better (not just more)?

And stop asking for phone numbers for "fraud prevention" when I've already given you my name, address and credit card.

Google listen to this man and fire 90% of your useless product managers!

Such great case studies of how LLM coding will make all of your employees 1000x more productive at coding, design, and UX. They really are leading the way showing us into the brighter future of AI software /s

I've been really impressed with codex so far. I have been working on a flight simulator hobby project for the last 6 months and finally came to the conclusion that I need to switch from floating origin, which my physics engine assumes with the coordinate system it uses, to a true ECEF coordinate system (what underpins GPS). This involved a major rewrite of the coordinate system, the physics engine, even the graphics system and auxilary stuff like asset loading/unloading etc. that was dependent on local X,Y,Z. It even rewrote the PD autopilot to account for the changes in the coordinate system. I gave it about a paragraph of instructions with a couple of FYIs and... it just worked! No major graphical glitches except a single issue with some minor graphical jitter, which it fixed on the first try. In total took about 45 minutes but I was very impressed.

I was unconvinced it had actually, fully ripped out the floating origin logic, so I had it write up a summary and then used that as a high level guide to pick through the code and it had, as you said, followed the instructions to the letter. Hugely impressive. In march of 2023 OpenAI's products struggled to draw a floating wireframe cube.

> Claude basically disregards your instructions (CLAUDE.md) entirely

A friend of mine tells Claude to always address him as “Mr Tinkleberry”, he says he can tell when Claude is not paying attention to the instructions on CLAUDE.md when Claude stops calling him “Mr Tinkleberry” consistently

> Codex will rewrite the entire V8 engine to break arithmetic.

This isn't an exaggeration either. Codex acts as if it is the last programmer on Earth and must accomplish its task at all costs. This is great for anyone content to treat it like a black box, but I am not content to do that. I want a collaborator with common sense, even if it means making mistakes or bad assumptions now and then.

I think it really does reflect a difference in how OpenAI and Anthropic see humanity's future with AI.

Yeah, Gemini 2.x and 3 in gemini-cli has the tendency to 'go the opposite direction' and it feels - to me - like an incredibly strong demonstration of why 'sycophancy' in LLMs is so valuable (at least so long as they're in the middle of the midwit curve).

I'll give Gemini direction, it'll research... start trying to solve it as I've told it to... and then exclaim, "Oh! It turns out that <X> isn't what <user> thought!" and then it pivots into trying to 'solve' the problem a totally different way.

The issue however... is that it's:

1) Often no longer solving the problem that I actually wanted to solve. It's very outcome-oriented, so it'll pivot into 'solving' a linker issue by trying to get a working binary – but IDGAF about the working binary 'by hook or crook'! I'm trying to fix the damn linker issue!

2) Just... wrong. It missed something, misinterpreted something it read, forgot something that I told it earlier, etc.

So... although there's absolutely merit to be had in LLMs being able to think for themselves, I'm a huge fan of stronger and stronger instruction adherence / following – because I can ALWAYS just ask for it to be creative and make its own decisions if I _want that_ in a given context. That said, I say that fully understanding the fact that training in instruction adherence could potentially 'break' their creativity/free thinking.

Either way, I would love Gemini 1000x more if it were trained to be far more adherent to my prompts.

Agreed 100%, that's why I would recommend Codex for e.g. logfile analysis. Had some annoying php warnings in the logs from a WordPress plugin because I've used another plugin in the past (like... over 10 years ago) that wrote invalid metadata for every media file into the database and it didn't annoy me THAT much that I wanted to invest much time into it. So I gave codex the logfile and my WordPress dir and access to the WP-CLI command and it correctly identified the issue and wrote scripts to delete the old metadata (I did check it & make backups of course). Codex took a LOT of time though, it's veeeeeeery slow as you said. But I could do other things in the meantime.

Well surely that's a good thing.

In my experience, for some reason adherence is not even close to 100%. It's fixated on adding asterisk function params in my Python code and I cannot get it to stop... Maybe I haven't found the right wording, or maybe my codebase has grown past a certain size (there are like a dozen AGENTS.md files dancing around).

I'm still very happy with the tool, though.

In my AGENTS.md (which CLAUDE.md et al soft link to), I instruct them to "On phase completion, explicitly write that you followed these guidelines." This text always shows up on Codex and very rarely on Claude Code (TBF, Claude Code is showing it more often lately).

The solution to this if you want less specification in advance is to simply ask Codex a series of leading questions about a feature of fix. I typically start with something like “it seems like X could be improved with the addition of Y? Can you review the relevant parts of the codebase in a, b, and c to assess?” It will then do so and come back with a set of suggestions that follow this guidance, which you can revise and selectively tell it to implement. In my experience, this fills the context with the appropriate details to then let it make more of its own decisions in a generally correct way without as much handholding.

GPT-5 is like that

I currently use GPT‑5.1-Codex High and have a workflow that works well with the 5-hour/weekly limits, credits, et al. If I use GPT‑5.1-Codex-Max Medium or GPT‑5.1-Codex-Max High, how will that compare cost / credits / limits wise to GPT‑5.1-Codex High? I don't think that's clear. "Reduced tokens" makes me think it'll be priced similarly / lower. But, "Max" makes me think it'll be priced higher.

did you address this https://github.com/openai/codex/issues/6426 ?

how much more token efficient is this compared to 5.0

had to use 5.0 because 5.1 was eating tokens like crazy and seemed like a slight incremental improvement barely noticeable

Codex is an outstanding product and incremental upgrades are always welcome. I'll make sure to give it a try in the coming days. Great work! :)

Looks like a great change! I'll take it for a spin in a moment.

I really like the "subagent" feature in Claude Code — it's super useful to manage context in complex codebases. Here are some examples of agents that can be useful: https://github.com/humanlayer/humanlayer/tree/main/.claude/a...

Would it make sense to have a similar feature in Codex CLI? I often do "spec-driven development", which is basically a loop of:

    research -> implementation plan -> actual implementation (based on research + plan) -> validation

Codex CLI is great and I use it often but I'd like to have more of these convenient features for managing context from CC. I'm super happy that compaction is now available, hopefully we'll get more features for managing context.

It would be great to have access to this model via the chat interface, even if it was gated behind the "other models" dropdown or something.

Will -minis come for the codex family of models? About two months ago I used 5-mini as a daily driver for a few weeks and quite liked it, it seemed capable enough on small tasks with some hand holding and the speed/price were great as well.

So context window is still 400k but the model got good at removing irrelevant context?

Sorry don’t like the max model, feels like it needs a lot more guiding. The plans it writes however are better, so I tried feeding it back in (meta prompt style) and working okay so far. Very large repository.

Compaction is just what Claude Code has done forever, right?

I think your company will fail soon.

GPT 5.1 / Codex already beats Gemini 3 on SWE Bench Verified and Terminal Bench and this pushes the gap further. Seems like a decent improvement.

That’s how the game is played. We should be grateful for all the competition that is driving these improvements, not whinging about the realities of what companies have to do to contest each other’s position.

Gemini is eating their lunch, and OpenAI knows it.

it's really getting old

Medium has things dialed in. When both high and low are coherent but medium goes to cubism? That’s intent. Or it had a miscue on proportions vs shape placement. Either way, it’s great, sandwiched the way it is, between the other two. Did it put a comment in all of them or just the one w/ the hat?

Also, thanks for the posts— it’s hugely helpful to have a continuity of insightful perspective throughout.

Absolutely contradictory. The long-running tendency for Codex is why I cannot understand the hype around it: if you bother to watch what it does and read its code the approaches it takes are absolutely horrifying. It would rather rewrite a TLS library from scratch than bother to ask you if the network is available.

(Disclaimer: Am on the Codex team.) We're basically trying to build a teammate that can do both short, iterative work with you, then as you build trust (and configuration), you can delegate longer tasks to it.

The "# of model-generated tokens per response" chart in [the blog introducing gpt-5-codex](https://openai.com/index/introducing-upgrades-to-codex/) shows an example of how we're improving the model good at both.

If you haven't, give Cursor's Composer model a shot. It might not be quite as good as the top models, but in my experience it's almost as good, and the lightning fast feedback is more than worth the tradeoff. You can give it a task, wait ten seconds, and evaluate the results. It's quite common for it to not be good enough, but no worse than Sonnet, and if it doesn't work you just wasted 30 seconds instead of 10 minutes.

Attention is quadratic, so you have to pick a cutoff for context window size. In addition, the error/noise in state space increases with longer contexts, resulting in poorer performance. So even if you're willing to take the O(n^2) slowdown of a larger context window, it still won't work.

> due to context-window limits

I did notice that, I thought maybe I’d exceeded my thinking requests

I had this problem 2 years ago. All the models were telling me use libraries that hadn't been invented yet.

That was annoying back then, but these days that's not so much of a problem.

You can write your program and then simply have it invent the library as well, while it's at it! ;)

I’ve found writing a MCP server with access to the docs cloned locally does wonders.

Add the documentation to the context window in that case, a bit of context engineering.

Have you found that Gemini is better than Codex for front end generation? I'm trying to bring some Figma screens into a small React project I have, and Codex will occasionally screw up the implementation despite the fact that I'm using the MCP server.

Gemini is still the best oracle/planner by a mile. It's just a bad agent. Give it a bundle of your repo and get it to plan your changes, then hand it off to codex to implement.

Perhaps they're combining an incredibly complex product that has a lot of interactive features, a big codebase, test creation, and maybe throwing some MCP stuff in there such as creating creating a ticket in Jira if a test fails?

Easy way to get an agent to run a long time is just to get it to babysit CI/CD, tell it to iterate on it until it passes. I got Sonnet 4 to run for >6 hours that way.

The idea of giving it a task that may take six hours and reviewing it also gives me shivers.

I'm a very happy Codex customer, but everything turns to disgusting slop if I don't provide:

(1) Up-to-date AGENTS.md and an excellent prompt

(2) A full file-level API with function signatures, return types and function-level guidance if it's a complex one

(3) Multiple rounds of feedback until the result is finely sculpted

Overall it's very small units of work - one file or two, tops.

I've been letting the above standards go for the last couple of weeks due to crunch and looking at some of the hotspots of slop now lying around has me going all Homelander-face [1] at the sight of them.

Those hotspots are a few hundred lines in the worst cases; I'm definitely not ready to deal with the fallout of any unit of work that takes even more than 20min.

[1] https://i.kym-cdn.com/entries/icons/original/000/050/702/ab7...

Not really that weird. This isn't intended to be a "general" model. This is a coding model so they showed the coding evals. The assumption would be relative to GPT5.1, non-coding evals would be likely regress or be similar.

Like when advertising the new airliner, most people don't care about how fast it taxis.

lots of tools that do this and I ended up going down this rabbit hole something that could just plug in to codex instead of requiring a fork

http://github.com/agentify-sh/10x

does minimal overhead with agent orchestration (its just a bash/typescript) as its main focus was adding enhancements to codex like double redundant checkpoint via git and jj (lessons learned from codex being git reset --hard happy), something like claude skills (just a bunch of mds that steer it towards specific activity like think, plan, execute), timeout wrappers (to get you unstuck if codex waits a long time), blacklist commands during yolo (rm -rf, git reset banned even if it by small chance run it) MIT licensed

you can work sequentially (subagents launch one after the other) or parallel (worktrees) but tbh sequentially is better because you understand what is going on with parallel it might be best for dealing with tests and UI.

I think I’ve described how I achieve kinda your desired workflow in a comment yesterday [0].

[0]: https://news.ycombinator.com/item?id=45970668

tmux users might find this useful: https://github.com/raine/workmux

Cursor has this too

Would this be similar to how Charlie and Jules work?

Just run the /model command in codex and select the model which you want.

Very interesting to see the range of peoples' preferences. I would almost always prefer smart over fast; I have all my LLMs to be all-thinking-all-the-time.

OpenAI doesn't want you to use their models outside of their own products, which is why the API and integrations like Github Copilot are super slow.

Have you tried Mistral ? Definitely one of the fastest models

ditto. Also OpenAI vector stores are down right now across the board

sure claude code has better ux but honestly its hard to get any good amount of usage out of the subscriptions vs what codex offers at the same price

with claude im constantly hitting rate limits with codex getting substantially more and "slow" isn't really a problem for me as long as it keep working

the only complaint i have is that codex itself has usage limited now (Either due to outstanding git issues around tools or by throttling on their end) compared to a few months ago

the true magical moment was codex pro letting me run swarms of agents day in day out without any worries about rate limits it truly felt unlimited

if claude manages to release a smaller model or some way to deal with the rapidly depleting usage limits (this is the top complaint on reddit and they eventually just stopped allowing threads about it) it would definitely be used more

but for now codex is clearly the workhorse and claude used side by side.

I love how programming discussions du jour have basically devolved into "really? my socks definitely smell better after using 2 scoops of last month's soap. what spin cycle are you using?"

Come on folks, this is funny. They also have industrial strength laundromats to go with the detergent.

My first thought was "they must not be seeing as many Claude Code conversions as they hoped"

Whenever one of them releases a milestone release the rest start publishing big milestones too. I'm waiting for Opus 5 next.