After using Claude for a long time, I tested Sol 5.6 for the first time today. Love it, its an incredibly capable model and uses far fewer tokens/time thinking. Its what I imagine Fable would be if I haven't been downgraded on every conversation - even after completing the verification program. I think I may cancel my Claude subscription finally.
I think Fable's dominance is overstated. It definitely has the lead, but quantifying what that lead actually is is really hard. I'm using GPT 5.6 Sol to do some shit that I personally would consider "crazy" - low level undocumented hardware driver alchemy, reverse engineering highly obfuscated code, even a bit of screwing around with a rendering engine in Vulkan, really just about the most complex tasks I can get any model to do, and it does great. For the more advanced stuff, it definitely needs the effort bumped. But even with the effort bumped, the token usage really doesn't seem to skyrocket too badly until at least you hit xhigh and max, which really only seem to be necessary if you are doing genuine crazy stuff, so it's not that bad. I did similar stuff with Fable. In fact, I went directly from an Anthropic subscription with Fable to an OpenAI subscription with Sol, more or less, and it really felt pretty seamless. If anything, I was thrilled to realize how much I actually preferred Codex CLI, to the point where I started using it at work too.
Fable seems to be generally more impressive at outputting one-shot web apps. I'm not really saying that to try to downplay what Fable can do, it's just that if I compare the two, this is one of the few definitely noticeable areas that you can easily demonstrate. Obviously, one-shotting programs is much better as a demonstration of a model's capabilities than it is practically useful (not that it is useless, but hopefully my point is understood).
However, whatever Fable truly is better at, one thing I really like about GPT 5.6 Sol is even harder to quantify: taste. GPT 5.6 Sol outputs are still LLM outputs and they contain many things that people would probably consider "Claude-isms" for better or worse, but overall I really prefer the GPT 5.6 Sol output. I find it to be generally more tasteful. Hard to quantify, but when talking to people I've had enough people seemingly agree with me to convince me that it really is true.
I used Sol to extract the remaining decryption keys from the Super Mario Maker 2 (Switch) game files. Someone had previously extracted all the keys from the original release, but not any of the new ones from updates. Not only did it succeed, but it helped me understand the data sufficiently to add support for “Super World” rendering to my level viewer (which I made back in 2021), eg the little widget at the top of https://www.smm2-viewer.com/players/B16-306-GVG
I was very pleasantly surprised to find Sol wasn’t obstructive over what was clearly a very grey area endeavour.
And Mai-Code-1.1-Flash seems like a really good cooperative player to GPT 5.6 Sol. You get Sol to help you make a detailed plan, and Mai codes it up and you can get pretty decent code out the other end without too many tokens if you are careful.
FYI I run it consistently in xhigh regardless of difficulty of the task at hand. I remember high being very fast, but I'd rather wait a bit more and get better output. AIs are insanely fast compared to me anyway, even on xhigh. Consumes more usage, but even at 100 EUR/m I don't hit limits.
After hitting the session limit on my company's plan so many times with Claude when I was using it, I mostly keep Codex on "high" rather than "xhigh" as a way to leave the tokens for my more ambitious coworkers. It's possible that having it higher might end up with better output, but so far at least I've yet to see a way to get any model to do 100% of what I need up front without any need for me to make changes that end up being more tedious to do via interaction than by hand, and it doesn't feel worth spending a bunch more tokens trying to figure out how to better communicate to it up front how the dominoes get set up so they fall in place properly the next time.
To be fair, I actually do run xhigh as my default. However, for the first time in my experience of trying and using LLMs, with Sol.. sometimes I feel confident enough to set the effort level to "Low". I just had Sol prototype some AWS stuff on low earlier. Great result, did exactly what I wanted.
things that are alchemical are rarely alchemy. That is to say things are very fiddly but stick a room of monkeys on typewriters, a schizophrenic developer with HolyC and adderall or an LLM, persistence is the key to many of these things like drivers, extracting keys from vintage security domains, etc. Dropping into xdd to a human is a chore, not for an LLM.
Although I am not exactly sure what you mean, I am not really claiming it is doing anything I couldn't do - but yes, it does so with much less effort. For example, I can have it set up probes and tracing on Linux that I personally would have to consult documentation to do. It might not even have to consult the documentation due to having the information on-tap, but even if it does, it's nothing that would cause it any fatigue, it's just going to keep moving forward in a loop until it is satisfied that it meets the criteria. I could've done all of this alone - I really could have. I just would not have. Being able to do something 10 times faster or with 10 times less effort is, in some senses, sometimes more impactful than being able to do entirely new things you couldn't do before.
This is a really good point that I definitely failed to grasp when first hearing about these tools. At least for me, the best way to use these tools is as a way to free myself from having to spend time thinking about the things that aren't worthwhile so I can focus on the things that truly are. I've had times in my life spending hours reading documentation and googling random things to try to tease out the correct sequence of commands or the exact right shape of an API to be able to make things work to know that it doesn't make me more productive to do that myself rather than point an LLM at the thing and let it spit out the answer after a few minutes. Meanwhile, I can spend that time thinking about what comes next, or what the correct way to take that one-off output and abstract it to something that can be used meaningfully in more flexible ways.
The only obvious objection I can think of to this line of thinking (at least from a technical perspective) is "how does someone build up the knowledge to be able to use a tool effectively in that way if not by doing things by hand at first?" The honest answer that is "I don't know, but that's also pretty much exactly the type of thing my employers have never been paying me to solve in the first place". Even just a decade into my career, there have already been plenty of times in my career I've struggle to convince people that we should do stuff in a way that won't bite us in the ass a month or two down the line, and in the times I've managed to succeed, it's usually only by putting in more of my own time and effort to make the initial investment seem more palatable. Luckily right now I'm not in one of those times when I'm having to go full throttle to keep the lights on a few months from now, but I don't have enough fuel in reserves to work on a plan for when we need to build a new rocket in another ten years. Maybe ask me next month.
Sol is way too eager to hone in on small details and ends up with massive over-engineering. Fable does it too - to be fair - but noticeably less.
After extensively using both on Max 20x plans, I've concluded that Fable is better for problem solving and coding, whereas Sol 5.6 Ultra shines in debugging specific issues: tackle a problem with Fable then leverage Sol to clean up, double check, or fix specific issues.
Fable (imo) had the edge on the $200 plan, but after this 50% reduction I'd say Codex is better value by far and there's no contest.
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Using Fable as the orchestrator and delegating tasks to Sol 5.6 Ultra via the codex plugin in Claude Code yielded good results, but still there was a lot more over-engineering (thus time and tokens spent) than Fable by itself would've done.
Both models suffer from doing-too-much. But both models are fundamentally really smart and knowledgeable. I think it's really close and pricing cuts really spice things up for us consumers! Sol is a clear winner in the value department and the $100 plan is enticing!
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*Claude Code usage is reducing by 33% in 2 days, Wednesday August 19... cmon anthropic: clau.de/cc-50-promo
*The "Max" I referred to was the plan tier, not the effort level btw
For small tasks, you can just use something like low or medium effort and it can usually avoid mistakes; after all, the model will test the code anyways and can do some baseline level of iterating.
In regards to cost, we need to acknowledge how generous OpenAI was in the last couple months with Codex usage credits (no weekly limits) and usage resets. It afforded me many a dive with Codex! Yes it uses more tokens, but sometimes it's worth it -- just depends on what you're working on.
Finally, Ultra(code) isn't that bad when it comes to cached tokens. I think folks overstate the general token usage of ultra effort on both providers.
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Both models are great at green-fielding a project when given detailed specs.
Both models overthink too liberally (imo) during these larger multi-shots. Sol overthinks more than Fable.
Both models are really smart and perform great for general knowledge and regular coding tasks.
It's great, don't get me wrong, but so is Fable. I'm just comparing the long-horizon task performance between the two at the same or similar effort levels.
Given the 50% discount on Sol and how smart it is, yeah it's unprecedented value. If you only want to use low effort, there's a clear winner here on value and it's not even close!
Fable feels less cumbersome to work with, but it is SO DAMN ANNOYING with the refusals that I'm leaning more and more on Sol, and very much looking forward to GPT6. Just seems like Anthropic is trying their hardest to ruin their reputation and user experience.
Sol has held stuff for a while to do the same sort of hazard checks I assume Fable is doing, but it always releases them. I think that's the better way to handle it rather than preventing me from seeing how far I can get generating schematics to use in Minecraft. Currently: a mostly normal voxel house.
Yeah at this point claude is overrated, overly expensive, weird writing style (elliptical), and the worst part is the aggressive guardrails that even normal convos get interrupted, meanwhile openAI is still I would say at the normal balance, if you ask something too obvious or direct it will stop you other than that, it work flawlessly, plus, I have yet to hit the limit despite heavily using it these past weeks.
Luna saw a huge jump after the price cut and is one of the more competitive models at the new price on openrouter.
Maybe they want to see how much market they can grab with Sol?
This might help but there are already cheaper models with Sol's intelligence more or less, the most notable being Grok 4.6 at $6/m which makes it a tougher sell
It's really only between Anthropic and OpenAI for many of my use cases, since I have a Zero Data Retention agreement with both. I'm not trusting random inference providers and especially not Elmo with sensitive data.
Actions speak louder than words. Anthropic has been shown in court to have no problem violating copyright law on a massive scale. Why do you trust them to uphold their side of the agreement when they willing to willfully violate the law repeatedly? How would you even know if they did retain your data?
or Tinfoil [0]? They serve open models with container integrity attested by Nvidia/AMD enclaves. Every cloud provider offers this of course, but not usually in a way that can be shared between distrusting users for economical inference. It still relies on the open-source containers being secure, and there's probably hardware sidechannels and stuff, but personally (ie privacy not liability) I trust it more than a contract
I used over a billion tokens per day of gpt-5.6 sol xhigh starting last Wednesday through Sunday before reaching my reset limit. The $200 pro plan is still the best deal.
Pretty basic. The codex app with one conversation per project and several running simultaneously all hours. I’m going for max caching that way and it never gets lost even with compaction somehow. Each has a plan with milestones to keep up to date and a thin agents file. I check in on them in the Remote app. Use case is protocol and control reverse engineering of audio hardware. I think they must be identifying the heavy use agent sessions and cranking up their cache lives so it’s not a big deal for them.
Ultra mode spins up many sub-agents. On a particularly challenging task, I’ve had as many as 29 agents working at one time.
Also if you don’t specify, most end up being the same as the parent model which is pretty wasteful.
I engineered a skill that spins up Terra High agents for most sub-agents, resorting to Sol Medium for technical research and Luna High for code/in-project research tasks.
On a slightly different topic, Luna Max is incredibly capable and doesn’t use as much quota (Luna tokens are dirt cheap).
Most importantly, are you seeing a return on investment for time and ultimate outcome?
No one can judge the enjoyment, learning, and hobby aspects. Just wondering if there is an end goal for that much overall expenditure (time, money, energy, etc.)
Some people just do crazy stuff. For example this now ex yc guy who said he has agents constantly scanning Sf govt apis and forming dashboards just because
Yeah I am, building large software with a vision - requirements - architecture - plan - code workflow. One Claude max account is enough to work on one, maybe two of those at a time (call it 15B tokens/month per project)
I’m exclusively using ultra and I run out in 3-4 days consistently. Those resets are great but I’ve noticed they like to cluster them at the start of the cycle, would be better if they spaced them out more.
I can’t sign up for that. I tried authorizing Codex a couple days ago. For some reason, their system says my phone number has been used for verification 3 times even though it definitely has not. I’ve had this phone number for over 20 years. OpenAI support is useless. They just keep repeating the policy without actually helping me.
Yes I filed a support ticket with them and explained that their system is broken and they just did not care. I explained how it was impossible for me to use it 3 times already as I've only made 2 chatgpt accounts EVER, and only recalling entering my phone number for one of the two chatgpt accounts. I told them that this issue locked me out of codex and chatgpt for work and they weren't willing to do anything about it. Totally useless support.
I ended up borrowing my gf's phone number just so I could get access for work. Ridiculous
Use TextVerified, load up like $5 of credit and OAI verification is like $1.00. Then when your account is made, ensure 2FA/passkey is setup then you don't need to worry about the phone number.
Historically those are less useful because some of the verification systems require a real phone number and that your name is associated with the account, depending on what and how they verify. It's annoying, I use a google voice number as my primary, and it often gets rejected.
A billion tokens per day?? Plausible estimates put the energy use at about 0.001 Wh/token, which means you're using 1000 kWh/day in electricity, just to generate slop. That's about the same as 50-100 houses. 300kg of CO2 per day - roughly the same as flying from London to New York every three days.
I think on average AI energy usage is not as big a deal as everyone is panicking about, but your usage is truly absurd and I don't know how you can live with that. It's immoral.
I can't speak for that guy, but I'm a physicist and work in clean energy... So it's not too hard! That said, I usually am closer to 10M on days I do heavy coding, so not nearly that bad.
Has anyone had mixed experience running Ultra with and without /goal?
I come back to it after 8 hours to find it got stuck navel gazing imagined and Byzantine errors.
I would in such a scenario expect the GPUs to be dumped to industrial breakers who would send them to China for refurbishment and repackaging before being sold again on Amazon, AliExpress, and Taobao as last gen gaming cards from weird brands and specs.
This is what happened after the great crypto GPU dumping.
Yeah, I ment it as a joke - I agree with you. Watched the Gamers Nexus GPU investigation recently, where they were shown how a chinese soldering shop can transplant GPU chips to a new board, including memory chip reuse.
Hopefully we can look forward to all that useless datacenter AI crap gets repurposed in a similar manner into something actually useful for users.
If they can cut the price of Sol by 50% and the price of Luna by 80%, then the original price might have carried a massive operating margin. They might still be serving the models at a profit after these price cuts, but we will never know.
I don’t think there’s a real answer for this. Margin depends on whatever number the accounting department wants to make up.
Do you include research and training costs? Of all models or only the ones being served? What percent of the R&D budget do you allocate to inference? What about data center capacity? Do you count future commitments? All the circular financing deals? Do you count employee equity grants as costs? At what valuation?
I saw this for Luna and then looked at the uptime and it said 85%. My interpretation is that this is just a gimmick where they serve the OpenAI flex tier at the same discount OpenAI provides for flex and then fall back to azure
You can train a LLM to inverse summarised thinking into thinking text. It’s not perfect, but it gets you maybe 80% of the quality with proper techniques.
FWIW, there’s not that much value protected here anyway IMHO, and even raw thinking text can lie (as shown by Anthropic’s amazing research), so for legitimate interpretability research it’s limited.
Scaling frontier performance hasn’t been SFT-bounded for a while now; it’s now basically how much you can scale RL rollouts.
No, this is OpenAI doing the discount, not Openrouter by themselves. OpenAI is crushing it with their 5.6 models, and they probably decided there was no better time to grab as much market share as possible.
I don’t understand this at all. They have never been profitable yet. How is this helping them? When it be more likely the case that not enough, people are using it as the prices they established already? So now they have to lower the prices?
I'm not having that experience. So far each major model update has been at least slightly better than the last, in ways I've found useful. Can't say it's perfect, or able to do exactly what I want without a decent amount of instruction/implementation/docs, but it's been useful enough to keep paying for it.
Not sure as OpenAI models (Sol, Luna,..) are also discounted on the Vercel AI Gateway rn. My bet is on OpenAI trying to drive more enterprise customers to their models through API.
You would probably get better results with Luna for the real simple tasks, or Sol with low thinking effort.
I find that I get exactly the effort that I asked for, which is pretty nice. The other side of that coin is that these are the least lazy models I’ve used so far. They will go on elaborate tangents to complete the task when I want them to.
I've found it's worse for simple tasks too, and I have to give it stricter guidelines, and sometimes it doesn't follow the same patterns I've grown to expect. I've found using 5.6 (sol) is good for diagnosing issues though, especially in terms of optimization of some given path
Yep. I have not yet had a single good experience with Sol or the 5.6 models on a variety of harnesses and configurations. It overthinks, overcomplicates and often makes my code into an unmaintainable sludge. It'll usually take 5+ turns of steering to get it in the right direction.
It's your responsibility to set an appropriate level of Thinking. For simple tasks, I use the instant model. As an approximation, the choice is proportional to the amount of time I want it spending on the task. Also, you can always ask it to respond succinctly.
Fable seems to be generally more impressive at outputting one-shot web apps. I'm not really saying that to try to downplay what Fable can do, it's just that if I compare the two, this is one of the few definitely noticeable areas that you can easily demonstrate. Obviously, one-shotting programs is much better as a demonstration of a model's capabilities than it is practically useful (not that it is useless, but hopefully my point is understood).
However, whatever Fable truly is better at, one thing I really like about GPT 5.6 Sol is even harder to quantify: taste. GPT 5.6 Sol outputs are still LLM outputs and they contain many things that people would probably consider "Claude-isms" for better or worse, but overall I really prefer the GPT 5.6 Sol output. I find it to be generally more tasteful. Hard to quantify, but when talking to people I've had enough people seemingly agree with me to convince me that it really is true.
I was very pleasantly surprised to find Sol wasn’t obstructive over what was clearly a very grey area endeavour.
The only obvious objection I can think of to this line of thinking (at least from a technical perspective) is "how does someone build up the knowledge to be able to use a tool effectively in that way if not by doing things by hand at first?" The honest answer that is "I don't know, but that's also pretty much exactly the type of thing my employers have never been paying me to solve in the first place". Even just a decade into my career, there have already been plenty of times in my career I've struggle to convince people that we should do stuff in a way that won't bite us in the ass a month or two down the line, and in the times I've managed to succeed, it's usually only by putting in more of my own time and effort to make the initial investment seem more palatable. Luckily right now I'm not in one of those times when I'm having to go full throttle to keep the lights on a few months from now, but I don't have enough fuel in reserves to work on a plan for when we need to build a new rocket in another ten years. Maybe ask me next month.
After extensively using both on Max 20x plans, I've concluded that Fable is better for problem solving and coding, whereas Sol 5.6 Ultra shines in debugging specific issues: tackle a problem with Fable then leverage Sol to clean up, double check, or fix specific issues.
Fable (imo) had the edge on the $200 plan, but after this 50% reduction I'd say Codex is better value by far and there's no contest.
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Using Fable as the orchestrator and delegating tasks to Sol 5.6 Ultra via the codex plugin in Claude Code yielded good results, but still there was a lot more over-engineering (thus time and tokens spent) than Fable by itself would've done.
Both models suffer from doing-too-much. But both models are fundamentally really smart and knowledgeable. I think it's really close and pricing cuts really spice things up for us consumers! Sol is a clear winner in the value department and the $100 plan is enticing!
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*Claude Code usage is reducing by 33% in 2 days, Wednesday August 19... cmon anthropic: clau.de/cc-50-promo
Is it more about just avoiding any mistakes? Seems like that would be costly when medium or high would work fine?
For small tasks, you can just use something like low or medium effort and it can usually avoid mistakes; after all, the model will test the code anyways and can do some baseline level of iterating.
In regards to cost, we need to acknowledge how generous OpenAI was in the last couple months with Codex usage credits (no weekly limits) and usage resets. It afforded me many a dive with Codex! Yes it uses more tokens, but sometimes it's worth it -- just depends on what you're working on.
Finally, Ultra(code) isn't that bad when it comes to cached tokens. I think folks overstate the general token usage of ultra effort on both providers.
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Both models are great at green-fielding a project when given detailed specs.
Both models overthink too liberally (imo) during these larger multi-shots. Sol overthinks more than Fable.
Both models are really smart and perform great for general knowledge and regular coding tasks.
Given the 50% discount on Sol and how smart it is, yeah it's unprecedented value. If you only want to use low effort, there's a clear winner here on value and it's not even close!
Also Opus 5 is fine if your codebase is simple.
I hear you on the downgrades, I'm 13/13 on downgrades, and last downgraded me to Sonnet for asking for reasoning chain.
They STILL don't have an option to "Sign in with Apple" on the website, but they do for Google??!? (and on iPhone of course)
Screw that asinine UX
(and no it wasn't better than Codex at this particular task)
Maybe they want to see how much market they can grab with Sol?
This might help but there are already cheaper models with Sol's intelligence more or less, the most notable being Grok 4.6 at $6/m which makes it a tougher sell
Court orders trump that ZDR agreement and OpenAI is under court order to retain data: https://hackernoon.com/openai-data-retention-court-order-imp...
[0] https://tinfoil.sh
For Mythos and even Fable they require prompt retention on their end.
edit: or more precisely if you want to access Mythos/Fable ZDR does not apply, and depending on config the exclusion can affect other models.
Their api pricing is absurdly expensive.
I assume at this point that it subsidizes subscriptions.
I've gotten more work done on a second chatgpt pro $100/mo subscription than I did with ~$150 of paying for usage through the app.
Also if you don’t specify, most end up being the same as the parent model which is pretty wasteful.
I engineered a skill that spins up Terra High agents for most sub-agents, resorting to Sol Medium for technical research and Luna High for code/in-project research tasks.
On a slightly different topic, Luna Max is incredibly capable and doesn’t use as much quota (Luna tokens are dirt cheap).
No one can judge the enjoyment, learning, and hobby aspects. Just wondering if there is an end goal for that much overall expenditure (time, money, energy, etc.)
I ended up borrowing my gf's phone number just so I could get access for work. Ridiculous
I think on average AI energy usage is not as big a deal as everyone is panicking about, but your usage is truly absurd and I don't know how you can live with that. It's immoral.
I’m guessing that Wh/token estimate is several orders of magnitude too high.
Leaked financial documents from 2025 show the company reported an operating loss of approximately $20.9 billion against $13.1 billion in revenue.
Fwiw I love K3 and use it as a daily driver. I haven't tried Sol, as I dislike OpenAI.
Overgrown datacenters or mounds of GPUs dumped into the harbour next ?
This is what happened after the great crypto GPU dumping.
Hopefully we can look forward to all that useless datacenter AI crap gets repurposed in a similar manner into something actually useful for users.
One person can use as many GPUs as they want.
Do you include research and training costs? Of all models or only the ones being served? What percent of the R&D budget do you allocate to inference? What about data center capacity? Do you count future commitments? All the circular financing deals? Do you count employee equity grants as costs? At what valuation?
Paper: https://arxiv.org/abs/2603.07267
FWIW, there’s not that much value protected here anyway IMHO, and even raw thinking text can lie (as shown by Anthropic’s amazing research), so for legitimate interpretability research it’s limited.
Scaling frontier performance hasn’t been SFT-bounded for a while now; it’s now basically how much you can scale RL rollouts.
I’d bet that explains this move!
OpenRouter attributes this promotion to OpenAI https://x.com/OpenRouter/status/2089416739398254662
What's the incentive here?
Open Responses API doesn't appear to support state management (yet)
I asked it to write a user todo and it turned out a four page essay. I gave the same task to 5.4 and got the small list of checkboxes I expected.
Then I switch models (to luna) before implementation. I find this combo nearly always does what I want.
I also use a skill called ponytail, its goal is to keep things terse and edits small. It may have contributed to the successes above.
I like that skills are easy to try out, too.
I find that I get exactly the effort that I asked for, which is pretty nice. The other side of that coin is that these are the least lazy models I’ve used so far. They will go on elaborate tangents to complete the task when I want them to.