> It's a damn good model. Not quite as "smart" as Fable, but it is incredibly capable. Fixed all the problems I had with GPT-5.5.
> It is incredibly determined. Will run for a day without even using a /goal. It understands subagents incredibly well and is great at orchestrating. It's super pleasant in use cases like OpenClaw and Hermes Agent. It knows iOS dev incredibly well.
> It has rough edges too, but FAR fewer than 5.5 did.
> For many things, gpt-5.6-sol will become my obvious defaults.
> It is better about [following instructions] than 5.5 was. Understands intent well and hammers until it gets there. Sometimes a bit too hard.
Also[^1]:
> gpt-5.6-sol is world leading in computer use. It made me use it 100x more. When we lost access to 5.6, I quickly started to go insane without it
I feel like listening to Theo about anything technical is like consulting a Labrador retriever for advice on quantum physics.
Every time I've ever seen one of his videos it's pretty clear he has very little understanding of development or engineering. I first became aware of him from his early "unit tests are a waste of time" stuff, and it seems his skillset is building a personal brand. Fair play, he's clearly talented at that, but that doesn't make his opinion on anything else worthwhile.
> it's pretty clear he has very little understanding of development or engineering
I cannot prove it but I have a feeling that you may be conflating "he clearly has different opinions on things I consider non-negotiable" to "he doesn't know what he's talking about".
I also watched a lot of his videos. I wildly disagree with him a lot of times, but he has his reasoning, and I can see (and verify!) that those ideas are coming from an engineering perspective.
The problem is that he's a tech influencer first, a tech expert second.
That's his motivation, influencing. Not teaching.
I'm not so against him as the previous commentor but I feel basically all YouTubers who have succeeded in building a brand have the same problem. They have to present their opinions as unassailable truth, they can't allow nuance. Within reason of course, they also have to play the game of appearing considerate and understanding of other people but it will always boil down to proving they are the real experts, their ultimate goal is always to get you watching more of their content.
If they don't do this, they appear less trustworthy and their brand wouldn't have grown as much as it did. They might genuinely have some expertise to share, and even contrary or downright wrong takes could teach us something if they were only presented in a way that encourages critical thinking. But as the person above said, their real deep expertise is in brand building.
I sort of disagree, the issue is that he like so many professionals (prime agent, being the other) becoming youtubers uses their experiences to make their opinion the only opinion when said opinion is nuanced or plain wrong objectively.
No he usually acknowledges other opinions (including the ones that I share) and tells from his perspective why they are wrong. It feels condescending when you see a face on screen, roasting what you think is right, but I personally could get over it and learn to take the bits that challenge my ideas.
Of course, youtube isn't interactive and when you see something that you think is objectively wrong, your options are writing a comment nobody will read or ignoring it, which is frustrating, but that, in my opinion, doesn't discredit the content producer itself.
You can make a counter argument video, which I've seen done, and then sometimes the original YouTuber will reply or collab. This happened when Theo had some truly awful takes on Flutter while knowing nothing about it, a prominent Flutter dev replied rebutting each point made, then they both collaborated to make a video where they switch technologies for a few hours to build something, and it was enlightening for both parties.
He's clearly very knowledgeable about some things, but I think he has harmed his credibility be becoming a 'tuber who prioritizes thumbnails and hot takes over engineering.
> he has demolished his credibility be becoming a 'tuber who prioritizes thumbnails and hot takes over engineering
I don't agree that he has demolished his credibility. I also dislike the youtube face and sensationalization but I personally don't hold it against him, given the Youtube algorithm.
Regardless of his style, I like hearing the take from an engineer who's working in a different country/culture and has a completely different perspective.
edit: it seems you changed "demolished" to "harmed". I still don't agree but it reads more defensible IMHO, thank you.
Not really. If you're a YouTuber it's necessary to follow the algorithm which includes making such a YouTuber face, clickbait actually works and has a direct financial correlation as Linus Tech Tips has shown.
If you are a competent engineer already why do you need to create self demeaning clickbait content on YouTube? Narcissism? I aggressively block any such content, because if you click on any of it, you easily get sent down the YouTube spiral of crap.
One can be multiple things. That you find it demeaning is your personal opinion and frankly is more a reflection on yourself. There are many like Casey Muratori that are competent engineers as well as YouTubers.
Casey (on his channels) does not do the clickbait thing, which makes all the difference. You can either maintain integrity and credibility or you can make brainrot shockface “it is over” content. There is no middle ground.
Maybe you are desensitized to it, but I have a carefully curated YouTube and I know that it can be a completely different platform experience if you reject with prejudice any such content.
He has big 'theatre kid' energy (at least certainly had, watched him years ago) - he desperately wants to make clear that there's a group of cool kids and he's in it.
His youtube channel used to be about talking about the new FOTM Javascript framework/technology - not presented as 'here's a cool thing, let's check it out' but 'everyone worth a damn already uses this, get with the times grandpa'
Sure. If his take was "100% unit test coverage is a waste of time" I think that's not unreasonable. You could make a case that the "you must write tests before you write code, every single time!" stuff is needlessly dogmatic. I also think that sometimes people focus too much on unit tests to the detriment of end to end tests that better model actual system interactions.
None of these were Theo's take. He was pushing the idea that unit tests in general were a waste of time because you could be shipping new features instead.
https://www.youtube.com/watch?v=pvBHyip4peo for an example of this. The nicest possible interpretation on this is that he's deliberately saying something he knows is wrong to attract attention.
Yep, I'm in full agreement. When extending functionality of some already existing code it also generally makes sense to write tests first.
I think the value is much lower (maybe even negative) when you're still trying to work out what shape the code will take, in an initial implementation.
Of course, as others have pointed out, nuanced opinion doesn't get clicks or YouTube views.
When I start using a chainsaw or a car I hope it has been tested (!) Without tests before delivery the one who tests is the end user. Disaster for a unreliable chainsaw, very unpleasant for a software.
But you're right, the goal is not to write test but to ensure delivery of a reliable software. However each software is a prototype, something that has never been made before (unlike a manufactured car or chainsaw) so the customer must be ready to some unexpected behaviors when the software is released.
Since tests are often sloppy or does not cover every edge case, I see a real value for GenAI. It also forces to write good spec: very clear about inputs and the invariants for each use case. I think that AI (especially GenAI) should first be a solution to existing problem, lack of tests and good specs is often one of them.
If you write too many preconditions, postconditions, invariants etc. Then you cement your software and you will spend most of your time on the tests rather than on the actual useful software
I looked at his YouTube, and found a stream of industry gossip and beginner content like "web dev tutorials". I have nothing against such content and it may be useful and good fun to watch.
But does that say anything about this particular model? People have been using models effectively for web code since Gpt 3.x.
> Not quite as "smart" as Fable, but it is incredibly capable.
THIS IS BECAUSE GPT-5.6 SOL IS... just a more posttrained version of GPT-5.5, not a brand new bigger model than GPT-5.5. It's not like how Mythos is bigger than Opus.
OpenAI switching to Sol/Terra/Luna renaming is just a way to rip off people and charge more usage for the same sized model.
GPT-5.6 --------> GPT-5.6 Sol
GPT-5.6-mini ---> GPT-5.6 Terra
GPT-5.6-nano ---> GPT-5.6 Luna
Except OpenAI is about to advertise GPT-5.6 Sol and GPT-5.6 Terra as a whole tier better, than if they named it GPT-5.6 and GPT-5.6-mini.
My feeling is that GPT-5.5 doesn't lack the raw intelligence so much as it lacks "methodology". I don't know how exactly to put it... how to approach a problem, how to take care of the details and side effects, how to handle unexpected difficulties and bugs, how to not spin out of control, how to write solid code, how to clean up afterwards, how to document, how to give useful feedback... the things that you learn on the job.
So, if they improved a lot in those areas, then GPT-5.6 could become a lot more useful compared to GPT-5.5 even though it might score lower in many benchmarks. It's possible but unlikely since their approach was mostly brute force in the past.
Is Fable really that much different? I almost instinctively create elaborate processes, workflows, set up a bunch of linters and dump research docs any time I bootstrap a new project regardless of what model I'm using. They all spiral out of control if they're not following a predefined process.
(Based purely on my feels of using both daily since forever)
Claudes are more creative and get shit done, suggesting and implementing stuff you didn’t ask for but actually kinda needed. Will leave gaps and bugs though. More of an artist, communicates a bunch during the dev process too.
GPT is the engineer, given exact specs it’ll disappear into its dark corner and putter away at doing exactly what was asked, nothing more nothing less. Very very good at spotting gaps from Claude’s get shit done code.
Yes it is. With Fable you don't need to create any sort of elaborate process, it seems to understand the user's intent much better than Opus class models.
Very. Fable 5 is incredibly efficient token wise, second only to GPT-5.5 and is far more affordable run-to-run than the pure input/ouput costs would suggest. Task adherence, task inference, tool calling and task assessment are all significantly ahead of GPT-5.5, especially as the later strongly degrades the second compaction comes into the mix, I suspect because of OpenAIs obsessive optimisation of reasoning tokens into a hard to read (and thus also hard to compact) mess.
Fable 5 meanwhile has a reliable 1m context window and compaction that the few times I did eval it does also do well. Not quite as easy to trust as GPT-5.4, but that's mainly because with thats 272k context window I simply got more familiar with GPT-5.4s incredibly dependable compaction.
Purely concerning encoded information wise, Fable 5 is near or on the same level as Gemini 3.1 Pro in my limited test set focused on those tasks, which in very niche cases can make a difference even with coding, but the truest advantage for coding assistance (besides frontend/UX) is that the code Anthropic models provide is more parsable. Hard to explain, but I can read, follow and mentally map Fable 5 (and even Opus 4.5-4.8) output far more than GPT-5.4 or GPT-5.5 code.
Task orchestration and (more importantly) knowing when to recommend against using such vs Opus 4.8 is another strength of Fable 5 I've use liberally, there is an understanding of what a tasks requirements and the most optimal setup for success are, I have not yet seen before. Computer use is also solid, albeit not as token efficient as GPT-5.5 for my limited use cases.
Lastly, I will say that the classifier has become far less intrusive for me compared to the initial release. During the previous launch window, on Claude.ai I triggered the classifier for simple frontend tasks for regular (not security vocabulary containing) webpages. Now that is no longer the case. Inside Claude Code I occasionally triggered the classifier previously, but after the re-release, I only managed one, even when working with a privacy focused section of the code base containing a significant number of code comments with security and privacy focused wording. That one instance was rectified quickly by trying again, so I really am having a hard time following how others experience the issues some describe. I do have routing to Opus 4.8 without confirmation by me deactivated too, simply because I want to know if it ever happens, so it's not that I missed reroutings.
That all being said, we are still far from a stage where I'd not want to review the output, but yes, I do rate Fable 5 very highly. GPT-5.5 can have a similar ceiling but long horizon has become less usable over GPT-5.4 and in either case, parsing their output is (far more) of a chore. Maybe post training can address some of this, hopeful on the compaction front myself. Also interested in what happened to OpenAI models on AWS Trainium, I was expecting that to be a major boon for their commercial adoption, but haven't heard anything since then...
On the post training front, I am still hopeful that the Gemini team can finally get tool calling and task adherence to an acceptable level as we do need every competitor possible and purely considering the information density the model was trained with, they have great potential.
I use Open AI and Claude a lot right like a lot everyday for hours multiple hours. Open AI gives much more value for money than Claude much more I'd say x 10. Mainly I use it for writing fiction books and literally Claude is locked 90% of everyday trying to jip me for tokens. It's not as good at coding for what I do which is a very complicated application. However it is very good at writing it's really good which is why I keep it right but over 90% maybe actually all of my work except the initial draft of a chapter is done by open AI.
Post-training can have big gains no? I don't think the current sizes at ~1T are saturated in intelligence (it's like saying AlphaGo Master is just a post-trained version of AlphaGo Lee)
No, GPTCyber is specifically trained for cybersecurity, and GPT-5.5-pro is just an ensemble of many subagents, not an actual model.
Mythos is simply a much bigger model in terms of parameters and I don't think OpenAI will have anything of its size anytime soon (My theory is that OpenAI had given up on scaling up parameters after GPT4.5 flopped).
> We generally treat GPT-5.5’s safety results as strong proxies for GPT-5.5 Pro, which is the same underlying model using a setting that makes use of parallel test time compute.
And Gemini also provides something similar. Gemini Deep Think models are pretty much the same thing [2]. As to why no other company uses this, I don't really know. Maybe compute constraints?
For some tasks, there is no amount of "steering" that will produce sensible code. The model needs to be sufficiently capable as a baseline; this is the "intent" that people are referring to with Fable.
That doesn’t sound like the “it hammers until it’s done”-type of intent.
Just last night Fable decided to get into a rabbit hole of debugging a database driver issue by packet sniffing the network traffic instead of just adding debug statements to the code. Definitely needed steering, and I don’t know many people whose first intuition would be to use pcap when they have a segfault.
I wouldn’t call it a recipe for disaster, but oh boy if you leave an agent that “hammers until it gets there” on its own with an underlying bug in a dependency…
Damn this is exciting. I love that gpt models are much faster, efficient and cheaper than Claude models. They are so fast even on high/xhigh that I don’t find myself using the parallel agent setup anymore much since its cognitively less demanding to just follow along what the model is doing and most tasks it will complete in <5-<10mins anyway.
This is because GPT-5.6 is just a more posttrained version of GPT-5.5, not a bigger model than GPT-5.5. It's not like how Mythos is bigger than Opus.
GPT-5.6 --------> GPT-5.6 Sol
GPT-5.6-mini ---> GPT-5.6 Terra
GPT-5.6-nano ---> GPT-5.6 Luna
Two important things to note, if you want to verify what I say/correct me:
GPT-5.6 Terra actually scores worse than GPT-5.5 on many benchmarks. It's not GPT-5.5 trained with more compute; it's basically GPT-5.6-mini that's been distilled from GPT-5.6 full size. Remember, GPT-5.4-mini had almost the same benchmarks as GPT-5.2 after all.
Opus 4.8 runs at ~90 tokens per second. Fable 5 runs at ~40 tokens per second on from Anthropic, because it's a bigger/slower model.
A few days after the release, when the dust dies down, look at how many tokens/second GPT-5.6 Sol is running at. I will bet it's the about same as GPT-5.5, and not half the speed. (OpenAI is not incentivized to slow down the model for paying customers). But the model tokens/sec will be a big clue- if OpenAI is charging more money for the same sized model or not.
not just that, but the entire industry spend several years seeking investment on the "pure" idea that they just need more compute and more parameters to reach AGI.
And the "business" obvious is still doing that but the science and implementation has be realizing that this just isn't true. They're not getting AGI out of a single LLM by itself.
right, but the business arm will always be dominant. I see what Chinese models are doing as the same as japanese car models in the 80s: producing smaller, more efficient products that address the realities of the "total addressable market" that no business model would support. They're, unfortunately, providing public value where the US and Europe used to tread.
There's a lot to make efficient, but it should be clear to everyone that just throwing compute at larger models isn't going to magically make it rain.
I’m bouncing back between Codex and Claude like a ping-pong ball. I much prefer the experience using Codex, less verbose and to-the-point I’ve found. But Fable, being as strong as it is, is a big draw for Claude right now. I’ll likely switch back to Codex if 5.6 Sol is comparable.
I personally like Codex much more as vscode integration. It's actually nice to use, looks great, handles images much, much better, can even send me images in the chat to see what it's doing(my side project is based heavily on image processing), and what's most important to me is that /steer actually works. When I type something to Claude mid-task, it'll maybe acknowledge that within next few minutes, although it seems to be actually quicker last few days but still takes a minute or two, whereas Codex will almost instantly read it, switch what it's doing or answer me. It feels much more polished in some ways(though usage page in settings almost never loads for me).
Same. For some reason late opus model are very superficial doing ux work and so am using gpt for that, but backend is much better engineered by claude, gpt prefer to duplicate everything it needs on the spot causing class sprawl
How are y'all carrying context history from one agent to the other?
I also flip between the models due to quota, TUI enhancements, model updates and service availability.
To handle this, I built a thing that normalizes your transcripts between Claude Code and Codex into a shared DB, then a CLI and skill.
It has made it so it doesn't matter what I built where (or when) I just refer to the work and drop in a /total-recall (or $total-recall on codex) and the agent brings it into the current convo.
I realize there are a lot of ~memory tools out there, but I think particular my approach and product behavior is unique.
Personally I just , in the orchestration loop, have all decisions be constantly reviewed and deliberated on and the decisions logged in a permanent way, that way everything is auditable, the model if needed can go back and look at why x or why decision was made or x or y tool used, and they're all labeled as D-1234 or whatever.
Plus I have it log the council discussions and always include provenance or the opinions so fable can go back after every major implementation and review how the orchestration loop could be improved. Basically have it log as much thinking in an organized compartmentalized way is better than any memory feature I've found though I haven't tried many. Auditable logs for every major decision, use 5.5 with reckless abandon (still have 3 resets myself).
Not claiming this is perfect but it has led to a very easy time of any fresh agent picking up the project. I also keep a task queue and project status and agent playbook that also get refined based off the logs of how a run went
How can be locked? If you have a proper agents in your project it will work out of the box with any model. I use codex and Hermes on same project with 0 issues. Skills, MCP and other features are useless imo.
My agent has access to glab with a user and can do whatever within permissions. No need a MCP. MCP maybe just for browser control.
5.6 Sol is extremely good, definitely Fable level from my experience. With 5.6 Sol being half the price and noticeably faster I think Anthropic will find the coming months unpleasant.
I would be absolutely stunned if this were really the case in general given how irresponsibly large Fable is, and 5.6 Sol most definitely is not. It depends on what your problems are though, I suppose, since there are those that swear Fable is at best a minor upgrade over Opus, which has not been my experience.
I know a few of my comments are related to this, but these new names are horrible. Why introduce ANOTHER layer of confusion and drop the mini, nano suffixes that people got used to?
How does this go through so many layers of management at a trillion dollar company without who has a say raising this? I simply can't believe how stupid the naming scheme from OpenAI was and continues to be even after they acknowledged it earlier.
And the names they came up with mirror the names OAI models come up with when I try to have them suggest codenames for projects. Just lacking any sort of imagination or coherence.
My theory is that they don't have Fable-class intelligence so they needed different hype vehicle :) This rename helps build excitement a bit more than just releasing ordinary GPT-5.6 increment.
Yet, in a month we'll be fine. We were fine with Anthropic naming models by music. I'm sure celestial bodies will be OK too. Larger = better. It's simple. As for the why? Marketing, making products feel "fresh", exciting, new, something alluring that we didn't have before. So, much like since industrialization.
What surprises me is not this, but that OpenAI changed things up without syncing with a GPT 6.
well I'm using mini models and GPT-5.4-mini is better than Gemini 3.1 Pro (considerably faster in exchange for less broad world knowledge) and 3.5 Flash (that one's trash by itself)
You are, sure, but I wonder how many weren't due to the name. In many people's minds, it's like, why even bother with a non frontier model? Especially if they're not paying for it, via their employers' subscriptions. This new OpenAI naming changes it though, they equate Sol to Fable so people will use Terra instead which has roughly 5.5 performance, thereby saving inference for OpenAI.
Honestly, "mini" and "nano" to me just seem like really awful names from a marketing perspective - they might as well call it "lobotomized crap version of GPT" and "even more lobotomized crap version of GPT".
Whereas Sol/Luna/Terra reads more like "GPT for hard/medium/basic problems".
I disagree. I needed a small text to json model that would parse basic info into a json, nothing else. I instantly knew to start with nano and if not good enough, use mini. This was obvious to me, just looking at the name. Now you have to actually know what those names mean. And I can guarantee you they'll add 5 more to create more hype, or make new names in whatever the next-gen-world-breaking-dangerous-model will be.
See, you are already confused: it's Sol/Terra/Luna, descending order by diameter. One is tempted to put the Sun and the Moon before the Earth, as those are "celestial".
So, Terra should work for most "down-to-earth" problems, Luna is for light-weight stuff (because less gravity?), and if you really want to burn tokens like there's no tomorrow, you go for the fusion furnace at the center of our solar system?
I mostly used GPT-Venti for the complex part, but the documentation was done by either GPT-Grande or GPT-Tall.
On a more serious note, I can vividly imagine how difficult it is to agree on a set of words that could plausibly suggest a relational meaning while remaining non-diminutive in every individual model name. Adding to the complexity, it is going to be used globally, and the main competitor already has an arguably successful, fabulous naming scheme.
Probably, but I think it's too little too late. Not much point to it if it's not permanent. The "get the most out of Fable until it goes away" frenzy is getting old fast. The cybersecurity blocks are very obnoxious too.
If OpenAI can launch a Fable tier model that's actually usable on a subscription, then Anthropic is just going to lose, and badly.
Agreed, this is one of the things I'm very surprised - one would think that a product like this is managed more consistently, but every few days there is another announcement or change in what the subscription can and can't do and to what extent.
Same also for the announced changes around `claude -p` and Agent SDK use that were backtracked
It's because Anthropic doesn't have capacity while OpenAI does. People clowned Altman a couple years ago because of the massive data center build out commitments but that has proved to be quite prescient. It is why Codex has much higher, almost unlimited limits, while Claude Code rate limits hourly and weekly much more.
Any previewers have hot takes? I've really preferred gpt-5.5 over Opus 4.8 for data analysis and scientific software work. It seems much more reliable. Fable is unusable for the type of work that I do (due to guardrails). Really looking forward to trying these new OpenAI models out.
Interesting to hear people like gpt-5.5. For me it feels smart only at one shot prompts, but if you try to build up session context before doing something it feels magnitudes inferior to Claude.
I'm almost sure its because the thinking of previous turns is stripped with the responses API, so if I tell it to analyse something deeply, what remains of the understanding in future turns is only the short response text of that analysis
For gpt-5.5 I build up that session context into a markdown file, and then I start a new session and give it the markdown file with the instructions for what to do.
I'm guessing this works better because it can always go back and re-analyze the saved context.
Do you have a source for this? I'm pretty sure responses api is only there to obfuscate reasoning, but that they're still keeping reasoning traces in the backend.
> Input and output tokens from each step are carried over, while reasoning tokens are discarded.
Keeping reasoning tokens around is better for caching and for remembering past insights, so you might reasonably wonder why we designed it this way. The main benefit of dropping reasoning tokens is that you can fit a lot more work inside the model's context window before you're forced into a slow and lossy compaction step. This was a larger consideration with our earlier reasoning models that had shorter context windows (~200k), longer thinking times (up to ~100k per message), and poor compaction. However, now that we've shipped longer context windows, we've trained our models think much more efficiently, and we've made compaction way better than it used to be, the balance of factors is changing. Tune in Thursday!
> fit a lot more work inside the model's context window before you're forced into a slow and lossy compaction step
This is something I never understood. Why the reasoning is not included until the context is full, then the reasoning stripped optionally to allow the conversation to continue. and only then when its truly full offer a compaction. Was it to optimize caching? Well I guess it doesn't matter now that you hinted that this choice was made because of prior limitations and may change very soon
> Why the reasoning is not included until the context is full, then the reasoning stripped optionally to allow the conversation to continue. and only then when its truly full offer a compaction.
Models are typically trained (at longer conversations/more turns) either with or without the reasoning still in the conversation. If you train a model with those, then using it without them, the model will perform a lot worse, same vice-versa if you train without but then end up using the model with them.
That's why you'll see some models have it and others don't, and trying to use them another way, will make them worse, they weren't trained like that.
So why aren't the models trained with both? I'm guessing that sort of permutation in the training would lead to double the amount of training time being needed, as you know effectively will have two variants of every session you train on, with and without the reasoning.
> thinking of previous turns is stripped with the responses API
Why do they store an encrypted reasoning payload in the session file and pass it to the API? Just a ruse? Reasoning isn’t even that many tokens, you think they’d degrade their model quality like that?
Reasoning messages would be lost immediately after a single tool call, unless you mean they sometimes go back and strip the reasoning channel retroactively, but that would increase costs via cache invalidation. I just don’t see any way it would make sense for them to do.
And wouldn’t this be noticeable by reasoning tokens not being accounted for in the context window usage?
For compiler work I found that Sol is noticably better than 5.5 (and I generally use OAI models because I like the Codex app), but Fable was still obviously better.
Better in what way? Does it follow the goals better, does the code produce have higher quality in a testable/maintainable sense or is it just closer to how you would usually program something?
I'm sorry to hear you are unable to use Fable; my partner is in the same boat and it frustrates her immensely to see what I've been able to do with it. As someone who is working with developing new linear algebra routines, Fable is so far ahead of GPT-5.5 and Opus that it's obscene. Massively better insights and far better at handling delicate corner cases without needing to mention them. I would be stunned if GPT-5.6 is at that level, but one can hope.
I've been running a custom enterprise agent on 5.4 and it's been very good so far. I am looking forward to trying it with the monster model to see if we can approach some additional business cases.
I think if you are not seeing reasonable performance in your agent loops as of 5.5, it's likely there is a deficit with how the loop, prompt or tools interact with the environment.
Coding with AI it feels like if you're not using the best model then you're possibly missing out - creating less capable, maintainable, just plain 'good' code. Why waste time using anything less than the best and cleaning up the mess later on. This is why I feel like local models and Chinese models aren't taking off (and Gemini/Grok) - they work, but they're plain just not as good as OpenAI/Anthropic. If you have the money then it doesn't make sense to code with anything else.
I’ve been using mostly deepseek v4, kimi k2.6, and gpt 5.3-codex
I sometimes chuck a few tokens to gpt 5.5 and opus 4.8 and they can sometimes solve a problem one of the other models couldn’t, but they’re not like 10x better or anything in my experience. More like 1.2x better
There are diminishing returns, especially for more mundane tasks. Fable is nice, and I bet Sol is also nice. But there really isn't much of a difference right now when using something beyond Opus or presumably Terra for most things. They're most useful when doing greenfield, highly complex/novel tasks. When Open Source catches up, it will be more widely adopted.
Yea, but by the time open source catches up, the frontier will be that much more capable and you won't want to waste time babysitting less capable models.
Another dimension for the fronteir to move in is speed. Codex has /fast which is great, but yea the bottleneck right now in many cases is just the time it takes these tools to complete tasks. I'm running many sessions in parallel just because I'm waiting for tasks to finish. I'm constantly round robin'ing them, and kicking them off on the next 20 minute task. If these models were faster I wouldn't need to context switch as much.
70s thru 90s computing and even into the early 2000s every new bit of computer meant new capabilities.
Eventually it plateaued and now you can do a decent chunk of your computing on something from 2012.
People keep saying scaling will top out, for example. But scaling keeps stubbornly refusing. New techniques keep coming along too. It's really still exploding into existence and every new generation brings new capability. Eventually it'll clear a ceiling for your key use cases and you'll stop worrying about new models.
It always pays to look back at history and see if you can pattern match.
Better code hands down. Actually GPT 5.5 is a good example of a model that's generally better at solving problems than Opus 4.8, but the code it generates is worse - over engineered, shortcuts, etc.. Fable does both, better code and solves problems, but it's also very expensive. Currently I use Opus mostly, Codex for code reviews because it is pedantic, and Fable for tough problems and high level design.
That depends entirely on how you're using AI. If you're getting it to do all the hard thinking, then sure using the best model is probably always going to be better. But it's also going to be expensive.
Using cheaper models and using your skills and expertise from the pre-AI era can get you working just as fast. You've gotta be more specific about the work you need doing. It's less "vibes" based, but they're still effective.
Also, Chinese models absolutely are taking off. I used Claude and GPT at work, and then I tried using some Chinese models for personal projects. I am 100% convinced they're like 90% as good for 10% of the cost. But you've basically gotta be a good developer first and know what you want and know when it's giving you shit.
Or maybe you are still iterating on a plan or spec file with Qwen 3.6 27B, while I implemented three features with Fable and QA tested them in the testing environment.
Of course, if you think that this approach is as fast and effective as "vibe coding" as in outsourcing more thinking to the AI, it is not surprising you would conclude the cheaper models were nearly as useful.
I don't know if you are right or not, a lot depends on the constraints of the project and team.
Some of the newer arguably now viable use cases, such as porting a large codebase to Rust, are certainly not going to be as fast with a more manual approach.
Is it cheaper than Codex for example? The problem with paying per token via API is it's not subsidized like subscriptions are, maybe Z AI one is though. But GLM doesn't have vision which is a deal breaker for many frontend or full stack tasks.
Honestly I'm on $200 a month for Claude Max and $100 a month for Codex, and it's nothing compared to the productivity gains if you're programming professionally. 10 bucks a day, I spend more for lunch. Time is money and I'm not going to waste time with a lesser model if I don't have to.
Yeah if you're a professional engineer it's a no brainer to buy these subs, even multiple subs, and you could replace another employee's salary especially if you're a solo founder working on your own product.
I'm most curious about whether OpenAI finally taught its models how to design interfaces. They have been behind the other labs in this area for what feels like ages.
I agree. Gemini actually is pretty good for isolated components too. But fable is much better at design than opus or gpt5.5. I have not seen as much difference elsewhere, but definitely design fable is great.
- everyone will get access Thursday (barring banned countries / individuals)
Historically, some companies and individuals have gotten alpha access before public launches, to give feedback and adapt their products to the new models. With GPT-5.6, some folks had early alpha access, but this was paused while the model was being evaluated and approved. Now, alpha access will be enabled for partners in the next two days before our wider launch.
I find codex way more usable. It’s not pretentiously verbose like Claude. It’s also responsive - I can see the progress easily and steer the conversation. With Claude, it might take 15 minutes and I would lose patience.
Both are verbose in their own way, and both - terrible. Claude models love to throw huge blobs of text in architecture planning / interview conversations, but in not a mentally draining language. OpenAI models are more compact, but very dense & formal - they will speak in RFC language for a button that clicks and submits a form.
I've seen this with GPT, and I usually ask it to put together a more easy to understand document for a specific target audience or reading level and it seems to do okay.
I held out on OpenAI until last month because I despise Sam Altman, but using Codex is a great experience and 5.5 (medium) I'm on 20$ is very capable, follows instructions when it should and confronts me/challenges me when it should.
UX is nicer where the agent is somehow "separated" from execution.
Earlier I predicted that Fable and Sol would be of similar capability, I think I will be wrong. Here is why: there is no indication that there are any classifiers like in Fable. I think OpenAI found out how to lobotomise the model without classifiers but the tradeoff is that it is a weaker model. I wonder how people feel about that. Would you like a highly intelligent jagged model with classifiers or slightly less intelligent smooth model without classifiers?
Based on the pricing I guess GPT 5.6 is the same size as GPT 5.5.
I would not be surprised if it is not as intelligent as the Mythos class models.
I have seen rumors that GPT 6 may release before September. The same person also claimed that a Fable 5.1 checkpoint has been completed a few weeks ago.
Sharing names with cryptocurrencies from four-ish years ago, the hype cycle is nearly complete.
Thoughts[^0] from Theo, who had early access:
> It's a damn good model. Not quite as "smart" as Fable, but it is incredibly capable. Fixed all the problems I had with GPT-5.5.
> It is incredibly determined. Will run for a day without even using a /goal. It understands subagents incredibly well and is great at orchestrating. It's super pleasant in use cases like OpenClaw and Hermes Agent. It knows iOS dev incredibly well.
> It has rough edges too, but FAR fewer than 5.5 did.
> For many things, gpt-5.6-sol will become my obvious defaults.
> It is better about [following instructions] than 5.5 was. Understands intent well and hammers until it gets there. Sometimes a bit too hard.
Also[^1]:
> gpt-5.6-sol is world leading in computer use. It made me use it 100x more. When we lost access to 5.6, I quickly started to go insane without it
[^0]: https://nitter.net/theo/status/2074708892341481755 [^1]: https://nitter.net/theo/status/2074720467395756499
I feel like listening to Theo about anything technical is like consulting a Labrador retriever for advice on quantum physics.
Every time I've ever seen one of his videos it's pretty clear he has very little understanding of development or engineering. I first became aware of him from his early "unit tests are a waste of time" stuff, and it seems his skillset is building a personal brand. Fair play, he's clearly talented at that, but that doesn't make his opinion on anything else worthwhile.
> it's pretty clear he has very little understanding of development or engineering
I cannot prove it but I have a feeling that you may be conflating "he clearly has different opinions on things I consider non-negotiable" to "he doesn't know what he's talking about".
I also watched a lot of his videos. I wildly disagree with him a lot of times, but he has his reasoning, and I can see (and verify!) that those ideas are coming from an engineering perspective.
The problem is that he's a tech influencer first, a tech expert second.
That's his motivation, influencing. Not teaching.
I'm not so against him as the previous commentor but I feel basically all YouTubers who have succeeded in building a brand have the same problem. They have to present their opinions as unassailable truth, they can't allow nuance. Within reason of course, they also have to play the game of appearing considerate and understanding of other people but it will always boil down to proving they are the real experts, their ultimate goal is always to get you watching more of their content.
If they don't do this, they appear less trustworthy and their brand wouldn't have grown as much as it did. They might genuinely have some expertise to share, and even contrary or downright wrong takes could teach us something if they were only presented in a way that encourages critical thinking. But as the person above said, their real deep expertise is in brand building.
I sort of disagree, the issue is that he like so many professionals (prime agent, being the other) becoming youtubers uses their experiences to make their opinion the only opinion when said opinion is nuanced or plain wrong objectively.
No he usually acknowledges other opinions (including the ones that I share) and tells from his perspective why they are wrong. It feels condescending when you see a face on screen, roasting what you think is right, but I personally could get over it and learn to take the bits that challenge my ideas.
Of course, youtube isn't interactive and when you see something that you think is objectively wrong, your options are writing a comment nobody will read or ignoring it, which is frustrating, but that, in my opinion, doesn't discredit the content producer itself.
You can make a counter argument video, which I've seen done, and then sometimes the original YouTuber will reply or collab. This happened when Theo had some truly awful takes on Flutter while knowing nothing about it, a prominent Flutter dev replied rebutting each point made, then they both collaborated to make a video where they switch technologies for a few hours to build something, and it was enlightening for both parties.
He's clearly very knowledgeable about some things, but I think he has harmed his credibility be becoming a 'tuber who prioritizes thumbnails and hot takes over engineering.
> he has demolished his credibility be becoming a 'tuber who prioritizes thumbnails and hot takes over engineering
I don't agree that he has demolished his credibility. I also dislike the youtube face and sensationalization but I personally don't hold it against him, given the Youtube algorithm.
Regardless of his style, I like hearing the take from an engineer who's working in a different country/culture and has a completely different perspective.
edit: it seems you changed "demolished" to "harmed". I still don't agree but it reads more defensible IMHO, thank you.
Not really. If you're a YouTuber it's necessary to follow the algorithm which includes making such a YouTuber face, clickbait actually works and has a direct financial correlation as Linus Tech Tips has shown.
If you are a competent engineer already why do you need to create self demeaning clickbait content on YouTube? Narcissism? I aggressively block any such content, because if you click on any of it, you easily get sent down the YouTube spiral of crap.
One can be multiple things. That you find it demeaning is your personal opinion and frankly is more a reflection on yourself. There are many like Casey Muratori that are competent engineers as well as YouTubers.
Casey (on his channels) does not do the clickbait thing, which makes all the difference. You can either maintain integrity and credibility or you can make brainrot shockface “it is over” content. There is no middle ground.
Maybe you are desensitized to it, but I have a carefully curated YouTube and I know that it can be a completely different platform experience if you reject with prejudice any such content.
He has big 'theatre kid' energy (at least certainly had, watched him years ago) - he desperately wants to make clear that there's a group of cool kids and he's in it.
His youtube channel used to be about talking about the new FOTM Javascript framework/technology - not presented as 'here's a cool thing, let's check it out' but 'everyone worth a damn already uses this, get with the times grandpa'
"Average Theo video be like": https://youtu.be/h1p9zdUtUdo
It's shocking how many accurate tropes this hits.
There's a simpler explanation. Social media rewards surprise and hype, not truth. Don't expect objectivity from someone who gets paid by the view.
And half his videos are him coming up with indirect ways of saying look how amazing I am.
There is a whole religion about tests that is worth attacking though
Sure. If his take was "100% unit test coverage is a waste of time" I think that's not unreasonable. You could make a case that the "you must write tests before you write code, every single time!" stuff is needlessly dogmatic. I also think that sometimes people focus too much on unit tests to the detriment of end to end tests that better model actual system interactions.
None of these were Theo's take. He was pushing the idea that unit tests in general were a waste of time because you could be shipping new features instead.
https://www.youtube.com/watch?v=pvBHyip4peo for an example of this. The nicest possible interpretation on this is that he's deliberately saying something he knows is wrong to attract attention.
Tests before code makes sense when fixing bugs. Red-green specifically.
1: get bug
2: write tests that should work, but don’t because of bug
3: fix bug
4: confirm fix by running tests
Makes things a LOT easier for people checking the PR, they can just confirm the tests are correct pretty much.
As a bonus the same bug can’t surface again.
Oh I do that naturally as my rational problem investigation. Sometimes you can’t write a test for that, you need to test it yourself
Yep, I'm in full agreement. When extending functionality of some already existing code it also generally makes sense to write tests first.
I think the value is much lower (maybe even negative) when you're still trying to work out what shape the code will take, in an initial implementation.
Of course, as others have pointed out, nuanced opinion doesn't get clicks or YouTube views.
When I start using a chainsaw or a car I hope it has been tested (!) Without tests before delivery the one who tests is the end user. Disaster for a unreliable chainsaw, very unpleasant for a software.
But you're right, the goal is not to write test but to ensure delivery of a reliable software. However each software is a prototype, something that has never been made before (unlike a manufactured car or chainsaw) so the customer must be ready to some unexpected behaviors when the software is released.
Since tests are often sloppy or does not cover every edge case, I see a real value for GenAI. It also forces to write good spec: very clear about inputs and the invariants for each use case. I think that AI (especially GenAI) should first be a solution to existing problem, lack of tests and good specs is often one of them.
If you write too many preconditions, postconditions, invariants etc. Then you cement your software and you will spend most of your time on the tests rather than on the actual useful software
i already found his clear shilling of nextjs a bit distasteful, but his whole gpt-5 thing really just made it clear he's just not worth listening to.
> Thoughts[^0] from Theo, who had early access:
I looked at his YouTube, and found a stream of industry gossip and beginner content like "web dev tutorials". I have nothing against such content and it may be useful and good fun to watch.
But does that say anything about this particular model? People have been using models effectively for web code since Gpt 3.x.
Increased tenacity & goal following is exactly what I want in this model, to make it compete with Claude models.
(A little toning down of the goblin fetish would be nice too, haha.)
> Not quite as "smart" as Fable, but it is incredibly capable.
THIS IS BECAUSE GPT-5.6 SOL IS... just a more posttrained version of GPT-5.5, not a brand new bigger model than GPT-5.5. It's not like how Mythos is bigger than Opus.
OpenAI switching to Sol/Terra/Luna renaming is just a way to rip off people and charge more usage for the same sized model.
GPT-5.6 --------> GPT-5.6 Sol
GPT-5.6-mini ---> GPT-5.6 Terra
GPT-5.6-nano ---> GPT-5.6 Luna
Except OpenAI is about to advertise GPT-5.6 Sol and GPT-5.6 Terra as a whole tier better, than if they named it GPT-5.6 and GPT-5.6-mini.
My feeling is that GPT-5.5 doesn't lack the raw intelligence so much as it lacks "methodology". I don't know how exactly to put it... how to approach a problem, how to take care of the details and side effects, how to handle unexpected difficulties and bugs, how to not spin out of control, how to write solid code, how to clean up afterwards, how to document, how to give useful feedback... the things that you learn on the job.
So, if they improved a lot in those areas, then GPT-5.6 could become a lot more useful compared to GPT-5.5 even though it might score lower in many benchmarks. It's possible but unlikely since their approach was mostly brute force in the past.
Is Fable really that much different? I almost instinctively create elaborate processes, workflows, set up a bunch of linters and dump research docs any time I bootstrap a new project regardless of what model I'm using. They all spiral out of control if they're not following a predefined process.
(Based purely on my feels of using both daily since forever)
Claudes are more creative and get shit done, suggesting and implementing stuff you didn’t ask for but actually kinda needed. Will leave gaps and bugs though. More of an artist, communicates a bunch during the dev process too.
GPT is the engineer, given exact specs it’ll disappear into its dark corner and putter away at doing exactly what was asked, nothing more nothing less. Very very good at spotting gaps from Claude’s get shit done code.
Yes it is. With Fable you don't need to create any sort of elaborate process, it seems to understand the user's intent much better than Opus class models.
There's been a ton of discussion on HN about this but yes. It's a totally different level from Opus.
Very. Fable 5 is incredibly efficient token wise, second only to GPT-5.5 and is far more affordable run-to-run than the pure input/ouput costs would suggest. Task adherence, task inference, tool calling and task assessment are all significantly ahead of GPT-5.5, especially as the later strongly degrades the second compaction comes into the mix, I suspect because of OpenAIs obsessive optimisation of reasoning tokens into a hard to read (and thus also hard to compact) mess.
Fable 5 meanwhile has a reliable 1m context window and compaction that the few times I did eval it does also do well. Not quite as easy to trust as GPT-5.4, but that's mainly because with thats 272k context window I simply got more familiar with GPT-5.4s incredibly dependable compaction.
Purely concerning encoded information wise, Fable 5 is near or on the same level as Gemini 3.1 Pro in my limited test set focused on those tasks, which in very niche cases can make a difference even with coding, but the truest advantage for coding assistance (besides frontend/UX) is that the code Anthropic models provide is more parsable. Hard to explain, but I can read, follow and mentally map Fable 5 (and even Opus 4.5-4.8) output far more than GPT-5.4 or GPT-5.5 code.
Task orchestration and (more importantly) knowing when to recommend against using such vs Opus 4.8 is another strength of Fable 5 I've use liberally, there is an understanding of what a tasks requirements and the most optimal setup for success are, I have not yet seen before. Computer use is also solid, albeit not as token efficient as GPT-5.5 for my limited use cases.
Lastly, I will say that the classifier has become far less intrusive for me compared to the initial release. During the previous launch window, on Claude.ai I triggered the classifier for simple frontend tasks for regular (not security vocabulary containing) webpages. Now that is no longer the case. Inside Claude Code I occasionally triggered the classifier previously, but after the re-release, I only managed one, even when working with a privacy focused section of the code base containing a significant number of code comments with security and privacy focused wording. That one instance was rectified quickly by trying again, so I really am having a hard time following how others experience the issues some describe. I do have routing to Opus 4.8 without confirmation by me deactivated too, simply because I want to know if it ever happens, so it's not that I missed reroutings.
That all being said, we are still far from a stage where I'd not want to review the output, but yes, I do rate Fable 5 very highly. GPT-5.5 can have a similar ceiling but long horizon has become less usable over GPT-5.4 and in either case, parsing their output is (far more) of a chore. Maybe post training can address some of this, hopeful on the compaction front myself. Also interested in what happened to OpenAI models on AWS Trainium, I was expecting that to be a major boon for their commercial adoption, but haven't heard anything since then...
On the post training front, I am still hopeful that the Gemini team can finally get tool calling and task adherence to an acceptable level as we do need every competitor possible and purely considering the information density the model was trained with, they have great potential.
I use Open AI and Claude a lot right like a lot everyday for hours multiple hours. Open AI gives much more value for money than Claude much more I'd say x 10. Mainly I use it for writing fiction books and literally Claude is locked 90% of everyday trying to jip me for tokens. It's not as good at coding for what I do which is a very complicated application. However it is very good at writing it's really good which is why I keep it right but over 90% maybe actually all of my work except the initial draft of a chapter is done by open AI.
God save us from these ai generated fiction books.
> OpenAI switching to Sol/Terra/Luna renaming is just a way to rip off people and charge more money for the same sized model.
Excuse me, but what are you on about?
Unless I'm mistaken, they have literally(1) stated that it will cost $5 per 1M tokens in, and $30 for 1M output tokens. The same as GPT-5.5.
[1] https://openai.com/index/previewing-gpt-5-6-sol/
Sounds like the same problem as condom. Nobody want to buy the "mini" size.
Post-training can have big gains no? I don't think the current sizes at ~1T are saturated in intelligence (it's like saying AlphaGo Master is just a post-trained version of AlphaGo Lee)
What is the language of those words (sol, terra, luna). It does not seem to be a single language.
Spanish: Sol, tierra, luna
Italian: Sole, terra, luna
Catalan: Sol, terra, lluna
Portuguese: Sol, terra, lua
Might as well call it gelatto, siesta, fiesta if they think it sounds cool.
As you may have guessed, it's Latin.
I wonder what those languages all stem from...
What a tantalising mystery there is here waiting to be solved!
Latin.
OpenAI already has a Mythos level model, it's called GPTCyber and before that, it was called gpt-5.5-pro.
No, GPTCyber is specifically trained for cybersecurity, and GPT-5.5-pro is just an ensemble of many subagents, not an actual model.
Mythos is simply a much bigger model in terms of parameters and I don't think OpenAI will have anything of its size anytime soon (My theory is that OpenAI had given up on scaling up parameters after GPT4.5 flopped).
how do you know gpt-5.5-pro is an ensemble? if it is, then how did OpenAI do it? why no other company has been able to pull it off?
It's pretty much confirmed by OpenAI here [1].
> We generally treat GPT-5.5’s safety results as strong proxies for GPT-5.5 Pro, which is the same underlying model using a setting that makes use of parallel test time compute.
And Gemini also provides something similar. Gemini Deep Think models are pretty much the same thing [2]. As to why no other company uses this, I don't really know. Maybe compute constraints?
[1] https://deploymentsafety.openai.com/gpt-5-5
[2] https://deepmind.google/models/gemini/deep-think/
Plenty of other companies do this. Meta Muse Spark has a "Contemplating" which is this. Kimi had this on their website too, IIRC.
This guy is enthusiastic about everything. Not a great benchmark
That's sad to see Sol not beating Fable as it was explicitly stated by OpenAI that Sol benchmarks and overall performance were better than Fable.
“Understands intent well and hammers until it gets there. “
If there’s anything I learned over the past 12-18 months is that this is a recipe for disaster, except for throwaway stuff.
I thought most senior engineers settled on the fact that steering a model yields much better results?
For some tasks, there is no amount of "steering" that will produce sensible code. The model needs to be sufficiently capable as a baseline; this is the "intent" that people are referring to with Fable.
That doesn’t sound like the “it hammers until it’s done”-type of intent.
Just last night Fable decided to get into a rabbit hole of debugging a database driver issue by packet sniffing the network traffic instead of just adding debug statements to the code. Definitely needed steering, and I don’t know many people whose first intuition would be to use pcap when they have a segfault.
I wouldn’t call it a recipe for disaster, but oh boy if you leave an agent that “hammers until it gets there” on its own with an underlying bug in a dependency…
It's very possible that would be the best strategy over the last 12-18 months and now that this is released it is no longer the best strategy.
That would be an extremely massive leap if agents could suddenly make nuanced architectural decisions and prevent technical debt.
In my experience even Fable still requires guidance (although the options it provides are generally better).
here is the original x post
https://x.com/theo/status/2074708892341481755
5.6 sol seems to hit a lot of the gaps with 5.5
sucks its not "mythos" but i will take it
Next up: Thoughts from an OnlyFans model.
Damn this is exciting. I love that gpt models are much faster, efficient and cheaper than Claude models. They are so fast even on high/xhigh that I don’t find myself using the parallel agent setup anymore much since its cognitively less demanding to just follow along what the model is doing and most tasks it will complete in <5-<10mins anyway.
This is because GPT-5.6 is just a more posttrained version of GPT-5.5, not a bigger model than GPT-5.5. It's not like how Mythos is bigger than Opus.
GPT-5.6 --------> GPT-5.6 Sol
GPT-5.6-mini ---> GPT-5.6 Terra
GPT-5.6-nano ---> GPT-5.6 Luna
Two important things to note, if you want to verify what I say/correct me:
GPT-5.6 Terra actually scores worse than GPT-5.5 on many benchmarks. It's not GPT-5.5 trained with more compute; it's basically GPT-5.6-mini that's been distilled from GPT-5.6 full size. Remember, GPT-5.4-mini had almost the same benchmarks as GPT-5.2 after all.
Opus 4.8 runs at ~90 tokens per second. Fable 5 runs at ~40 tokens per second on from Anthropic, because it's a bigger/slower model. A few days after the release, when the dust dies down, look at how many tokens/second GPT-5.6 Sol is running at. I will bet it's the about same as GPT-5.5, and not half the speed. (OpenAI is not incentivized to slow down the model for paying customers). But the model tokens/sec will be a big clue- if OpenAI is charging more money for the same sized model or not.
> This is because GPT-5.6 is just a more posttrained version of GPT-5.5, not a bigger model than GPT-5.5.
What is this very confident assumption based on?
If it were fully trained from scratch, you'd expect to see a major version bump. The other point releases have been fine tunes or post trains.
but 5.5 was also no major version bump
bigger doesn't mean better, chill out
That's true but size of LLMs has been strongly correlated with their "intelligence".
not just that, but the entire industry spend several years seeking investment on the "pure" idea that they just need more compute and more parameters to reach AGI.
And the "business" obvious is still doing that but the science and implementation has be realizing that this just isn't true. They're not getting AGI out of a single LLM by itself.
They clearly know it but have to pretend otherwise to keep the money flowing.
right, but the business arm will always be dominant. I see what Chinese models are doing as the same as japanese car models in the 80s: producing smaller, more efficient products that address the realities of the "total addressable market" that no business model would support. They're, unfortunately, providing public value where the US and Europe used to tread.
There's a lot to make efficient, but it should be clear to everyone that just throwing compute at larger models isn't going to magically make it rain.
In this case, it does actually.
I’m bouncing back between Codex and Claude like a ping-pong ball. I much prefer the experience using Codex, less verbose and to-the-point I’ve found. But Fable, being as strong as it is, is a big draw for Claude right now. I’ll likely switch back to Codex if 5.6 Sol is comparable.
I personally like Codex much more as vscode integration. It's actually nice to use, looks great, handles images much, much better, can even send me images in the chat to see what it's doing(my side project is based heavily on image processing), and what's most important to me is that /steer actually works. When I type something to Claude mid-task, it'll maybe acknowledge that within next few minutes, although it seems to be actually quicker last few days but still takes a minute or two, whereas Codex will almost instantly read it, switch what it's doing or answer me. It feels much more polished in some ways(though usage page in settings almost never loads for me).
That's an IDE difference not a model difference, it's called steering vs queuing.
Same. For some reason late opus model are very superficial doing ux work and so am using gpt for that, but backend is much better engineered by claude, gpt prefer to duplicate everything it needs on the spot causing class sprawl
How are y'all carrying context history from one agent to the other?
I also flip between the models due to quota, TUI enhancements, model updates and service availability.
To handle this, I built a thing that normalizes your transcripts between Claude Code and Codex into a shared DB, then a CLI and skill.
It has made it so it doesn't matter what I built where (or when) I just refer to the work and drop in a /total-recall (or $total-recall on codex) and the agent brings it into the current convo.
I realize there are a lot of ~memory tools out there, but I think particular my approach and product behavior is unique.
If you're open to giving it a try, I'd appreciate any feedback: https://contextify.sh recent show hn: https://news.ycombinator.com/item?id=48777790
> how are yall carrying context...
Personally I just , in the orchestration loop, have all decisions be constantly reviewed and deliberated on and the decisions logged in a permanent way, that way everything is auditable, the model if needed can go back and look at why x or why decision was made or x or y tool used, and they're all labeled as D-1234 or whatever.
Plus I have it log the council discussions and always include provenance or the opinions so fable can go back after every major implementation and review how the orchestration loop could be improved. Basically have it log as much thinking in an organized compartmentalized way is better than any memory feature I've found though I haven't tried many. Auditable logs for every major decision, use 5.5 with reckless abandon (still have 3 resets myself).
Not claiming this is perfect but it has led to a very easy time of any fresh agent picking up the project. I also keep a task queue and project status and agent playbook that also get refined based off the logs of how a run went
Sounds similar to the tool I built: https://github.com/neilberkman/ccrider#ccrider (although it's OSS whereas yours appears to be commercial)
Same here. Late to agentic party. 1st month was Codex, 2nd Claude, already thinking 3rd will be Codex again! Since Fable will be gone soon.
Why not both? T3 Code exists.
Hadn't heard about it. Launching either "codex" or "claude" from cli isn't exactly a pain point I was looking to fix.
Edit: I guess the real answer is that I don't want multiple subscriptions. I use one for a month, and then decide if I switch to the other.
Nice try theo
theo's alt account
How? I'm pretty much locked into Claude Code and even if gpt models are good now, the experience with codex CLI has been so bad I won't go back to it.
e.g., it still doesn't have /revise or /undo!
I've never used /revise or /undo, everything I do is just through natural language and seems to work great.
Use the Codex desktop app.
Pi.dev makes GPT models a lot smarter, the codex harness isn’t very good
How can be locked? If you have a proper agents in your project it will work out of the box with any model. I use codex and Hermes on same project with 0 issues. Skills, MCP and other features are useless imo.
My agent has access to glab with a user and can do whatever within permissions. No need a MCP. MCP maybe just for browser control.
5.6 Sol is extremely good, definitely Fable level from my experience. With 5.6 Sol being half the price and noticeably faster I think Anthropic will find the coming months unpleasant.
I would be absolutely stunned if this were really the case in general given how irresponsibly large Fable is, and 5.6 Sol most definitely is not. It depends on what your problems are though, I suppose, since there are those that swear Fable is at best a minor upgrade over Opus, which has not been my experience.
What type of work is this for in your experience?
How do you have access?
OpenAI said in their initial blog post that certain organizations, probably big tech, have early preview access.
I know a few of my comments are related to this, but these new names are horrible. Why introduce ANOTHER layer of confusion and drop the mini, nano suffixes that people got used to?
How does this go through so many layers of management at a trillion dollar company without who has a say raising this? I simply can't believe how stupid the naming scheme from OpenAI was and continues to be even after they acknowledged it earlier.
Because they want a Haiku, Sonnet, Opus equivalent I guess?
Exactly, and I think the tipping point was how many times “Fable” was mentioned the last weeks
Then they should have just called it Fairytale
Confabulation
Perchance
You can't just say perchance.
And the names they came up with mirror the names OAI models come up with when I try to have them suggest codenames for projects. Just lacking any sort of imagination or coherence.
The funny thing is they had the decision to go with some less or more pretentious...
My theory is that they don't have Fable-class intelligence so they needed different hype vehicle :) This rename helps build excitement a bit more than just releasing ordinary GPT-5.6 increment.
I think OpenAI can also use the naming playbooks of current Intel and AMD of 2000s.
Possibilities are endless:
Sounds nice, looks cool. Why not?Or NVIDIA.
OpenAI GptForce 5600 XT
You can go far beyond that:
OpenAI GptForce FX6800TI Founders Edition, for example.
> OpenAI can also use the naming playbooks of current Intel
Past Intel had way better naming.
It'd just be OpenAI GPT 5.6++++++++++++++
Marketing... that is all
Yet, in a month we'll be fine. We were fine with Anthropic naming models by music. I'm sure celestial bodies will be OK too. Larger = better. It's simple. As for the why? Marketing, making products feel "fresh", exciting, new, something alluring that we didn't have before. So, much like since industrialization.
What surprises me is not this, but that OpenAI changed things up without syncing with a GPT 6.
(At least in Europe) Toyota used to name their models "Sol", "Luna" and "Terra" as well. Sol had all the trimmings, Terra had the least.
It sounded nicer than something like "Luxury" and "Basic".
> Sol had all the trimmings, Terra had the least.
That’s interesting. My understanding is that:
Sol = Sun Terra = Earth Luna = Moon
So it’s a bit surprising that in Toyota’s nomenclature, Terra is the basic trim instead of Luna.
Nano and mini, which is smaller? This is a bit more clear imo. It also helps for expectations, bigger isn't necessarily better for all use cases.
(Well given the limited amount of things we can deduce from a name)
> Nano and mini, which is smaller?
mini > micro (see, e.g., -skirts, -computers)
micro > nano (see, SI)
so, mini > nano.
If you ever find out, let me know. My department at work has been renamed so many times, I don't even know our current name.
Because people weren't using the mini and nano models. Like someone else said those are awful marketing names.
well I'm using mini models and GPT-5.4-mini is better than Gemini 3.1 Pro (considerably faster in exchange for less broad world knowledge) and 3.5 Flash (that one's trash by itself)
You are, sure, but I wonder how many weren't due to the name. In many people's minds, it's like, why even bother with a non frontier model? Especially if they're not paying for it, via their employers' subscriptions. This new OpenAI naming changes it though, they equate Sol to Fable so people will use Terra instead which has roughly 5.5 performance, thereby saving inference for OpenAI.
so many layers of management
Sounds like the source of your problem, right there.
Honestly, "mini" and "nano" to me just seem like really awful names from a marketing perspective - they might as well call it "lobotomized crap version of GPT" and "even more lobotomized crap version of GPT".
Whereas Sol/Luna/Terra reads more like "GPT for hard/medium/basic problems".
I disagree. I needed a small text to json model that would parse basic info into a json, nothing else. I instantly knew to start with nano and if not good enough, use mini. This was obvious to me, just looking at the name. Now you have to actually know what those names mean. And I can guarantee you they'll add 5 more to create more hype, or make new names in whatever the next-gen-world-breaking-dangerous-model will be.
See, you are already confused: it's Sol/Terra/Luna, descending order by diameter. One is tempted to put the Sun and the Moon before the Earth, as those are "celestial".
So, Terra should work for most "down-to-earth" problems, Luna is for light-weight stuff (because less gravity?), and if you really want to burn tokens like there's no tomorrow, you go for the fusion furnace at the center of our solar system?
It's the same issue with American and ISO date formats again. I mean, it's 8/7/2026 today, what's the problem?
I mostly used GPT-Venti for the complex part, but the documentation was done by either GPT-Grande or GPT-Tall.
On a more serious note, I can vividly imagine how difficult it is to agree on a set of words that could plausibly suggest a relational meaning while remaining non-diminutive in every individual model name. Adding to the complexity, it is going to be used globally, and the main competitor already has an arguably successful, fabulous naming scheme.
It sounds like a PR minefield.
I think GPT 5.6 sol is pretty slow. I went back to 5.5
Though its been just 3 days I started using.
Half way through the chat, GPT 5.6 Sol stops and does a safety verification, pretty annoying
Is this the reason Anthropic extended use of Fable 5 via subscriptions until July 12? Seems a bit like it
Probably, but I think it's too little too late. Not much point to it if it's not permanent. The "get the most out of Fable until it goes away" frenzy is getting old fast. The cybersecurity blocks are very obnoxious too.
If OpenAI can launch a Fable tier model that's actually usable on a subscription, then Anthropic is just going to lose, and badly.
Agreed, this is one of the things I'm very surprised - one would think that a product like this is managed more consistently, but every few days there is another announcement or change in what the subscription can and can't do and to what extent.
Same also for the announced changes around `claude -p` and Agent SDK use that were backtracked
It's because Anthropic doesn't have capacity while OpenAI does. People clowned Altman a couple years ago because of the massive data center build out commitments but that has proved to be quite prescient. It is why Codex has much higher, almost unlimited limits, while Claude Code rate limits hourly and weekly much more.
Mirror: https://xcancel.com/OpenAI/status/2074704958419792299
Any previewers have hot takes? I've really preferred gpt-5.5 over Opus 4.8 for data analysis and scientific software work. It seems much more reliable. Fable is unusable for the type of work that I do (due to guardrails). Really looking forward to trying these new OpenAI models out.
Interesting to hear people like gpt-5.5. For me it feels smart only at one shot prompts, but if you try to build up session context before doing something it feels magnitudes inferior to Claude. I'm almost sure its because the thinking of previous turns is stripped with the responses API, so if I tell it to analyse something deeply, what remains of the understanding in future turns is only the short response text of that analysis
For gpt-5.5 I build up that session context into a markdown file, and then I start a new session and give it the markdown file with the instructions for what to do.
I'm guessing this works better because it can always go back and re-analyze the saved context.
Do you have a source for this? I'm pretty sure responses api is only there to obfuscate reasoning, but that they're still keeping reasoning traces in the backend.
I work at OpenAI and can confirm that's correct: reasoning tokens are discarded after each new user turn (though not after each message or tool call).
Our docs show a diagram here:
https://developers.openai.com/api/docs/guides/reasoning
> Input and output tokens from each step are carried over, while reasoning tokens are discarded.
Keeping reasoning tokens around is better for caching and for remembering past insights, so you might reasonably wonder why we designed it this way. The main benefit of dropping reasoning tokens is that you can fit a lot more work inside the model's context window before you're forced into a slow and lossy compaction step. This was a larger consideration with our earlier reasoning models that had shorter context windows (~200k), longer thinking times (up to ~100k per message), and poor compaction. However, now that we've shipped longer context windows, we've trained our models think much more efficiently, and we've made compaction way better than it used to be, the balance of factors is changing. Tune in Thursday!
> fit a lot more work inside the model's context window before you're forced into a slow and lossy compaction step
This is something I never understood. Why the reasoning is not included until the context is full, then the reasoning stripped optionally to allow the conversation to continue. and only then when its truly full offer a compaction. Was it to optimize caching? Well I guess it doesn't matter now that you hinted that this choice was made because of prior limitations and may change very soon
> Why the reasoning is not included until the context is full, then the reasoning stripped optionally to allow the conversation to continue. and only then when its truly full offer a compaction.
Models are typically trained (at longer conversations/more turns) either with or without the reasoning still in the conversation. If you train a model with those, then using it without them, the model will perform a lot worse, same vice-versa if you train without but then end up using the model with them.
That's why you'll see some models have it and others don't, and trying to use them another way, will make them worse, they weren't trained like that.
So why aren't the models trained with both? I'm guessing that sort of permutation in the training would lead to double the amount of training time being needed, as you know effectively will have two variants of every session you train on, with and without the reasoning.
dude what the hell
> thinking of previous turns is stripped with the responses API
Why do they store an encrypted reasoning payload in the session file and pass it to the API? Just a ruse? Reasoning isn’t even that many tokens, you think they’d degrade their model quality like that?
Reasoning messages would be lost immediately after a single tool call, unless you mean they sometimes go back and strip the reasoning channel retroactively, but that would increase costs via cache invalidation. I just don’t see any way it would make sense for them to do.
And wouldn’t this be noticeable by reasoning tokens not being accounted for in the context window usage?
For compiler work I found that Sol is noticably better than 5.5 (and I generally use OAI models because I like the Codex app), but Fable was still obviously better.
Better in what way? Does it follow the goals better, does the code produce have higher quality in a testable/maintainable sense or is it just closer to how you would usually program something?
I'm sorry to hear you are unable to use Fable; my partner is in the same boat and it frustrates her immensely to see what I've been able to do with it. As someone who is working with developing new linear algebra routines, Fable is so far ahead of GPT-5.5 and Opus that it's obscene. Massively better insights and far better at handling delicate corner cases without needing to mention them. I would be stunned if GPT-5.6 is at that level, but one can hope.
It seems comparable to Fable to me in my uses.
What types of use cases?
That's great to hear - and for the same price as 5.5, and reportedly with much lower token use per task.
Were you able to try Sol Ultra?
No, my organization limits access to xhigh.
Interesting, data analysis work is the only thing I’ll use Gemini for
Will it be available on subscription tiers? That will get me to switch away from Anthropic.
I've been running a custom enterprise agent on 5.4 and it's been very good so far. I am looking forward to trying it with the monster model to see if we can approach some additional business cases.
I think if you are not seeing reasonable performance in your agent loops as of 5.5, it's likely there is a deficit with how the loop, prompt or tools interact with the environment.
Coding with AI it feels like if you're not using the best model then you're possibly missing out - creating less capable, maintainable, just plain 'good' code. Why waste time using anything less than the best and cleaning up the mess later on. This is why I feel like local models and Chinese models aren't taking off (and Gemini/Grok) - they work, but they're plain just not as good as OpenAI/Anthropic. If you have the money then it doesn't make sense to code with anything else.
I’ve been using mostly deepseek v4, kimi k2.6, and gpt 5.3-codex
I sometimes chuck a few tokens to gpt 5.5 and opus 4.8 and they can sometimes solve a problem one of the other models couldn’t, but they’re not like 10x better or anything in my experience. More like 1.2x better
There are diminishing returns, especially for more mundane tasks. Fable is nice, and I bet Sol is also nice. But there really isn't much of a difference right now when using something beyond Opus or presumably Terra for most things. They're most useful when doing greenfield, highly complex/novel tasks. When Open Source catches up, it will be more widely adopted.
Yea, but by the time open source catches up, the frontier will be that much more capable and you won't want to waste time babysitting less capable models.
Another dimension for the fronteir to move in is speed. Codex has /fast which is great, but yea the bottleneck right now in many cases is just the time it takes these tools to complete tasks. I'm running many sessions in parallel just because I'm waiting for tasks to finish. I'm constantly round robin'ing them, and kicking them off on the next 20 minute task. If these models were faster I wouldn't need to context switch as much.
70s thru 90s computing and even into the early 2000s every new bit of computer meant new capabilities.
Eventually it plateaued and now you can do a decent chunk of your computing on something from 2012.
People keep saying scaling will top out, for example. But scaling keeps stubbornly refusing. New techniques keep coming along too. It's really still exploding into existence and every new generation brings new capability. Eventually it'll clear a ceiling for your key use cases and you'll stop worrying about new models.
It always pays to look back at history and see if you can pattern match.
Does Fable write better code or just can solve more problems?
Better code hands down. Actually GPT 5.5 is a good example of a model that's generally better at solving problems than Opus 4.8, but the code it generates is worse - over engineered, shortcuts, etc.. Fable does both, better code and solves problems, but it's also very expensive. Currently I use Opus mostly, Codex for code reviews because it is pedantic, and Fable for tough problems and high level design.
The way I'd describe it, Fable is the first model that's good enough.
If all I had was Fable for the next couple of years then I'd be totally fine with that. I have never felt that about any version of Opus.
That’s the way I feel about GPT 5.2
That depends entirely on how you're using AI. If you're getting it to do all the hard thinking, then sure using the best model is probably always going to be better. But it's also going to be expensive.
Using cheaper models and using your skills and expertise from the pre-AI era can get you working just as fast. You've gotta be more specific about the work you need doing. It's less "vibes" based, but they're still effective.
Also, Chinese models absolutely are taking off. I used Claude and GPT at work, and then I tried using some Chinese models for personal projects. I am 100% convinced they're like 90% as good for 10% of the cost. But you've basically gotta be a good developer first and know what you want and know when it's giving you shit.
Or maybe you are still iterating on a plan or spec file with Qwen 3.6 27B, while I implemented three features with Fable and QA tested them in the testing environment.
Of course, if you think that this approach is as fast and effective as "vibe coding" as in outsourcing more thinking to the AI, it is not surprising you would conclude the cheaper models were nearly as useful.
I don't know if you are right or not, a lot depends on the constraints of the project and team.
Some of the newer arguably now viable use cases, such as porting a large codebase to Rust, are certainly not going to be as fast with a more manual approach.
OpenCode + GLM has become a daily driver for me. With the z.ai subscription it’s super cheap too.
I’d describe it as something between Sonnet and Opus.
Is it cheaper than Codex for example? The problem with paying per token via API is it's not subsidized like subscriptions are, maybe Z AI one is though. But GLM doesn't have vision which is a deal breaker for many frontend or full stack tasks.
Honestly I'm on $200 a month for Claude Max and $100 a month for Codex, and it's nothing compared to the productivity gains if you're programming professionally. 10 bucks a day, I spend more for lunch. Time is money and I'm not going to waste time with a lesser model if I don't have to.
Yeah if you're a professional engineer it's a no brainer to buy these subs, even multiple subs, and you could replace another employee's salary especially if you're a solo founder working on your own product.
I'm most curious about whether OpenAI finally taught its models how to design interfaces. They have been behind the other labs in this area for what feels like ages.
Yes. I'm really happy with frontend design of Sol (and it does scale down well to Terra!). Definitely a step change on design.
What do you feel is the best model for interface design right now?
For me claude is the best, hands down. Fable took it a step even further.
I agree. Gemini actually is pretty good for isolated components too. But fable is much better at design than opus or gpt5.5. I have not seen as much difference elsewhere, but definitely design fable is great.
Will it be restricted as heavily as Fable? Will it come to CodeX?
My quota is about to reset. Really can’t wait to use it.
But only for a small percentage of world's population, right?
Waiting.....
The question is, launch to who …
To everyone.
"We’re expanding preview access globally now." Preview access? Not as straightforward as "launching on thursday".
I believe what the post meant to communicate is:
- alpha testers will start getting access now
- everyone will get access Thursday (barring banned countries / individuals)
Historically, some companies and individuals have gotten alpha access before public launches, to give feedback and adapt their products to the new models. With GPT-5.6, some folks had early alpha access, but this was paused while the model was being evaluated and approved. Now, alpha access will be enabled for partners in the next two days before our wider launch.
(I work at OpenAI.)
I find codex way more usable. It’s not pretentiously verbose like Claude. It’s also responsive - I can see the progress easily and steer the conversation. With Claude, it might take 15 minutes and I would lose patience.
Both are verbose in their own way, and both - terrible. Claude models love to throw huge blobs of text in architecture planning / interview conversations, but in not a mentally draining language. OpenAI models are more compact, but very dense & formal - they will speak in RFC language for a button that clicks and submits a form.
So claude: 10 paragraphs of prose
codex: 1 paragraph of jargon over jargon.
I've seen this with GPT, and I usually ask it to put together a more easy to understand document for a specific target audience or reading level and it seems to do okay.
I held out on OpenAI until last month because I despise Sam Altman, but using Codex is a great experience and 5.5 (medium) I'm on 20$ is very capable, follows instructions when it should and confronts me/challenges me when it should.
UX is nicer where the agent is somehow "separated" from execution.
Earlier I predicted that Fable and Sol would be of similar capability, I think I will be wrong. Here is why: there is no indication that there are any classifiers like in Fable. I think OpenAI found out how to lobotomise the model without classifiers but the tradeoff is that it is a weaker model. I wonder how people feel about that. Would you like a highly intelligent jagged model with classifiers or slightly less intelligent smooth model without classifiers?
Based on the pricing I guess GPT 5.6 is the same size as GPT 5.5.
I would not be surprised if it is not as intelligent as the Mythos class models.
I have seen rumors that GPT 6 may release before September. The same person also claimed that a Fable 5.1 checkpoint has been completed a few weeks ago.
...so is it a good idea to use up all my Codex quota by Thursday in the hopes of a reset to promote GPT 5.6?
Honestly they sound like pokemon game names.
Probably some Pokemon names might be inspired by planet names in Latin, yes.