> 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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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!
Well, obviously its true as you can see in the silbing comment. But still interesting how experiences differ. I wonder did you ever use codex in more demanding workflows, because that at least something like that is going on is obvious if you use it for a few hours
> 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.
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.
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 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.
- 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.
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?
> 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
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.
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.
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.
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'
It's shocking how many accurate tropes this hits.
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.
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.
(A little toning down of the goblin fetish would be nice too, haha.)
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.
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.
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/
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?
[1] https://deploymentsafety.openai.com/gpt-5-5
[2] https://deepmind.google/models/gemini/deep-think/
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
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?
In my experience even Fable still requires guidance (although the options it provides are generally better).
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.
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
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.
e.g., it still doesn't have /revise or /undo!
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.
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.
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.
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.
If OpenAI can launch a Fable tier model that's actually usable on a subscription, then Anthropic is just going to lose, and badly.
I'm guessing this works better because it can always go back and re-analyze the saved context.
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!
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?
Were you able to try Sol Ultra?
So claude: 10 paragraphs of prose
codex: 1 paragraph of jargon over jargon.
UX is nicer where the agent is somehow "separated" from execution.
- 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.)