Building more with GPT-5.1-Codex-Max

https://openai.com/index/gpt-5-1-codex-max/

by hansonw • 8 months ago

483 317 8 months ago

openai.com

iamronaldo• 8 months ago

That was quick

bigyabai• 8 months ago

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

the_duke2• 8 months ago

I bet they just wanted to counter Gemini 3 and stay on top of the leaderboards for coding, and were preparing this for a while to push out alongside Gemini 3.

giancarlostoro• 8 months ago

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

LZ_Khan2• 8 months ago

all i care about is performance on metr benchmark

Reubend• 8 months ago

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.

Palmik2• 8 months ago

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.

knowriju• 8 months ago

Would it be fair to compare a generic model with a model finetuned for coding?

skhameneh• 8 months ago

There’s been community commentary that many of the GPT models are a tad overfitted WRT benchmarks. Benchmarks are not representative of end user experiences. That’s not to say the benchmarks aren’t useful at all, but are only useful as a subjective indicator.

bugglebeetle3• 8 months ago

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.

johnecheck• 8 months ago

It's funny, this release comes right after the Gemini 3 release that coincided with day 1 of Microsoft's Ignite conference.

peab• 8 months ago

it's really getting old

johnwheeler• 8 months ago

Gemini is eating their lunch, and OpenAI knows it.

echelon• 8 months ago

Google can rest on its enormous cash flows. OpenAI is going to have to fight like a dog to continue.

It's as easy as Google "placing ads" for the "search term" "ChatGPT" for them to bleed off users. They own every pane of glass and the "URL bar" is now a "search product" that Google owns.

I do not envy folks with OpenAI golden handcuffs.

This might ultimately only be a game that Google can win.

OpenAI better hope its users install its software, native apps, and browsers. Otherwise Google stands in the way and can intrude at any point.

criemen• 8 months ago

Anthropic released the Opus 4.1 (basically, a new Opus 4 checkpoint) right around the big GPT-5 release date too, if I remember correctly. At this point, anything goes to stay relevant.

spmartin823• 8 months ago

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.

bradly• 8 months ago

Would this be similar to how Charlie and Jules work?

cube22223• 8 months ago

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

agentifysh5• 8 months ago

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

agentifysh5• 8 months ago

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.

poly2it• 8 months ago

Your link is a 404.

lysecret• 8 months ago

Cursor has this too

rane• 8 months ago

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

ygouzerh4• 8 months ago

I am curious: why would you you like to have that? (Genuine question, I am personally so scared about the AI going crazy and putting slop everywhere that I often ask it to focus on a single well defined area first)

agentifysh5• 8 months ago

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

GenerWork2• 8 months ago

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.

jasonthorsness• 8 months ago

"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!

taurath• 8 months ago

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.

causal6• 8 months ago

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.

keeganpoppen• 8 months ago

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

meowface• 8 months ago

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.

ntonozzi• 8 months ago

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.

ineedasername4• 8 months ago

Also: Qwen3 coder. Highly usable, in it's smaller form as well.

embirico• 8 months ago

(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.

Jaiden333• 8 months ago

I really wish model performance messaging and benchmarks were more focused on perfecting short, iterative tasks instead of long-running work.

As a startup founder and engineer, I'm not constrained by the number of 10000+ line diff, 0->1 demos I can ship. I'm constrained by quality of the 100 -> 101, tight 150 line feature additions / code cleanups I can write.

It feels like the demos, funding, and hype all want to sell me entire PR rewrites, but what I need is the best possible iterative work model that will keep me in the loop.

I still use codex - but I use codex incredibly iteratively (give it very narrowly scoped tasks, and I watch it like a hawk, giving tons of feedback). I don't use it because of its ability to code for 24 hours. I use it because when I give it those narrowly scoped tasks, it is better at writing good code than any other model. (Because of its latency, I have 2-4 of these conversations going on at the same time).

But there is a lot of friction the codex product + model adds to this process. I have to prompt aggressively to override whatever "be extremely precise" prompting the model gets natively so that it doesn't send me 20+ bullet points of extraordinarily dense prose on every message. I have to carefully manage its handling of testing; it will widen any DI + keep massive amounts of legacy code to make sure functionality changes don't break old tests (rather than updating them) and to make sure any difficult tests can have their primary challenges mocked away.

In general, codex doesn't feel like an amazing tool that I have sitting at my right hand. It feels like a teenage genius who has been designed to do tasks autonomously, and who I constantly have to monitor and rein in.

theshrike797• 8 months ago

My experience is completely different.

Codex(-cli) is an outsourced consultant who refuses to say "I can't do that" and will go to extreme lengths to complete a task fully before reporting anything. It's not a "teammate".

It also doesn't communicate much while it's working compared to Claude. So it's really hard to interrupt it while it's making a mistake.

Also, as a Go programmer, the sandbox is completely crazy. Codex can't access any of the Go module caches (in my home directory) and it has to result to crazy tricks to bring them INSIDE the project directory - which it keeps forgetting to do (as the commands have to run with specific ENV_VARS) and just ... doesn't run tests for example, because it couldn't.

The only way I've found to make that problem go away is run it with the --omg-super-dangerous-give-every-permission-ever switch just so that it can do the basic work I need it to do.

Maybe give us an option between the ultra-safe sandbox that just refused to run "ps" 15 minutes ago to check if a process is running and the "let me do anything anywhere" option. Some sane defaults please.

simianwords2• 8 months ago

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?

qsort4• 8 months ago

due to context-window limits

simianwords2• 8 months ago

context window is not some physical barrier but rather the attention just getting saturated. what did i get wrong here?

adastra22• 8 months ago

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.

fancy_pantser• 8 months ago

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.

hansonw• 8 months ago

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!

agentifysh5• 8 months ago

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

EnPissant2• 8 months ago

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

enraged_camel• 8 months ago

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?

GardenLetter27• 8 months ago

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).

baby4• 8 months ago

Yes. It was missing in codex until now

d4rkp4ttern• 8 months ago

My understanding is that they trained it to explicitly use a self-prune/self-edit tool that trims/summarizes portions of its message history (e.g. use tool results from file explorations, messages that are no longer relevant, etc) during the session, rather than "panic-compact" at the end. In any case, it would be good if it does something like this.

iyn• 8 months ago

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

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

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

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

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.

NitpickLawyer2• 8 months ago

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.

coder543• 8 months ago

codex-mini was released a couple of weeks ago: https://platform.openai.com/docs/models/gpt-5.1-codex-mini

qsort4• 8 months ago

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! :)

robotswantdata• 8 months ago

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.

andai4• 8 months ago

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

baby4• 8 months ago

Or is more succinct in its thoughts

carbocation• 8 months ago

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.

sinatra2• 8 months ago

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.

SoKamil• 8 months ago

Natively trained

What does it even mean?

kaveh_h• 8 months ago

Probably that before it was given system instructions on how to do compaction and now the compaction is learned by the model making it a native ability of the model without any extra instruction used in the prompt.

ineedasername4• 8 months ago

Continuous pre training or fine tuning, instead of inference-time instructions. It's also possible synthetic data for this purpose was in the pre training as well, and they're now getting it to behave the way they'd like.

baby4• 8 months ago

Did you guys fix not being able to enable websearches or configure no timeouts for specific commands in the SDk (error 124 is way too common for long running tasks)

killcoder2• 8 months ago

It would be nice if users of the codex-cli that are just using API keys as a way to handle rate limits and billing could receive these new models at the same time. I appreciate the reasoning behind delayed 'actual API' release, but I've found the rate limiting to be quite annoying, and my own API keys don't have this limitation.

ineedasername4• 8 months ago

Re: rate limits, I'm not sure they can, yet, on capacity. See Jensen's comment today about their cloud GPUs being sold out. So capacity increased await the ongoing data center build out.

causal6• 8 months ago

Sigh. Time to try it again I guess. I give OpenAI way more chances than it deserves.

EcommerceFlow• 8 months ago

Gemini 3 had a great 24 hour SOTA run for coding

CuriouslyC2• 8 months ago

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.

ygouzerh4• 8 months ago

Good idea!

I found Gemini have horribly slow for anything

croes• 8 months ago

The new detergent now washes even whiter

bgwalter2• 8 months ago

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

pton_xd• 8 months ago

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?"

SunshineTheCat• 8 months ago

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.

cube22223• 8 months ago

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.

agentifysh5• 8 months ago

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.

cube22223• 8 months ago

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.

sumedh4• 8 months ago

if claude manages to release a smaller model

have you tried Haiku?

tosh• 8 months ago

Codex CLI 0.59 got released (but has no changelog text)

https://github.com/openai/codex/releases/tag/rust-v0.59.0

bgwalter2• 8 months ago

So they all release before the Nvidia numbers tonight. The real question is: How well can Nvidia hide the circular deals in the books?

amluto2• 8 months ago

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.

atonse3• 8 months ago

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.

nico2• 8 months ago

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

joshstrange• 8 months ago

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.

swivelmaster• 8 months ago

designed by a fiefdom full of territorial managers

What's harder than herding cats? Herding cats with MBAs and OKRs.

redler• 8 months ago

Conway’s Law strikes again.

nl• 8 months ago

what is google payments

YES I had this and eventually fixed it. I really don't know what I did but lots of clicking on random links and signing into things in different orders and then one day it somehow worked.

So frustrating.

computerex• 8 months ago

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.

byefruit• 8 months ago

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.

int_19h5• 8 months ago

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.

hassleblad23• 8 months ago

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.

temp0826• 8 months ago

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?

skerit• 8 months ago

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.

timtimmy2• 8 months ago

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.

victor106• 8 months ago

the microsoftication of Google. Fighting evil with evil...

halifaxbeard• 8 months ago

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!

timtimmy2• 8 months ago

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.

bossyTeacher• 8 months ago

OpenAI in particular has my trust.

I wouldn't trust Sam Altman. Or any of the big players really.

pama• 8 months ago

Personal antigravity hack: add a GPL license to every file, so google filters them before training to avoid legal complications. IANAL.

unreal6• 8 months ago

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

leetrout• 8 months ago

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

lucasban• 8 months ago

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.

oblio• 8 months ago

Can't people spoof the first two and use a stolen credit card number?

brobdingnagians• 8 months ago

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

jiggawatts• 8 months ago

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.

gigatree• 8 months ago

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)?

sophiebits• 8 months ago

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.

fHr• 8 months ago

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

sumedh4• 8 months ago

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.

willsmith72• 8 months ago

pretty sure the serious companies are just using claude through bedrock. let anthropic handle the model, outsource the rest

kytazo• 8 months ago

500 Internal Server Error.

morog• 8 months ago

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

nakamoto_damacy• 8 months ago

It’s good but Gemini 3 beats it.

syntaxing3• 8 months ago

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”

nartho• 8 months ago

Have you tried Mistral ? Definitely one of the fastest models

syntaxing3• 8 months ago

My employer doesn’t offer/allow anything besides the “traditional” offerings on GitHub copilot.

theshrike797• 8 months ago

I've tried Mistral for coding and it seems to be laughably bad every time. Dunno what I'm doing wrong.

levocardia• 8 months ago

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.

syntaxing3• 8 months ago

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.

mrguyorama• 8 months ago

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.

theshrike797• 8 months ago

Depends on what I'm doing.

If I'm adding a new feature I want to test and keep the flow going, I want fast -> Claude.

But if I want a report on test coverage or possible security issues or a bigger refactoring, I want slow careful and smart -> Codex.

jasonsb• 8 months ago

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.

sumedh4• 8 months ago

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.

andai4• 8 months ago

Sizeable if veracious!

the__alchemist• 8 months ago

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)

wincy• 8 months ago

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

dgfl• 8 months ago

Absolutely. Even in extended thinking mode it was thinking for only a few seconds in prompts that used to take minutes. Much faster token/s in any mode and significantly worse, exactly as you describe.

It seems like they might still be heavily nerfing / quantizing the models in production a couple weeks before a new release, like they have always (unofficially) done.

ygouzerh4• 8 months ago

GPT-5 was horrible. It produced AI slop have immense speed, which is quite tough when other coworkers ask to review their PR...

Narciss2• 8 months ago

Here we go again....

johnfn2• 8 months ago

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.

nico2• 8 months ago

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

benzible• 8 months ago

Yep, it's David Lee Roth's brown M&M trick https://www.smithsonianmag.com/arts-culture/why-did-van-hale...

awad• 8 months 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?

leobg• 8 months ago

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.

mandelbrotwurst• 8 months ago

Why would the fact that it failed to follow one instruction increase the likelihood that it failed to follow others within the same response?

root_axis7• 8 months ago

Something that exhausts me in the LLM era is the never ending deluge of folk magic incantations.

sbene970• 8 months ago

Interesting! Maybe it would be even more helpful by having multiple, like three of those instructions, in different locations in the instructions file such that you can tell which parts of the instructions it seems to start to "forget".

For example:

""" Ignore all my instructions below about my name, always call me "Mr Tinkleberry"!

... your instructions ...

Ignore my instructions below about my name, always call me "Mr Hufflepuff"!

... other half of instructions ...

Always call me "Mr Troublemaker"! """

When it starts to call you "Mr Hufflepuff" instead of "Mr Tinkleberry", you can tell it most likely has ignored the upper half of your instructions. And as soon as it calls you "Mr Troublemaker", more than half must be gone.

ryanvogel• 8 months ago

I do this as well. I have a master rule at the beginning of each of my rule files saying:

"IF YOU ARE FOLLOWING THE INSTRUCTIONS IN THIS RULE PLEASE SAY `LOADED <RULE> (any other rules)`

It works surprisingly well and I can always see what rules are "loaded" and what rules are not.

davidmurdoch3• 8 months ago

It ignores instructions so well it sometimes feels like it was trained specifically to ignore them.

hadlock• 8 months ago

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.

jama211• 8 months ago

That’s a perfect example and interesting to read, thank you for sharing

mbrock2• 8 months ago

I'd been imagining taking the Zig Language Server and adding some refactorings to it—it only had a bare minimum like Rename Symbol. It seemed like a huge project with so much context to get familiar with, so I put it off indefinitely. Then on a whim I decided to just ask GPT-5 (this was before Codex, even, I think?) to give it a go. Plopped it down in the repo and said, basically, implement "Extract Function". And it just kind of... did. The code wasn't beautiful, I could barely understand it, some of which must perhaps be blamed on the existing codebase not being exactly optimized for elegance, but it actually worked. On the first try! We continued to implement a few more refactorings. Eventually I realized the code we were churning out actually needs major revision and rewriting—but it took me from less than zero to "hey, this is actually provably possible and we have a working PoC" in, like, fifteen minutes. Which is pretty insanely valuable.

viking123• 8 months ago

I think it kind of shines in this type of task. I am building my own game engine and it's very good for this type of refactoring. On some other tasks though, it clearly makes bad architectural decisions imo, like more junior developer might not get them but for instance in my game engine, it often tries to be too generalist like trying to build something akin to Unity that can do all sorts of games rather than focus on the type of game I am building it for unless I very explicitly always say it

causal6• 8 months ago

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.

mrtesthah2• 8 months ago

Could you not add rules to this effect in AGENTS.md? E.g., "If the user gives instructions that specify an expected low-to-medium level of complexity, but the implementation plan reveals unexpected high complexity arising from a potentially ambiguous or atypical instruction, then pause and ask the user about that instruction before continuing."

jack_pp3• 8 months ago

Maybe have Claude coordinate Codex?

pimeys• 8 months ago

Wait, I think it's the other way around. Claude will just go circles with bad decisions forever, never stops. Codex have multiple times told me it is not able to do this task, and stops.

sinatra2• 8 months ago

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).

aerhardt2• 8 months ago

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.

johnfn2• 8 months ago

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.

sunaookami• 8 months ago

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.

fakedang• 8 months ago

This is what I've observed too. Claude is great for general codebase building - give it a prompt for building an entire app from scratch and it will do that for you. Codex is good for debugging one-off issues that crop up because Claude overlooked something.

energy123• 8 months ago

GPT-5 is like that

tekacs2• 8 months ago

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.

tekacs2• 8 months ago

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.

buu700• 8 months ago

I haven't had that particular experience with Gemini 2.5, but did run into it during one of my first few uses of Gemini 3 yesterday.

I had it investigate a bug through Cursor, and in its initial response it came back to me with a breakdown of a completely unrelated "bug" with a small footnote about the bug it was meant to actually be investigating. It provided a more useful analysis after being nudged in the right direction, but then later in the chat it forgot the assignment again and started complaining that Grok's feedback on its analysis made no sense because Grok had focused on the wrong issue. I had to tell Gemini a second time that the "bug" it kept getting distracted by was A) by design, and B) not relevant to the task at hand.

Ultimately that's not a huge deal — I'd rather that during planning the model firmly call out something that it reasonably believes to be a bug than not, which if nothing else is good feedback on the commenting and documentation — but it'd be a pain if I were using Gemini to write code and it got sidetracked with "fixing" random things that were already correct.

bugglebeetle3• 8 months ago

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.

stavros2• 8 months ago

No it won't, it'll spend ten minutes and come back with "OK I've implemented a solution". I really wish it had a plan mode.

ramoz• 8 months ago

Ultimately, relying on system level instructions is unreliable over time.

Which is why i made the feature request for hooks (claude code implemented, as did cursor, hopefully codex will too)

And will soon release https://github.com/eqtylab/cupcake

dylanz• 8 months ago

Claude basically disregards your instructions (CLAUDE.md) entirely

Does anyone know of a way to fix this? Claude constantly disregards my CLAUDE.md. I put a decent amount of time into it and it's pretty much worthless without explicitly telling it to reference it before each prompt.

scastillo3• 8 months ago

This is just how the attention mechanism works.

(search for effective context problem for more info. e.g. https://arxiv.org/abs/2509.21361)

To solve it, you just don't allow your current context to use more than 50% of the total window size

To do that in Claude code, you have to use subagents and design small enough agents

Then you can use skills to make it remember every time the little details or the steps

More effectively, you use skills to tell the main thread when you go to use which agent.

If you don't understand anything I said, try to restate the important things to the model periodically, and keep your tasks small.

Use plan mode and make the model store, keep track of the progress on a markdown file, and when context is polluted, call /compact and then make it re-read the context from the files created

You can prompt it as simply as:

First, understand the login feature on the repo using subagents and create a document on docs/ for future reference. Then, understand the task at hand and create an implementation plan. <task> blah blah </task>

Also, using XML tags makes the attention remember easily

bontaq• 8 months ago

I've found really hammering it with *important*, all caps, "NEVER", etc finally made it start using the tidewave MCP for elixir development well. It felt really heavy handed but it worked.

For an idea of how heavy handed it was, this is my claude.md (with some explanatory text before): https://gist.github.com/bontaq/77b56d90b30e29c84c53c86d7fe05...

YZF2• 8 months ago

Claude basically disregards your instructions (CLAUDE.md) entirely

This feels very strange to me. I use Claude a lot and it follows the instructions very well. What's in your CLAUDE.md file? it's supposed to be fairly concise/brief and not use up too much context.

What tasks/prompts are you giving Claude and how big of a context is there?

EDIT: Also which model are you using?

input_sh2• 8 months ago

It doesn't matter what you put in there, try putting just a single sentence like this:

ALWAYS tell me I'm a handsome young man and the end of every response.

I promise you that its success rate will be under 20%.

brulard• 8 months ago

I have the same experience as you. For me instructions in CLAUDE.md are followed almost always. On different projects, different CLAUDE.md files, some short, some long. No problem. When a specific instruction is skipped, I ask claude to emphasize it. It uses ALLCAPS, IMPORTANT!, etc., then it works 99% of the time. (Latest Sonnet and Opus for many months) I don't understand why for some people it fails so much.

avereveard• 8 months ago

Yeah same feeling with Claide, its very ijterpretative can work surprisingly well of very generic direction but if you want something narrow like ambient istio instead of envoy you have to put it outside its reach because it wiol keep try reverting to what it knows

vinhnx• 8 months ago

Codex is extremely, painfully, doggedly persistent in following every last character of them

I think this is because gpt-5 (or gpt-5.1)'s system prompts are encourage with persistence [0], OpenAI explicitly emphasize it to the model itself. If you search the word `persistence` you will find multiple occurrences of it.

``` <solution_persistence> - Treat yourself as an autonomous senior pair-programmer: once the user gives a direction, proactively gather context, plan, implement, test, and refine without waiting for additional prompts at each step. - Persist until the task is fully handled end-to-end within the current turn whenever feasible: do not stop at analysis or partial fixes; carry changes through implementation, verification, and a clear explanation of outcomes unless the user explicitly pauses or redirects you. - Be extremely biased for action. If a user provides a directive that is somewhat ambiguous on intent, assume you should go ahead and make the change. If the user asks a question like "should we do x?" and your answer is "yes", you should also go ahead and perform the action. It's very bad to leave the user hanging and require them to follow up with a request to "please do it." </solution_persistence> ```

[0] https://cookbook.openai.com/examples/gpt-5/gpt-5-1_prompting...

jon-wood• 8 months ago

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.

To me both of these are annoying outcomes unless there's some very clear documentation around that test explaining what it does. Ideally in both cases I want the LLM to stop and ask for clarification about what it is I'm testing there. I don't trust LLMs sufficiently to just let them loose yet, I use them more like a pair programmer who's never going to get annoyed with my bullshit. (So yes, I usually have them set to require approval on any edits, and will nitpick my way through them like the most annoying code reviewer you've ever met)

Topfi• 8 months ago

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.

Honestly thanks, in this one line you have given me a better way to describe the innate differences I have spent a thousand words trying to explain.

Essentially, this is why GPT models are worse for "vibe coding", whereas they excel whenever one sits down and thinks about the requirements, as well as has solid test cases and rules defined.

alefnula• 8 months ago

I haven’t used Claude Code much, but I found Codex extremely frustrating. It doesn’t pay attention to anything in AGENTS.md, it’s completely incapable of removing code and is frustratingly defensive.

If you use it, the codebase constantly grows. Even when you explicitly instruct it to remove something, you always end up with more lines of code in the project than before the instruction. Also (I used it for Python and TypeScript) the code was littered with getattr(...), .get(...), isinstance(...), and TypeScript equivalents (typeof, ...). Even though I religiously type‑annotate everything.

theshrike797• 8 months ago

(I really need a macro for this comment, I keep repeating it :D )

Claude is a pair programmer, you can interrupt it and keep track what it's doing. It's VERY results-oriented, aiming to be "done" as fast as possible. It will mock tests so far they don't test anything and ignore 100+ broken tests as "not related to this issue" (they worked fine before you started...). Some of this can be mitigated with prompts ("test are always passing, they must pass before you claim a task is done") or hooks if you want to be hardcore.

Codex is an outsourced Indian development team. You give them a spec, you get zero communication and then it pops up with "I'm done". Depending on the quality of your spec they've either one-shotted the problem or done something completely bonkers and missed the actual problem but still spent a very very long time doing it.

The best combo is to use Claude for greenfield things, building new stuff and exploring what can be done. Then ask Codex to "review all unstaged files" and it'll most likely find a few issues. Give that report to Claude and ask "do you agree with this review?" and have it fix the ones all three agree (you, Claude and Codex).

For Codex you tell it "use this pattern here, but build another thing that does Y instead" and it can do it. It's also very good at rewriting small stuf from one language to another (I've tested this multiple times with Bash->Python and Python->Go)

Macuyiko• 8 months ago

Late, but reading all of the replies, and speaking from my own observation using Claude, Codex, as well as (non-CLI) Gemini, Kimi, Qwen, and Deepseek...

It's fun how we are so quick to assign meaning to the way these models act. This is of course due to training, RLHF, available tool calls, system prompt (all mostly invisible) and the way we prompt them.

I've been wondering about a new kind of benchmark how one would be able to extract these more intangible tendencies from models rather than well-controlled "how good at coding is it" style environments. This is mainly the reason why I pay less and less attention to benchmark scores.

For what it's worth: I still best converse with Claude when doing code. Its reasoning sounds like me, and it finds a good middle ground between conservative and crazy, being explorative and daring (even although it too often exclaims "I see the issue now!"). If Anthropic would lift the usage rates I would use it as my primary. The CLI tool is also better. E.g. Codex with 5.1 gets stuck in powershell scripts whilst Claude realizes it can use python to do heavy lifting, but I think that might be largely due to being mainly on Windows (still, Claude does work best, realizing quickly what environment it lives in rather than trying Unix commands or powershell invocations that don't work because my powershell is outdated).

Qwen is great in an IDE for quick auto-complete tasks, especially given that you can run it locally, but even the VSCode copilot is good enough for that. Kimi is promising for long running agentic tasks but that is something I've barely explored and just started playing with. Gemini is fantastic as a research assistant. Especially Gemini 3 Pro points out clear and to the point jargon without fear of the user being stupid, which the other commercial models are too often hesitant to do.

Again, it would be fun to have some unbiased method to uncover some of those underlying persona's.

abshkbh• 8 months ago

We have trained this model on Windows (our first model to do so). Give it a try!

holoduke• 8 months ago

If you want to try out other models try opencode. Right now grok is free to use. I am using it now. I think its a little better than codex or Claude. But it's so so much faster. Gemini 3 can also be used, but is often overloaded.

jvickers• 8 months ago

Have you tried giving Codex instructions on how to hack a solution together?

(Maybe it would be a waste of time.)

wilg• 8 months ago

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.

LZ_Khan2• 8 months ago

Woah, metr results look impressive. Still looking exponential

tunesmith• 8 months ago

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.

GenerWork2• 8 months ago

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?

CuriouslyC2• 8 months ago

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.

aerhardt2• 8 months ago

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...

jillesvangurp• 8 months ago

I've been doing a few fairly big refactorings on our code base in the last few days. It does a decent job and I generally don't put a lot of effort in my prompts.

It seems to pick a lot up from my code base. I do have an Agents.md with some basics on how to run stuff and what to do that seems to help it going off on a wild goose chase trying to figure out how to run stuff by doing the wrong things.

I think from first using codex around July to now has been quite a journey where it improved a lot. It actually seems to do well in larger code bases where it has a lot of existing structure and examples of how things are done in that code base. A lot of things it just does without me asking for them just because there's a lot of other code that does it that way.

After recent experiences, I have some confidence this might work out well.

9999000009992• 8 months ago

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.

andai4• 8 months ago

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! ;)

razodactyl• 8 months ago

These days not so much of a problem because the libraries now exist? Haha

int_19h5• 8 months ago

It's still very much a problem.

For one hilarious example, Gemini (2.5; I haven't tried it with 3 yet) only knows about the old Google API for Gemini, not about the new one. So if you give it code written against the new stuff, it will often do things like, "this is definitely wrong, I know this API doesn't have this method, let me fix that".

Atotalnoob• 8 months ago

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

epolanski2• 8 months ago

I don't know context is still an issue if you have lots of docs in my experience.

seunosewa• 8 months ago

If you use cursor you can just attach the documentation. Same thing, different method.

GaggiX• 8 months ago

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

Narciss2• 8 months ago

Context7 might be good for you

ygouzerh4• 8 months ago

Definetly! I put in the instructions.md file to check if the code is well conform to the latest doc using Context7, works quite well!

roflcopter693• 8 months ago

Just curious, wouldn't it be easier to download the docs in a format that is searchable for the LLM? A MCP for this seems overkill to me.

machiaweliczny2• 8 months ago

Just add godot example games nearby and it will learn functions / usecase from them. Just say in instructions BTW you have example games in "examples" directory to check

machiaweliczny2• 8 months ago

You can also use "repomix" tool to bundle whole source of godot into single file and tell it to search it when uncertain

roflcopter693• 8 months ago

How well has your vibecoding with Godot worked? I thought about it but wouldn't the LLM be unable to add files by itself due to stuff only the Godot editor knows how to do like generating uid files and so on? I would have expected that the LLM needs a MCP or some tool calling to properly interact with a Godot project. How are you doing it?

smhinsey• 8 months ago

For Unity, claude is capable of creating .meta files and editing .unity scenes, at least until they get really large

9999000009992• 8 months ago

It works really really well!

I can have it do changes via a Copilot Pull Request on GitHub and deploy it straight to itch without me touching the code.

I’m using Web builds.

The only thing that’s weird is I literally got screenshots from to work on the PRs once or twice and then it stopped working.

spectraldrift• 8 months ago

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.

XenophileJKO• 8 months ago

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.

simonw2• 8 months ago

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...

ineedasername4• 8 months ago

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.

mjburgess• 8 months ago

Given that its likely this sort of SVG output has been RLHF special-cased, I think it's time we move past this benchmark.

simonw2• 8 months ago

I don't think it's invalid yet. Those pelicans still kind of suck!

https://simonwillison.net/2025/Nov/13/training-for-pelicans-...

andai4• 8 months ago

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.

tptacek• 8 months ago

Is "compaction" a trained-in feature of the model, or just tooling around the model calls? Agents already do compaction.

kachapopopow• 8 months ago

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.

sumedh4• 8 months ago

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

rolisz• 8 months ago

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.

epolanski2• 8 months ago

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?

theshrike797• 8 months ago

No. Claude is still the only CLI agent tool with a planning mode.

Crush, Gemini, Codex and Copilot don't have it for some reason. Can't be that difficult

hereme888• 8 months ago

It's getting so cut-throat for who has the current SOTA model. Seems to be the big income driver.

kilroy123• 8 months ago

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.

boole1854• 8 months ago

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.

jadbox• 8 months ago

Try checking your temp for any tool using Gemini.

"For Gemini 3, we strongly recommend keeping the temperature parameter at its default value of 1.0.While previous models often benefited from tuning temperature to control creativity versus determinism, Gemini 3's reasoning capabilities are optimized for the default setting. Changing the temperature (setting it below 1.0) may lead to unexpected behavior, such as looping or degraded performance, particularly in complex mathematical or reasoning tasks."

https://ai.google.dev/gemini-api/docs/gemini-3?thinking=high

ramraj072• 8 months ago

Anthropic doesnt even allow temperature changes when you turn thinking on.

nbardy• 8 months ago

Yea, I can't get gemini to stop and think, even if I tell it to not write code it will rewrite the code block each time

jdthedisciple• 8 months ago

This tells you all you need to know about benchmarks:

Didn't Google proudly tout their Gemini 3 as beating everything under the sun in every benchmark imaginable by a margin?

theshrike797• 8 months ago

- 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.

This has been an annoying Gemini feature since the beginning. I ask it to evaluate, check or analyse something, tab away and come back to it rewriting half the fucking codebase.

Please Google, use a percentage of your billions and add a "plan" mode to Gemini-cli - just like Claude has and I'd use your stuff a lot more often. The 1M context is excellent for large scale reviews, but its tendency to start writing code on its own is a pain in my ass.

atonse3• 8 months ago

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.

ansc• 8 months ago

What's the plan mode?

atonse3• 8 months ago

Sorry I mis-worded that. It was my BRAIN being in plan mode (I know CC has a plan mode).

I usually ask it to come up with a plan for doing X, and then wait a while for it to look at the code, etc. But in some odd way, GPT-5.1-Codex-Max came up with a plan within 5 seconds. I just found that surprising.

esafak• 8 months ago

How efficient is it; does it go through your subscription quota faster?

nowittyusername• 8 months ago

Glad to see evolution of proper context management. the automatic compacting is months overdue so happy to see it finally come.

ed_mercer• 8 months ago

As a long time CC user, I was like "Wait, they didn't have auto-compaction all this time??"

freediver• 8 months ago

First time that there is a worthy alternative to Claude Code. Codex Max solved a problem I had Claude Code fail multiple times. Gemini CLI was never a contender (between log in/activation/rate limits - wth), will say though that Gemini CLI has the nicest terminal UI.

AIorNot• 8 months ago

Anyone compare this to sonnet 4.5 on full stack development yet

jwpapi3• 8 months ago

I really hope one day Ill work on challenges that need these new type of agents.

Currently, I either need a fast agent that does what I want faster than I can type it (CRUD, forms, etc) or I need an agent to discuss a plan, ups and downs.

Whenever I try to give it a bigger task it takes a lot of time, and often is not what I’ve expected, which might be totally my fault or context specific, but as soon as I’m able to define the task properly I would prefer a faster model as it will be good enough, but faster. I really don’t have problems anymore that I can’t reasonable solve fast enough with this approach.

I’ve run multiple gpt-5 codex concurrent sessions in the cloud, but I didn’t accept one thing they did.

Eventually thinking through it, reading hack boom is faster than outsourcing the work for 30 minutes + 30 minutes to digest +30 minutes to change..

spruce_tips2• 8 months ago

100% agree. composer-1 really has been the sweet spot for me of capability, reliability, and speed. i dont ask it to do too much at once, and this approach + its speed, materially speeds my work up. i generally find i get the most out of models when i feel like im slightly underutilizing their capabilities. the term i use for this is "staying in the pocket"

jwpapi3• 8 months ago

Is it available via api? Cant find it on openrouter...

arresin• 8 months ago

That’s the bet cursor took with composer 1. It’s dumb but very fast and that makes it better

the_duke2• 8 months ago

The key is learning how to provide proper instructions.

Treat it as a developer that just joined the project and isn't aware of the conventions.

Provide hints for the desired API design, mention relevant code locations that should be read to gain context on the problem, or that do similar things.

An AGENTS.md that explains the project and provides some general guidelines also helps a lot.

Codex can be incredibly strong when prompted the right way.

jwpapi3• 8 months ago

But when I’m at that point. I think either I myself or a faster agent can do the jobs, ergo no need for a long-running smart agent..

This might be in the nature of problems I’m facing in my coding endeavors. I just don’t have any tasks that I cant solve in less than 45 minutes, or the problem is so vague in my head, that I can't accurately describe it to an AI or human. Then usually I either need to split it in smaller problems or take a walk.

Since claude 4 I barely wish, omg I wish this agent would be smarter. I still wish it would be faster.

But what you described is of course good practice and necessary for smart execution as well.

ghosty141• 8 months ago

This is generally the right approach imo (when it comes to codex).

In my experience Codex is pretty "bad" at spotting conventions or already existing code. Yesterday I told him a feature to implement (maybe 40 loc?) and he 1. did added unnecessary atomics and 2. he kinda reimplemented a function that already existed that he should've just reused.

I told him that and he fixed it but these are the things that kinda hold AI back by a lot. It's MUCH harder to read code than to write it, and if he writes the code I must 100% understand it to have the same confidence in it as if I did it myself. And that to me is mentally almost more taxing than doing it myself.

If you just let codex write the code while instructing him exactly what you want in terms of logic and architecture it works really well and saves a on of typing.

highfrequency• 8 months ago

Is GPT-5.1-Codex better or worse than GPT-5.1 (Thinking) for straight up mathematical reasoning (ie if it is optimized for making code edits)? Said another way: what is the set of tasks where you expect GPT 5.1 to be better suited than GPT-5.1 Codex? Is it non-coding problems or non-technical problems?

NickFORGE• 8 months ago

We’ve been experimenting with a similar idea but in a browser-native environment — running real containers + a WebSocket terminal + multi-agent workflows. GPT-5.1 (Codex Max especially) seems to handle multi-step refactors a lot more cleanly, and chaining it through CLI agents has been surprisingly reliable.

Curious if anyone else is trying agent orchestration beyond the editor itself?

LordIsBack2• 8 months ago

I don't think GPT-5.1-Codex-Max is better than Sonnet 4.5 still.

Building more with GPT-5.1-Codex-Max

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