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.
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.
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).
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.
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.
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.
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.
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
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.
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.
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. It depends on whatever number the accounting department wants to make up.
Do you include research costs? Of all models or only specific ones? What percent of the R&D budget do you allocate to model serving? What about data center capacity? Do you count future commitments? All the circular financing deals? Employee equity grants?
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.
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.
Fable is still the best there is. Sol close second but I find it gets way to stuck on details.
Also Opus 5 is fine if your codebase is simple.
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.
I'm also in Anthropic Cyber Verification Program, but they specifically exclude Fable, just goes up to Opus 5.
I hear you on the downgrades, I'm 13/13 on downgrades, and last downgraded me to Sonnet for asking for reasoning chain.
I recently tried Claude again after several months, to see if it was any better at something Codex has been struggling with…
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)
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.
I spent $800 in a few hours when my sub maxed out because I was trying to get something done and had a long car ride to let it churn.
Their api pricing is absurdly expensive.
> Their api pricing is absurdly expensive.
I assume at this point that it subsidizes subscriptions.
A billion a day? How many agents are you running?
I have mine churning like butter and I'm rarely hitting a billion tokens per day, what's your workflow look like?
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.
A billion tokens a day is 11,000 tokens a second sustained. How many tokens per second are you getting off of GPT 5.6 Sol per project?
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).
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
Is anyone hitting caps without agents or API usage? Seems very difficult.
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.
Get a burner and use it? If you're spending $200/mo on something, $40 or whatever for a burner phone seems like a pretty cheap price.
For context, Stripe has just acquired OpenRouter for >$7B.
I’d bet that explains this move!
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.
Why would the potential acquisition have anything to do with this? They do discounts all the time on various models. Luna was 50% off last week..
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.
plenty of companies offer ZDR and are just as random as OpenAI and Anthropic in their age
Since when does Grok 4.6 have Sol 5.6's intelligence? I don't believe it.
I do have free sol and cursor ultra for 200 I prefer grok over sol, they are equally capable but grok is faster
Why not?
Because it’s good on benchmarks but not on real usage?
But OP said they've never used it. How would they know?
It doesn't.
I wonder if xAI is A/B testing routing some difficult grok 4.6 queries to Sol to seed some true believers.
Price wars did wonders for many businesses, like the bike sharing industry in China.
Overgrown datacenters or mounds of GPUs dumped into the harbour next ?
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.
I wouldnt be so hopeful because they dont use commodity hardware afaik How are you going to use a h100 at home?
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
Is this motivated by the value of the thinking traces gleaned from the traffic?
They can’t decrypt the thinking traces.
I'm loving this race to the bottom.
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.
GP means race to the bottom in price not quality.
Does OpenRouter eat this cost to get their hands on a copy of the conversations people are using with the model?
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.
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. It depends on whatever number the accounting department wants to make up.
Do you include research costs? Of all models or only specific ones? What percent of the R&D budget do you allocate to model serving? What about data center capacity? Do you count future commitments? All the circular financing deals? Employee equity grants?
OpenAI didn't cut the price of Sol by 50% like they did with Luna's 80%. Sol was unchanged. This is just a limited promo for OpenRouter non-BYOK.
No ZDR. No dice.
The title looks to be misleading, since this price cut is limited to OpenRouter. It does not apply for the native OpenAI price listed at https://developers.openai.com/api/docs/models/gpt-5.6-sol
Right? Should we switch from direct OpenAI API integration to OpenRouter?
What's the incentive here?
Open Responses API doesn't appear to support state management (yet)
Which raises the question - who is subsidizing this, and why?
Possibly OAI? If you have OAI tokens you are a captive audience. If you have OpenRouter you are bidding on a free market.
OpenRouter attributes this promotion to OpenAI https://x.com/OpenRouter/status/2089416739398254662
OpenRouter is likely just leveraging Codex subscriptions.
Do other people find 5.6 to be worse at most simple tasks and frequently over complicate things?
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.
I've found it to be great for planning code changes (or new projects). I use the superpowers plug-in which I think guides the planning.
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.
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.