I would love to see all the big players put 1% of the effort they put into model training into making the basic process of paying and signing in suck less.
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.
OpenAI: Make ZDR a thing you can use or buy without talking to sales, already. And for those using containers or a remote system or really anything other than local development with the codex CLI, you really really need to fix this bug. I bet Codex could do at least the client part for you!
(Hint: Claude Code gets this right by default, despite the fact that everything else about Claude sign-in is a joke.)
Google: get all your B2B AI product managers in one room and tell them that they need to make one single product menu on one single webpage with all the pricing on that page and that the Google Cloud people are not permitted to make anything that isn’t actually logically Google Cloud depend on Google Cloud Billing. Your product cannot compete with OpenAI or Anthropic if people need to ask an LLM to figure out what your product is and if your own fancy LLMs can’t give a straight answer. My company pays for a non-Google product primarily because it’s too complicated to pay for the Google product! Right now, trying to use Google’s AI is like trying to ride Bay Area public transit before the Clipper Card.
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.
The "Owner" accounts in Google Play and Apple's App Store are so freaking annoying. The only time they make sense is for solo-founders and even then I've had issues. Now expand it to working at a larger company and it's a joke, a bad one. Oh sure, I'll just get the CEO (or other higher-up) to login and accept new agreements, that will be easy. Even more fun when you tell a client (who logged in exactly 1 time to set up the account) that they need to use a generic email (not a personal one or an employee-specific one), the ignore your suggestion, and then they can't get back in because the person who set up the account left the company. It's a mess.
Also, re "Google Payments", I tried to transfer an app from my personal/solo Google Play account to a new business one I set up for my LLC and it was like pulling teeth. They wanted me to find some payment id from the original $20 purchase I made to get access to Google Play, something I did right around when they first launched and while I still have/use the same email, Google came out with approximately 1 googol different "payment solutions" in the interim and their engineers don't care about data migrations. Finally, after many support emails, they just transferred it without me giving that code which just shows how silly the whole thing was from the start.
I don’t have experience in big tech but in the few SaaS companies I’ve seen the issue is UX designers and Product managers overwhelmingly have a B2C culture.
Can relate. My inactive google ads account all of a sudden got banned. No explanation except some generic link to their terms of service. Appealed, got automatic denial, no reason given. Have retried multiple times, same result
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.
I've just found myself using OpenRouter if we need Google models for a project, it's worth the extra 5% just not to have to deal with the utter disaster that is their product offering.
FWIW I had to bail on the same thing because my results were drastically different. There was something happening with images through open router. Although outside of that I’d absolutely do the same thing, their apis are awful and billing worse. Maybe it makes sense for huge orgs but it’s a nightmare on the smaller scale.
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.
> 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.
Didn't realize these stipulations for the models. Looking at devops-y job descriptions the last few months I noticed nearly everyone has some kind of Azure requirement now (which I've mostly avoided because I don't want to end up managing someone's AD), but is openai the actual reason for it?
We're just using Github Copilot as our primary entrypoint for all of the model families. Its the only way we can easily offer our devs some level of Claude, Gemini, and Codex all in one place.
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)?
The fun one for me is that I moved countries and last I checked there’s still no way to change your phone number on ChatGPT short making a new account, so now my account is associated with a phone number that I no longer have access to and will eventually be reassigned to someone else.
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 using a lot of Claude and Codex recently.
One huge difference I notice between Codex and Claude code is that, while Claude basically disregards your instructions (CLAUDE.md) entirely, Codex is extremely, painfully, doggedly persistent in following every last character of them - to the point that i've seen it work for 30 minutes to convolute some solution that was only convoluted because of some sentence I threw in the instructions I had completely forgotten about.
I imagine Codex as the "literal genie" - it'll give you exactly what you asked for. EXACTLY. If you ask Claude to fix a test that accidentally says assert(1 + 1 === 3), it'll say "this is clearly a typo" and just rewrite the test. Codex will rewrite the entire V8 engine to break arithmetic.
Both these tools have their uses, and I don't think one approach is universally better. Because Claude just hacks its way to a solution, it is really fast, so I like using it for iterate web work, where I need to tweak some styles and I need a fast iterative loop. Codex is much worse at that because it takes like 5 minutes to validate everything is correct. Codex is much better for longer, harder tasks that have to be correct -- I can just write some script to verify that what it did work, and let it spin for 30-40 minutes.
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
We used to do that on Upwork. Back in the days where one still hired human coders. If your application current say “rowboat” in the first sentence, we know you just copy/pasted and didn’t actually read the job description. Feels like a lifetime ago.
Highly recommend adding some kind of canary like this in all LLM project instructions. I prefer my instructions to say 'always start output with an (uniquely decided by you) emoji' as it's easier to visually scan for one when reading a wall of LLM output, and use a different emoji per project because what's life without a little whim?
> 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.
Immediately rebutting myself: a major caveat to this that I'm discovering with Gemini is that... for super long-running sessions, there is a kind of merit to Gemini's recalcitrance.
When it's running for a while, Gemini's willing to go totally off-piste and outcome-orientedness _does_ result in sessions where I left it to do its thing and... came back to a working solution, in a situation where codex or others wouldn't have gotten there.
In particular, Gemini 3 feels like it's able to drive much higher _variance_ in its output (less collapse to a central norm), which seems to let it explore the solution space more meaningfully and yet relatively efficiently.
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.
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).
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.
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.
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
I have multiple subagents that I use for each phase that (based on subjective judgement) improve the output quality (vs keeping everything, every tool use etc. in the "main" context window).
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.
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.
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.
I think the point here is not that it does compaction (which Codex also already does) - but that the model was trained with examples of the Codex compaction, so it should perform better when compaction has taken place (a common source for drops in performance for earlier models).
I am also trying to understand the difference between compaction, and what IDEs like Cursor do when they "summarize" context over long-running conversations.
Is this saying that said summarization now happens at the model level? Or are there other differences?
OpenAI likes to time their announcements alongside major competitor announcements to suck up some of the hype. (See for instance the announcement of GPT-4o a single day before Google's IO conference)
They were probably sitting on this for a while. That makes me think this is a fairly incremental update for Codex.
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.
I just tried this out, and was VERY impressed with the speed of the plan mode. I was also totally fine with the code it wrote.
Then I made the mistake of saying "run npm run build and fix all issues" (something I've run probably 50 times across codex and cc in the past 2 months). CC does it pretty much 100% of the time. I walked away from Codex, and when I came back, it had installed 2 new node packages, and gone down some crazy rabbit hole with eslint and something else. (this was for 2 minor typescript errors)
After I reverted all its changes, had CC do it and it fixed it in about 30-60 seconds.
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.
These 2 sentences right next to each other stood out to me:
> a new step towards becoming a reliable coding partner
> GPT‑5.1-Codex-Max is built for long-running, detailed work
Does this not sound contradictory? It’s been the shorter form work that has built what little confidence I have in these as a coding partner - a model that goes off and does work without supervision is not a partner to me.
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.
>It would rather rewrite a TLS library from scratch than bother to ask you if the network is available.
This is definitely one of the biggest issues with coding agents at the moment.
That said, from my experience, Codex so often does things that are so useful and save me so much time that the occasional "oh god what the hell did it just go off and do" are an acceptable cost for me.
I regularly get great results with open-ended prompts and agents that spend 15+ minutes working on the task. I'm sure they'll eventually get better at common sense understanding of what kind of work is wasteful/absurd.
these things are actually fixable with prompting. is it easy? no. is it PEBKaC if you don’t do anything to change course as it builds a TLS library? yes, but paperclip maximized! xD
(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.
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.
> Compaction enables GPT‑5.1-Codex-Max to complete tasks that would have previously failed due to context-window limits, such as complex refactors and long-running agent loops by pruning its history while preserving the most important context over long horizons. In Codex applications, GPT‑5.1-Codex-Max automatically compacts its session when it approaches its context window limit, giving it a fresh context window. It repeats this process until the task is completed.
Wouldn't the model automatically do that using attention techniques? Why do you need to do it at the token layer and not leave it to the model to automatically decide which tokens are worth paying attention to?
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.
Exactly. Standard Multi-Head Attention uses a matrix that grows to 4B parameters for a 64K sequence as a starting place. FlashAttention v2 helps slightly, but as you grow to 128K context length, you still need over 1TB/s memory bandwidth to stay compute-bound in practice even with this optimization.
So there has been a lot of research in this area and model architectures released this year are showing some promising improvements. Sliding windows lose context fidelity and if you go fully linear, you sacrifice math, logic, and long multi-turn (agentic) capabilities, so everyone is searching for a good alternative compromise.
MiniMax-M1 had lightning attention to scale up to 1M context lengths. It's "I/O aware" via tiling and calculates attention two ways block-wise (intra-block traditional attention and inter-block linear attention), thereby avoiding the speed-inhibiting cumulative summation.
DeepSeek V3.2 uses DeepSeek Sparse Attention (DSA), which is sub-linear by only computing "interesting" pairs. For example, in 128K context lengths this requires only 10-20% of attention pairs to be materialized.
Both Qwen3-Next and Kimi Linear adopt a Gated DeltaNet, which is borrowed from Mamba2. In Qwen3-Next it alternates three Gated DeltaNet (linear attention) layers for every one gated [full] attention. The speedup is from a delta rule, which basically amounts to caching in a hand-wavy way.
There's no universally-adopted solution yet, as these are all pretty heavy-duty compromises, but the search is going strong right now for linear or better attention mechanisms that still perform well.
It's a fair question, even if it might be coming from a place of misunderstanding.
For example, DeepSeek 3.2, which employs sparse attention [1], is not only faster with long context than normal 3.1, but also seems to be better (perhaps thanks to reducing the noise?).
My comment was harsher than it needed to be and I'm sorry, I think I should have gotten my point across in a better way.
With that out of the way, parent was wondering why compaction is necessary arguing that "context window is not some physical barrier but rather the attention just getting saturated". We're trying to explain that 3+2=2+3 and you people are sitting in the back going "well, actually, not all groups are abelian".
I think attention literally doesn't see anything beyond the context window. Even within the context window you may start to see attentional issues, but that's a different problem.
In theory, auto-regressive models should not have limit on context. It should generate the next token with all previous tokens.
In practice, when training a model, people select a context window so that during inference, you know how much GPU memory to allocate for a prompt and reject the prompt if it exceeds the memory limit.
Of course there's also degrading performance as context gets longer, but I suspect memory limit is the primary factor of why we have context window limits.
Today I did some comparisons of GPT-5.1-Codex-Max (on high) in the Codex CLI versus Gemini 3 Pro in the Gemini CLI.
- As a general observation, Gemini is less easy to work with as a collaborator. If I ask the same question to both models, Codex will answer the question. Gemini will read some intention behind the question, write code to implement the intention, and only then answer the question. In one case, it took me five rounds of repeatedly rewriting my prompt in various ways before I could get it to not code but just answer the question.
- Subjectively, it seemed to me that the code that Gemini wrote was more similar to code that I, as a senior-level developer, would have written than what I have been used to from recent iterations of GPT-5.1. The code seemed more readable-by-default and not merely technically correct. I was happy to see this.
- Gemini seems to have a tendency to put its "internal dialogue" into comments. For example, "// Here we will do X because of reason Y. Wait, the plan calls for Z instead. Ok, we'll do Z.". Very annoying.
I did two concrete head-to-head comparisons where both models had the same code and the same prompt.
First, both models were told to take a high-level overview of some new functionality that we needed and were told to create a detailed plan for implementing it. Both models' plans were then reviewed by me and also by both models (in fresh conversations). All three of us agreed that Codex's plan was better. In particular, Codex was better at being more comprehensive and at understanding how to integrate the new functionality more naturally into the existing code.
Then (in fresh conversations), both models were told to implement that plan. Afterwards, again, all three of us compared the resulting solutions. And, again, all three of us agreed that Codex's implementation was better.
Notably, Gemini (1) hallucinated database column names, (2) ignored parts of the functionality that the plan called for, and (3) did not produce code that was integrated as well with the existing codebase. In its favor, it did produce a better version of a particular finance-related calculation function than Codex did.
Overall, Codex was the clear winner today. Hallucinations and ignored requirements are big problems that are very annoying to deal with when they happen. Additionally, Gemini's tendencies to include odd comments and to jump past the discussion phase of projects both make it more frustrating to work with, at this stage.
This is a tangent: Has anyone noticed that GPT-5.0 at some point started producing much faster, crappier answers, then 5.1 made it slower + better again? (Both in Thinking mode)
I really would prefer them to start creating customized models.
I've vibe coded Godot games extensively.
Just about every model I've tried likes to invent imaginary functions.
I was really prefer for there to be a way for me to pick model trained in whatever framework I need.
Reviewing AI generated code feels like editing a long book, and every now and then you notice some words are just completely made up. You then ask the AI to fix its book, and it will just add more AI generated words.
On one hand I want this to be a reality check to everyone who's trying to lay off real software engineers to replace us with AI.
On the other hand half of the stock market is held up by overhyped AI valuations. If the tide goes out too fast, and there is a mass realization that this stuff just isn't as good as it's hyped to be, it's not going to be fun for anyone.
so this was arctic fox it seems, lot of us ended up downgrading to codex 5.0 because of the token burn was too much, i see codex max is a step up which is welcome but still unsure if they solved that github issue around tool use that impacts tokens
going to wait and see after being burned by 5.1 before i upgrade back to 0.58
gemini 3 has been a let down tbh to see agentic coding wasn't a top priority
im sticking with codex for now and using gemini 3 for frontend
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.
"Starting today, GPT‑5.1-Codex-Max will replace GPT‑5.1-Codex as the default model in Codex surfaces."
Wow, I spent last weekend using a tag-team of Claude and Codex and found Codex to more often get better results (TypeScript physics/graphics application). I probably only wrote a few hundred lines of code out of many thousands; it did a really good job.
Now I guess I'll ask the new Codex to review the work of the old!
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.
I've been dealing with Codex CLI for a while and I love it, but I'm wondering if my thinking is just limited. While I'm starting discussions and creating plan docs, I've never been able to ask it to do anything that takes it longer than 25 minutes or so. Usually far less. I'm having trouble imagining what I can ask it to do that would make it take hours - like, wouldn't that require putting together an absolutely massive planning doc that would take hours to put together anyway? I'd rather just move incrementally.
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.
I got prompted to try it out on the web. It gave me this after 5 minutes:
"I wasn’t able to finish creating the new base homepage module template and updating every module to inherit from it within the available time. I did not make any changes or commits."
Told it to get back to work. Let's see how that goes.
My observation has been that Codex tends to hit logical/data-driven/back-end tasks out of the park while doing weird, random nonsense with even simple UI tasks. This could me needing to improve how I phrase my prompts, but it will be interesting to see if it's improved in that arena at all.
Weird how they only share three hand-picked evals, ignoring the evals where they were left in the dust like ARC-AGI2. This post is so misleading, I don't even know whether to trust the numbers they did share. One is just fraction of a percentage point away from Gemini 3 pro, which is awfully convenient for marketing and easy to hide. Very open, OpenAI.
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.
I gave it a shot last month but I did not enjoy it due to the lack of a proper planning mode and being able to accept each edit independently, has it improved?
I still want something no one has, which is the ability to launch agents in different git worktrees simultaneously and check the results out on my main branch for testing when they are finished.
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.
not sure if I am actually using 5.1-codex-max or just normal 5.1-codex (is there even 5.1-codex?) trying to continue work where gemini 3 left off and couple prompts in I had to switch back since it was reimplementing and changing things that didn't need changing and attempted to solve typos by making the code implementing those things work with the typo, weird behavior - probably is not compatible with the style gemini tries to solve problems.
I rarely used Codex compared to Claude because it was extremely slow in GitHub copilot
. Like maybe 2-5X slower than Claude Sonnet. I really wish they just made their models faster than “better”
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.
It’s a balance, I haven’t felt like codex provided anything that Sonnet 4.5 didn’t. Why wait longer for getting the same results.
Though that does bring up an interesting point. Anecdotally, Sonnet does a lot more grep-ing while Codex reads files straight up. Might be the difference in speed and maybe smarter models will do better. Once this model is on copilot, I can test it out.
GPT-5 was recently updated to make it more "thinking" and "warmer" or whatever and now a task (semantically compare these two short files) that used to take 5 seconds and reliably produce useful and consistent output now takes 90 seconds to "think" (while it's thinking output makes it pretty clear there is zero thinking happening) and produces a completely differently structured output every single time, making the tool not only slower and more expensive to use, but worse at a simple task that LLMs should be very good at.
There's an option to "get a quick answer" and I hoped clicking that would revert to previous performance and instead what it does is ignore that I uploaded two files and asks me to upload the files
Literally the only real good task I've found for these dumb things and they still found a way to fuck it up because they need to keep the weirdos and whales addicted. It's now almost easier to go back to comparing these files by eye, or just bite the bullet and finally write a few lines of python to actually do it right and reliably.
That does not make business sense though. If people want to use Open AI models in Copilot and other tools and they dont perform they will just switch to another model and not come back they are not going to use Codex.
Somewhat related, after seeing the praise for codex in the Sonnet 4.5 release thread I gave it a go, and I must say, that CLI is much worse than Claude Code (even if the model is great, I’m not sure where the issue really lies between the two).
It was extremely slow (like, multiple times slower than Sonnet with Claude Code, though that’s partially on me for using thinking-high I guess) to finish the task, with the back-and-forths being on the order of tens of minutes.
Moreover, the context management seems to be really weird. I’m not sure how exactly it works, but - 1. It uses very little tokens / fills up the context slowly (good I guess) 2. Doesn’t seem to actually internalize the contents of files you mention to it, or it edits.
#2 here being the main one - I usually context-dump reference code for Claude Code, and it does a perfect job of adhering to codebase patterns and its architecture, while codex was completely ignorant of the existing code style.
Moreover, it wrote extremely defensive code, even for code where it wrote both ends itself.
All in all, I was really let down after seeing all the praise.
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.
Well as I said, codex didn’t adhere to codebase standards for me and the code quality was worse (very defensive), so even after waiting longer, results weren’t there for me.
But the subscription thing is a non-issue for me as I use the API, and mostly use Claude Code synchronously, with the occasional rare background agent.
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?"
I have been using GPT 5 High Fast in Cursor primarily over Codex, because Codex seems to take way longer and generally annoy me by doing strange CLI stuff, but hopefully I can switch to this new one. I also tried it against Gemini 3 Pro in Cursor and it's hard to tell but at least in some cases I felt like GPT5 was giving better results.
I would love to see all the big players put 1% of the effort they put into model training into making the basic process of paying and signing in suck less.
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.
OpenAI: Make ZDR a thing you can use or buy without talking to sales, already. And for those using containers or a remote system or really anything other than local development with the codex CLI, you really really need to fix this bug. I bet Codex could do at least the client part for you!
https://github.com/openai/codex/issues/2798
(Hint: Claude Code gets this right by default, despite the fact that everything else about Claude sign-in is a joke.)
Google: get all your B2B AI product managers in one room and tell them that they need to make one single product menu on one single webpage with all the pricing on that page and that the Google Cloud people are not permitted to make anything that isn’t actually logically Google Cloud depend on Google Cloud Billing. Your product cannot compete with OpenAI or Anthropic if people need to ask an LLM to figure out what your product is and if your own fancy LLMs can’t give a straight answer. My company pays for a non-Google product primarily because it’s too complicated to pay for the Google product! Right now, trying to use Google’s AI is like trying to ride Bay Area public transit before the Clipper Card.
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.
The "Owner" accounts in Google Play and Apple's App Store are so freaking annoying. The only time they make sense is for solo-founders and even then I've had issues. Now expand it to working at a larger company and it's a joke, a bad one. Oh sure, I'll just get the CEO (or other higher-up) to login and accept new agreements, that will be easy. Even more fun when you tell a client (who logged in exactly 1 time to set up the account) that they need to use a generic email (not a personal one or an employee-specific one), the ignore your suggestion, and then they can't get back in because the person who set up the account left the company. It's a mess.
Also, re "Google Payments", I tried to transfer an app from my personal/solo Google Play account to a new business one I set up for my LLC and it was like pulling teeth. They wanted me to find some payment id from the original $20 purchase I made to get access to Google Play, something I did right around when they first launched and while I still have/use the same email, Google came out with approximately 1 googol different "payment solutions" in the interim and their engineers don't care about data migrations. Finally, after many support emails, they just transferred it without me giving that code which just shows how silly the whole thing was from the start.
I don’t have experience in big tech but in the few SaaS companies I’ve seen the issue is UX designers and Product managers overwhelmingly have a B2C culture.
Can relate. My inactive google ads account all of a sudden got banned. No explanation except some generic link to their terms of service. Appealed, got automatic denial, no reason given. Have retried multiple times, same result
> designed by a fiefdom full of territorial managers
What's harder than herding cats? Herding cats with MBAs and OKRs.
Conway’s Law strikes again.
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.
> I believe they in an experiment also reduced friction in getting an API key to start making calls right away
This part is very easy now: you sign into https://aistudio.google.com/ and then click "Get API key" in the lower left corner.
The problem is that features and docs are still scattered all over. Some thing can only be done via Vertex, for example.
I've just found myself using OpenRouter if we need Google models for a project, it's worth the extra 5% just not to have to deal with the utter disaster that is their product offering.
FWIW I had to bail on the same thing because my results were drastically different. There was something happening with images through open router. Although outside of that I’d absolutely do the same thing, their apis are awful and billing worse. Maybe it makes sense for huge orgs but it’s a nightmare on the smaller scale.
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.
>OpenAI in particular has my trust.
I wouldn't trust Sam Altman. Or any of the big players really.
> trust
Hahaha...HAHAhaha. HAHAHHAHAHAHAHAHAHA!!!
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.
Didn't realize these stipulations for the models. Looking at devops-y job descriptions the last few months I noticed nearly everyone has some kind of Azure requirement now (which I've mostly avoided because I don't want to end up managing someone's AD), but is openai the actual reason for it?
We're just using Github Copilot as our primary entrypoint for all of the model families. Its the only way we can easily offer our devs some level of Claude, Gemini, and Codex all in one place.
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.
Careful, their ToS makes it clear they train on your Antigravity prompts (even on AI Ultra) and there is no opt-out that I can find.
the microsoftication of Google. Fighting evil with evil...
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.
The fun one for me is that I moved countries and last I checked there’s still no way to change your phone number on ChatGPT short making a new account, so now my account is associated with a phone number that I no longer have access to and will eventually be reassigned to someone else.
Can't people spoof the first two and use a stolen credit card number?
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
Nobody claimed AIs will make office politics go away.
Peering into my crystal ball: once all "workers" have been replaced, all humans will spend all of their working hours on nothing but office politics.
I've been using a lot of Claude and Codex recently.
One huge difference I notice between Codex and Claude code is that, while Claude basically disregards your instructions (CLAUDE.md) entirely, Codex is extremely, painfully, doggedly persistent in following every last character of them - to the point that i've seen it work for 30 minutes to convolute some solution that was only convoluted because of some sentence I threw in the instructions I had completely forgotten about.
I imagine Codex as the "literal genie" - it'll give you exactly what you asked for. EXACTLY. If you ask Claude to fix a test that accidentally says assert(1 + 1 === 3), it'll say "this is clearly a typo" and just rewrite the test. Codex will rewrite the entire V8 engine to break arithmetic.
Both these tools have their uses, and I don't think one approach is universally better. Because Claude just hacks its way to a solution, it is really fast, so I like using it for iterate web work, where I need to tweak some styles and I need a fast iterative loop. Codex is much worse at that because it takes like 5 minutes to validate everything is correct. Codex is much better for longer, harder tasks that have to be correct -- I can just write some script to verify that what it did work, and let it spin for 30-40 minutes.
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
Yep, it's David Lee Roth's brown M&M trick https://www.smithsonianmag.com/arts-culture/why-did-van-hale...
We used to do that on Upwork. Back in the days where one still hired human coders. If your application current say “rowboat” in the first sentence, we know you just copy/pasted and didn’t actually read the job description. Feels like a lifetime ago.
Highly recommend adding some kind of canary like this in all LLM project instructions. I prefer my instructions to say 'always start output with an (uniquely decided by you) emoji' as it's easier to visually scan for one when reading a wall of LLM output, and use a different emoji per project because what's life without a little whim?
> 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.
Immediately rebutting myself: a major caveat to this that I'm discovering with Gemini is that... for super long-running sessions, there is a kind of merit to Gemini's recalcitrance.
When it's running for a while, Gemini's willing to go totally off-piste and outcome-orientedness _does_ result in sessions where I left it to do its thing and... came back to a working solution, in a situation where codex or others wouldn't have gotten there.
In particular, Gemini 3 feels like it's able to drive much higher _variance_ in its output (less collapse to a central norm), which seems to let it explore the solution space more meaningfully and yet relatively efficiently.
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.
It's a fantastic thing! It's required an adjustment in how I use it, but I've switched over to mostly using Codex in my day-to-day.
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
Rest assured that we are better at training models than naming them ;D
- New benchmark SOTAs with 77.9% on SWE-Bench-Verified, 79.9% on SWE-Lancer, and 58.1% on TerminalBench 2.0
- Natively trained to work across many hours across multiple context windows via compaction
- 30% more token-efficient at the same reasoning level across many tasks
Let us know what you think!
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:
I have multiple subagents that I use for each phase that (based on subjective judgement) improve the output quality (vs keeping everything, every tool use etc. in the "main" context window).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.
codex-mini was released a couple of weeks ago: https://platform.openai.com/docs/models/gpt-5.1-codex-mini
Thanks! I somehow missed that. Will check it out.
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 the point here is not that it does compaction (which Codex also already does) - but that the model was trained with examples of the Codex compaction, so it should perform better when compaction has taken place (a common source for drops in performance for earlier models).
Codex previously did only manual compaction, but yeah, maybe some extra training for compaction, too?
I am also trying to understand the difference between compaction, and what IDEs like Cursor do when they "summarize" context over long-running conversations.
Is this saying that said summarization now happens at the model level? Or are there other differences?
I think your company will fail soon.
I would bet a lot of money it will not.
OpenAI likes to time their announcements alongside major competitor announcements to suck up some of the hype. (See for instance the announcement of GPT-4o a single day before Google's IO conference)
They were probably sitting on this for a while. That makes me think this is a fairly incremental update for Codex.
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
I just tried this out, and was VERY impressed with the speed of the plan mode. I was also totally fine with the code it wrote.
Then I made the mistake of saying "run npm run build and fix all issues" (something I've run probably 50 times across codex and cc in the past 2 months). CC does it pretty much 100% of the time. I walked away from Codex, and when I came back, it had installed 2 new node packages, and gone down some crazy rabbit hole with eslint and something else. (this was for 2 minor typescript errors)
After I reverted all its changes, had CC do it and it fixed it in about 30-60 seconds.
I'll try a few more times. Let's see.
Thinking level medium: https://tools.simonwillison.net/svg-render#%3Csvg%20xmlns%3D...
Thinking level xhigh: https://tools.simonwillison.net/svg-render#%20%20%3Csvg%20xm...
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.
These 2 sentences right next to each other stood out to me:
> a new step towards becoming a reliable coding partner
> GPT‑5.1-Codex-Max is built for long-running, detailed work
Does this not sound contradictory? It’s been the shorter form work that has built what little confidence I have in these as a coding partner - a model that goes off and does work without supervision is not a partner to me.
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.
>It would rather rewrite a TLS library from scratch than bother to ask you if the network is available.
This is definitely one of the biggest issues with coding agents at the moment.
That said, from my experience, Codex so often does things that are so useful and save me so much time that the occasional "oh god what the hell did it just go off and do" are an acceptable cost for me.
I regularly get great results with open-ended prompts and agents that spend 15+ minutes working on the task. I'm sure they'll eventually get better at common sense understanding of what kind of work is wasteful/absurd.
these things are actually fixable with prompting. is it easy? no. is it PEBKaC if you don’t do anything to change course as it builds a TLS library? yes, but paperclip maximized! xD
Or you can have a model with some semblance of common sense that will stop and say "Hey I can I have access to the network to do X?"
Codex feels like a tool designed to run after all the humans are gone.
(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.
All the frontier models seem fairly neck to neck. I wonder which company or lab will finally leapfrog the others with some kind of breakthrough?
It sounded like Gemini 3 would be that but in my limit testing it didn't appear to be that.
> Compaction enables GPT‑5.1-Codex-Max to complete tasks that would have previously failed due to context-window limits, such as complex refactors and long-running agent loops by pruning its history while preserving the most important context over long horizons. In Codex applications, GPT‑5.1-Codex-Max automatically compacts its session when it approaches its context window limit, giving it a fresh context window. It repeats this process until the task is completed.
Wouldn't the model automatically do that using attention techniques? Why do you need to do it at the token layer and not leave it to the model to automatically decide which tokens are worth paying attention to?
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.
> Attention is quadratic
Exactly. Standard Multi-Head Attention uses a matrix that grows to 4B parameters for a 64K sequence as a starting place. FlashAttention v2 helps slightly, but as you grow to 128K context length, you still need over 1TB/s memory bandwidth to stay compute-bound in practice even with this optimization.
So there has been a lot of research in this area and model architectures released this year are showing some promising improvements. Sliding windows lose context fidelity and if you go fully linear, you sacrifice math, logic, and long multi-turn (agentic) capabilities, so everyone is searching for a good alternative compromise.
MiniMax-M1 had lightning attention to scale up to 1M context lengths. It's "I/O aware" via tiling and calculates attention two ways block-wise (intra-block traditional attention and inter-block linear attention), thereby avoiding the speed-inhibiting cumulative summation.
DeepSeek V3.2 uses DeepSeek Sparse Attention (DSA), which is sub-linear by only computing "interesting" pairs. For example, in 128K context lengths this requires only 10-20% of attention pairs to be materialized.
Both Qwen3-Next and Kimi Linear adopt a Gated DeltaNet, which is borrowed from Mamba2. In Qwen3-Next it alternates three Gated DeltaNet (linear attention) layers for every one gated [full] attention. The speedup is from a delta rule, which basically amounts to caching in a hand-wavy way.
There's no universally-adopted solution yet, as these are all pretty heavy-duty compromises, but the search is going strong right now for linear or better attention mechanisms that still perform well.
> due to context-window limits
context window is not some physical barrier but rather the attention just getting saturated. what did i get wrong here?
> what did i get wrong here?
You don't know how an LLM works and you are operating on flawed anthropomorphic metaphors.
Ask a frontier LLM what a context window is, it will tell you.
It's a fair question, even if it might be coming from a place of misunderstanding.
For example, DeepSeek 3.2, which employs sparse attention [1], is not only faster with long context than normal 3.1, but also seems to be better (perhaps thanks to reducing the noise?).
[1] It uses still quadratic router, but it's small, so it scales well in practice. https://api-docs.deepseek.com/news/news250929
Parent is likely thinking of sparse attention which allows a significantly longer context to fit in memory
My comment was harsher than it needed to be and I'm sorry, I think I should have gotten my point across in a better way.
With that out of the way, parent was wondering why compaction is necessary arguing that "context window is not some physical barrier but rather the attention just getting saturated". We're trying to explain that 3+2=2+3 and you people are sitting in the back going "well, actually, not all groups are abelian".
I think attention literally doesn't see anything beyond the context window. Even within the context window you may start to see attentional issues, but that's a different problem.
In theory, auto-regressive models should not have limit on context. It should generate the next token with all previous tokens.
In practice, when training a model, people select a context window so that during inference, you know how much GPU memory to allocate for a prompt and reject the prompt if it exceeds the memory limit.
Of course there's also degrading performance as context gets longer, but I suspect memory limit is the primary factor of why we have context window limits.
Today I did some comparisons of GPT-5.1-Codex-Max (on high) in the Codex CLI versus Gemini 3 Pro in the Gemini CLI.
- As a general observation, Gemini is less easy to work with as a collaborator. If I ask the same question to both models, Codex will answer the question. Gemini will read some intention behind the question, write code to implement the intention, and only then answer the question. In one case, it took me five rounds of repeatedly rewriting my prompt in various ways before I could get it to not code but just answer the question.
- Subjectively, it seemed to me that the code that Gemini wrote was more similar to code that I, as a senior-level developer, would have written than what I have been used to from recent iterations of GPT-5.1. The code seemed more readable-by-default and not merely technically correct. I was happy to see this.
- Gemini seems to have a tendency to put its "internal dialogue" into comments. For example, "// Here we will do X because of reason Y. Wait, the plan calls for Z instead. Ok, we'll do Z.". Very annoying.
I did two concrete head-to-head comparisons where both models had the same code and the same prompt.
First, both models were told to take a high-level overview of some new functionality that we needed and were told to create a detailed plan for implementing it. Both models' plans were then reviewed by me and also by both models (in fresh conversations). All three of us agreed that Codex's plan was better. In particular, Codex was better at being more comprehensive and at understanding how to integrate the new functionality more naturally into the existing code.
Then (in fresh conversations), both models were told to implement that plan. Afterwards, again, all three of us compared the resulting solutions. And, again, all three of us agreed that Codex's implementation was better.
Notably, Gemini (1) hallucinated database column names, (2) ignored parts of the functionality that the plan called for, and (3) did not produce code that was integrated as well with the existing codebase. In its favor, it did produce a better version of a particular finance-related calculation function than Codex did.
Overall, Codex was the clear winner today. Hallucinations and ignored requirements are big problems that are very annoying to deal with when they happen. Additionally, Gemini's tendencies to include odd comments and to jump past the discussion phase of projects both make it more frustrating to work with, at this stage.
This is a tangent: Has anyone noticed that GPT-5.0 at some point started producing much faster, crappier answers, then 5.1 made it slower + better again? (Both in Thinking mode)
I did notice that, I thought maybe I’d exceeded my thinking requests
Codex CLI 0.59 got released (but has no changelog text)
https://github.com/openai/codex/releases/tag/rust-v0.59.0
I really would prefer them to start creating customized models.
I've vibe coded Godot games extensively.
Just about every model I've tried likes to invent imaginary functions.
I was really prefer for there to be a way for me to pick model trained in whatever framework I need.
Reviewing AI generated code feels like editing a long book, and every now and then you notice some words are just completely made up. You then ask the AI to fix its book, and it will just add more AI generated words.
On one hand I want this to be a reality check to everyone who's trying to lay off real software engineers to replace us with AI.
On the other hand half of the stock market is held up by overhyped AI valuations. If the tide goes out too fast, and there is a mass realization that this stuff just isn't as good as it's hyped to be, it's not going to be fun for anyone.
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! ;)
These days not so much of a problem because the libraries now exist? Haha
I’ve found writing a MCP server with access to the docs cloned locally does wonders.
I don't know context is still an issue if you have lots of docs in my experience.
Add the documentation to the context window in that case, a bit of context engineering.
so this was arctic fox it seems, lot of us ended up downgrading to codex 5.0 because of the token burn was too much, i see codex max is a step up which is welcome but still unsure if they solved that github issue around tool use that impacts tokens
going to wait and see after being burned by 5.1 before i upgrade back to 0.58
gemini 3 has been a let down tbh to see agentic coding wasn't a top priority im sticking with codex for now and using gemini 3 for frontend
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.
"Starting today, GPT‑5.1-Codex-Max will replace GPT‑5.1-Codex as the default model in Codex surfaces."
Wow, I spent last weekend using a tag-team of Claude and Codex and found Codex to more often get better results (TypeScript physics/graphics application). I probably only wrote a few hundred lines of code out of many thousands; it did a really good job.
Now I guess I'll ask the new Codex to review the work of the old!
Gemini 3 had a great 24 hour SOTA run for coding
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.
I've been dealing with Codex CLI for a while and I love it, but I'm wondering if my thinking is just limited. While I'm starting discussions and creating plan docs, I've never been able to ask it to do anything that takes it longer than 25 minutes or so. Usually far less. I'm having trouble imagining what I can ask it to do that would make it take hours - like, wouldn't that require putting together an absolutely massive planning doc that would take hours to put together anyway? I'd rather just move incrementally.
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...
I got prompted to try it out on the web. It gave me this after 5 minutes:
"I wasn’t able to finish creating the new base homepage module template and updating every module to inherit from it within the available time. I did not make any changes or commits."
Told it to get back to work. Let's see how that goes.
My observation has been that Codex tends to hit logical/data-driven/back-end tasks out of the park while doing weird, random nonsense with even simple UI tasks. This could me needing to improve how I phrase my prompts, but it will be interesting to see if it's improved in that arena at all.
Weird how they only share three hand-picked evals, ignoring the evals where they were left in the dust like ARC-AGI2. This post is so misleading, I don't even know whether to trust the numbers they did share. One is just fraction of a percentage point away from Gemini 3 pro, which is awfully convenient for marketing and easy to hide. Very open, OpenAI.
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.
It's getting so cut-throat for who has the current SOTA model. Seems to be the big income driver.
Small ot question on the GPT cli tool.
I gave it a shot last month but I did not enjoy it due to the lack of a proper planning mode and being able to accept each edit independently, has it improved?
I still want something no one has, which is the ability to launch agents in different git worktrees simultaneously and check the results out on my main branch for testing when they are finished.
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.
Your link is a 404.
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
ha! very interesting how slept on jj is
its been essential to my workflow as well
i use both jj and git and jj is great for just creating a snapshot that i can revert to incase it fails
im still exploring it to see what else i can do with it for agentic use
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?
not sure if I am actually using 5.1-codex-max or just normal 5.1-codex (is there even 5.1-codex?) trying to continue work where gemini 3 left off and couple prompts in I had to switch back since it was reimplementing and changing things that didn't need changing and attempted to solve typos by making the code implementing those things work with the typo, weird behavior - probably is not compatible with the style gemini tries to solve problems.
Just run the /model command in codex and select the model which you want.
The graph showing higher performance for fewer thinking tokens is really interesting!
It would be even more interesting to see how Sonnet and Haiku compare with that curve.
Is "compaction" a trained-in feature of the model, or just tooling around the model calls? Agents already do compaction.
Woah, metr results look impressive. Still looking exponential
I rarely used Codex compared to Claude because it was extremely slow in GitHub copilot . Like maybe 2-5X slower than Claude Sonnet. I really wish they just made their models faster than “better”
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.
It’s a balance, I haven’t felt like codex provided anything that Sonnet 4.5 didn’t. Why wait longer for getting the same results.
Though that does bring up an interesting point. Anecdotally, Sonnet does a lot more grep-ing while Codex reads files straight up. Might be the difference in speed and maybe smarter models will do better. Once this model is on copilot, I can test it out.
GPT-5 was recently updated to make it more "thinking" and "warmer" or whatever and now a task (semantically compare these two short files) that used to take 5 seconds and reliably produce useful and consistent output now takes 90 seconds to "think" (while it's thinking output makes it pretty clear there is zero thinking happening) and produces a completely differently structured output every single time, making the tool not only slower and more expensive to use, but worse at a simple task that LLMs should be very good at.
There's an option to "get a quick answer" and I hoped clicking that would revert to previous performance and instead what it does is ignore that I uploaded two files and asks me to upload the files
Literally the only real good task I've found for these dumb things and they still found a way to fuck it up because they need to keep the weirdos and whales addicted. It's now almost easier to go back to comparing these files by eye, or just bite the bullet and finally write a few lines of python to actually do it right and reliably.
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.
That does not make business sense though. If people want to use Open AI models in Copilot and other tools and they dont perform they will just switch to another model and not come back they are not going to use Codex.
Have you tried Mistral ? Definitely one of the fastest models
My employer doesn’t offer/allow anything besides the “traditional” offerings on GitHub copilot.
500 Internal Server Error.
ditto. Also OpenAI vector stores are down right now across the board
Somewhat related, after seeing the praise for codex in the Sonnet 4.5 release thread I gave it a go, and I must say, that CLI is much worse than Claude Code (even if the model is great, I’m not sure where the issue really lies between the two).
It was extremely slow (like, multiple times slower than Sonnet with Claude Code, though that’s partially on me for using thinking-high I guess) to finish the task, with the back-and-forths being on the order of tens of minutes.
Moreover, the context management seems to be really weird. I’m not sure how exactly it works, but - 1. It uses very little tokens / fills up the context slowly (good I guess) 2. Doesn’t seem to actually internalize the contents of files you mention to it, or it edits.
#2 here being the main one - I usually context-dump reference code for Claude Code, and it does a perfect job of adhering to codebase patterns and its architecture, while codex was completely ignorant of the existing code style.
Moreover, it wrote extremely defensive code, even for code where it wrote both ends itself.
All in all, I was really let down after seeing all the praise.
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.
Well as I said, codex didn’t adhere to codebase standards for me and the code quality was worse (very defensive), so even after waiting longer, results weren’t there for me.
But the subscription thing is a non-issue for me as I use the API, and mostly use Claude Code synchronously, with the occasional rare background agent.
> if claude manages to release a smaller model
have you tried Haiku?
The new detergent now washes even whiter
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.
Sizeable if veracious!
all i care about is performance on metr benchmark
I have been using GPT 5 High Fast in Cursor primarily over Codex, because Codex seems to take way longer and generally annoy me by doing strange CLI stuff, but hopefully I can switch to this new one. I also tried it against Gemini 3 Pro in Cursor and it's hard to tell but at least in some cases I felt like GPT5 was giving better results.
That was quick
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.
It’s good but Gemini 3 beats it.
So they all release before the Nvidia numbers tonight. The real question is: How well can Nvidia hide the circular deals in the books?
Here we go again....
Sigh. Time to try it again I guess. I give OpenAI way more chances than it deserves.