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.
There's people that have tried to contact Jeff McBride and follow the IP trail but the IP is currently owned by a company that went defunct. Not sold, but no one is even bothering to register its LLC any more, it's simply dead.
Some of the topics it’s flagged have been hard for me to understand what it seeing that can be remotely concerning in my requests.
Sol is great and has never blocked a request, and generally gives great answers. Happily switched over to it now.
Which is definitely protecting their turf, but also probably a little bit hiding their “RSI” abilities for competitive reasons. My theory is that a lot of “safety blocking” is actually WIP training of new business directions. Anthropic has started hiring biologists and has opened a preview of a “Claude code for bioinformatics”. I’m guessing they’re tweaking their bioinformatics market play, and block “bio safety” requests so competitors can’t learn about their training.
Compared to what I am doing at home experimentally, I feel like day-to-day work is absolutely nothing. Not only am I also working with existing codebases in my experimental prototyping, but I am also doing things vastly more complex with vastly harder constraints.
Getting the AI to output code that you like is difficult.
As an example, let's say in React you have a "useLocale()" hook.
The AI will happily pass down locale as a prop to 5 child components instead of just calling the hook in the component.
A review from another model did not flag such stylistic issues either.
I believe that the latest models are very good at functionally achieving the goal, but still have poor taste for UX or code quality.
The most productive use of AI for software development happens in an environment where you do not review the code but test the UX end to end.
I think it sometimes worked, for example for testing preferences, but sometimes it did not.
Could be a problem with the harness also.
In any case, I feel that it's a bit playing whac-a-mole with explicit rules for things that a more intelligent model should do by default.
MAI also offers a ultra cheap version that's competitive with Luna.
So much so that the models look like they were designed by a product manager explicitly to eat away OpenAI's market share.
Vscode even pushed them quite hard onto users with the latest release, going to the extent of putting up a modal to convince users to try them out.
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
However, I think these are very different models in terms of orchestration. Long-horizon tasks are way more predictable with Fable. It just doesn't lose track of details. Thus I ended up building a small wrapper around Pi (where I run Sol) so that CC can delegate via background tasks, automatically wait for completion, and do what was one of the most effective parts - steer Sol toward simplicity, getting Sol out of code-review infinite loops (Pi calls for Codex review to ship better, but generally gets stuck on P2 and results in vastly overengineered work).
One of the worst experiments was enforcing coverage at 100%. Only Sol, with an enormous amount of code and significant pushback (on architecture decisions) to Fable, was able to reach it. It made me think this is somehow related to overengineering in general, so that instructions on acceptance criteria in claude.md plus proper DX (e.g., Lefthook) actually led to okay results. It mostly helped that responsibilities were clearly split: Fable designs architecture, Sol handles coding and debugging.
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!
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.
I'm in Australia, and Fable downgrades to Opus when testing for bugs in memory in a legacy C code base. If Fable starts taking initiative and writes a test case that involves writing to a null pointer, that's the end of the conversation.
> even after completing the verification program
Was it easy to complete it?
I ended up in some weird state where I can't even attempt the verification at all. Opened the Persona tab once, closed it and then it never opened ever again. It says a verification precheck failed.
Even without TAC, Sol doesn't seem to get blocked very often. Fable would downgrade to Opus if I looked at it wrong.
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.
It the first model to actually make me pissed off to use AI. I absolutely hate the model so much.
I don't even want to see the codebases this model is fucking up.
It might just be good at finding bugs that about it. That all I would ever use it for just because it works harder than Claude models.
What on earth are you asking it?
Sol also doesnt _really_ work but it sort of tricks me into thinking it does more convincingly :p.
cancelled my subscriptions few days ago. (was on 100$ ones, not sure if there is diff in quality for higher tiers or not.. there might be that too).
what i hate the most is that they will make any obvious mistake you do not tell them to avoid. then on the next plan to fix it, your token limit is hit at step 4/5 -_-. Both models seem incredibly good at that mostly...
for tasks outside of coding and program design i do find them quite useful. like devops crap. maybe because i hate that, i like their help there more.
I will not be told what I can and can't do by AI and I will no longer be supporting American companies run by despicable people. GPT only gets my money right now because its so fast and cheap but I'll be back to Chinese models in no time.
Its a crutch that is no longer competitive
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)
Can somebody at Anthropic tag claude in slack or whatever goofy shit you do and ask it to add Apple OAuth to your website? Clearly humans aren't testing it.
and sure enough, I was right to do so: They don't even let you remove your payment method afterwards. Every other store, Steam etc., lets you.
No way I have enough trust to install their desktop app after that, so I just want to try it through their website..
Can Sign In with Google, but not with Apple
so you gotta open the Passwords app, copy your random email, paste into the website, then copy the OTP from your email..
It's been that way for at least a year
and the AI itself hasn't been that hot compared to ChatGPT/Codex either: https://i.imgur.com/jYawPDY.png
So all the Claude hype posted on HN seems like a case of the emperor with no clothes to me
(P.S. The thing I just now tried to do on Claude hit the weekly usage limit after 2 minutes)
If Sol isn't the best model, it is up there...
You don't cut the price of the best model for no reason...
Always has been. My prediction is that both OpenAI and Claude will go bust unless they deliver a killer product. And unlike scrappy startups, they have a pretty serious deadline because creditors will come a-knockin'.
There's little to no functional difference between Kimi, Qwen, Sol, Opus, etc. All flagship models are within like 1-5% of each other and the real moat will be what's always been the hard part: making a good product.
Don't know about that.
I'm using code review of my lone lisp project as a benchmark. It's a massive parallel code review where a coordinator cuts up the codebase into sections and dispatches agents to consider each part from different perspectives like quality, maintainability, consistency, correctness, rigor, etc.
Ran a complete Fable/max code review. Took over a month on a subscription. Now I've switched to OpenAI and am repeating the exact same review with Sol/max.
It's still not done yet but preliminary findings suggest Sol can only reproduce 70-90% of Fable's findings. So I think these models aren't as close as we've been led to believe.
Claude already has a killer product (claude.ai/chat is a Swiss army knife) but just relying on people typing stuff into chat is not enough to sustain the company.
The other strategy is entrenching yourself as the LLM of choice into existing products (like ChatGPT is on Apple products).
This is basically undercutting KimiK3 and Grok 4.6 where previously utilised gad soke advantages but was a step more expensive
That shows a bunch of models, including Sol, with a discount. None of them say how long it’s for, but I’d assume in all their cases it’s for a limited time as the banner said, and only on OpenRouter.
I posted elsewhere, but the Azure uptime & performance for Sol is truly dire. OpenAI is offering 5x faster latency, 4x faster tokens generation, and vastly better uptime (Azure US has only 87% uptime), all for 50% of the price now. I assume the pricing is to compete with other shiny new models (Grok, Qwen etc), but it might also be to cut-off a truly poorly performing Microsoft hosting experience.
OpenAI's docs still show non-discounted pricing https://developers.openai.com/api/docs/models/gpt-5.6-sol
You're literally encouraging someone else to come in and steal your customer base,
Multiple times a year, retailers here in Australia have co-ordinated sales on Apple products. Apple.com or their retail stores don't have these sales.
But they're clearly Apple-funded when competing retailers launch the same sales on the same days; and the margins aren't enough for retailers to take a loss.
It also seems to be providing a vastly better user experience - Azure has less than 99% uptime (Azure USA only has 87% uptime), latency of 20 - 30 seconds, and a mere 8 tokens per second. OpenAI is offering 32 tokens per second (4x faster), 4 seconds latency (5x faster), and all for half the price of what Microsoft is charging for a vastly inferior experience.
Data taken from this page:
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
Batch API is a bit harder, not many models/providers support Batch. It primarily is only the Gemini/ChatGPT/Claude models that do. DigitalOcean does support a 50% discount on Batch API via them directly, not listed on OpenRouter.
[1] https://docs.digitalocean.com/products/inference/how-to/use-...
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.
As for ZDR and court-orders, what would you rather happen there? Violate the law or comply with holding the data? I would bet that any ZDR agreement has this court-ordered risk mutually understood and agreed upon.
Codex has always beaten claude in coding benchmarks, hasn't it?
God knows I've tried. I've got a variant of the ASD-STE100 trick which does the job, mostly, at the start … but get to about 100k of context and it goes out the window.
The model's personality is too strong for simple suggestion, alas.
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).
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.
As for co2, it depends on the provider, it could be way lower as well.
As for ethics, you don’t know what he works on, and how effectively - he might be saving 10x that much of co2 for the planet.
Do some people still deny you can do a shit ton of work with AI?
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.
I ended up borrowing my gf's phone number just so I could get access for work. Ridiculous
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?
(there's probably going to be a reply about 'but how can you trust them'; I'm just stating what they say)
With models a commodity at this point there isn’t much leverage for the big labs to keep their pricing anywhere near where it’s at. And that’s at the worst possible time as they need to be dramatically raising prices to have a viable business model.
Expect pricing to rapidly fall towards the underlying cost of compute and as players get really desperate we’ll likely see inference at less than the cost of compute as the market starts to rationalize and squeeze out weaker players who’s only play left will be to be the cheapest option in town.
The AI bubble is just waiting for the first player to scream mercy and cut capex as they simply can’t afford to throw more cash on the burning pile. That will be the trigger that implodes this bubble.
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.
https://www.youtube.com/watch?v=rE75WvOtcu8
The Shrek movie market correction correlation may be due again in July 2027. =3
If this nudges Anthropic to give me more Fable usage, that's even better.
Fwiw, you could do this with any small or medium model, and it's easier with the aws-docs mcp. AWS is pretty stable, well documented, and programmatic, so most AI can figure out what it needs pretty quick
I don't get this thread.... Really. Is it full of bots?
Here are all the providers giving discounts: https://openrouter.ai/collections/discounted-models
Another thing some people don't notice is flex pricing, which is way lower than default pricing, for slightly worse latency and reliability. Depends on the provider and model
That’s why Chinese models are gaining traction and it’ll be the only way for OpenAI or Anthropic to keep up.
So this isn’t really a price cut. As to why, lots of possible reasons. Perhaps an agreement with OpenAI to help them drive up more diverse traffic priorities.
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.
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)
At this point, I'm considering going back to cursor over codex due to the ability to get more control over what model I use since there is clearly a heap of user preference and having frontier providers constantly shift the goal post with "State of the Art" is complete non-sense.
The TypeScript code which was transpiled into Rust (and is compatible with most hugo templates) runs faster than the original hugo.
[1]: https://github.com/tsoniclang/tsonic-examples/tree/main/rust...
The transpiler is still WIP, but the fact that it can do this says a lot of about how far LLMs have come.
Its like rehiring an employee every few months then training them up. Its honestly tiring and cant stay like this.
Opus has the same problem too…
if you want to solve basic problem then use Luna
If you remember programming language discussions, they are exactly like this.
Software development is still in the leeches and bloodlettings phase.
I’d bet that explains this move!
Is the HN community just too online and sucked in to the musk mind manipulation vortex? Or what is going on? Why does nobody seem to care?
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 agree Luna is great for task execution, either as a sub-agent with Sol planning and coordinating or if the task is well defined and straightforward, but there are lots of models now that you can say that about.
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 used Claude exclusively for the past few months
Was excited when Sol came out a few weeks ago and loaded it up
I made the mistake of treating it as if it were Claude - I’d assumed they were close enough in ability and treated them that way
Well, turns out my instruction sets for Claude are 100% too complicated for Sol
Sol made the stupidest assumptions, constantly did things that it wasn’t asked to do and always approached code in what I considered a weird way - I had redo a lot of my prompts to get it anywhere close
Now, did it do good work?
Yes, on occasion. But with LLMs and coding, consistency is the name of the game. Constantly having to correct the LLM and constantly feeling paranoid that it won’t listen makes for an exhausting session
Maybe if you “came up” in the codex world you’re more fluent with it, but sticking with Claude for now
Fwiw I love K3 and use it as a daily driver. I haven't tried Sol, as I dislike OpenAI.
Now DeepSeek v4 Flash 0731 is eating Gemini's lunch, and suddenly we saw a price cut (the "introductory price") for 3.7. DeepSeek is of same quality or sometimes better than Gemini for text, Google knows it and they have to compete. Too bad it's too little and too late, it's still 4-5x more expensive in our evals.
And these models are not going away, nor their prices going up because of competition in the inference providers and due to the fact that you can buy/rent the hardware and run them in your own premises.
[1] https://youtu.be/kacf2bib-X0
I assume it's highly use case dependent, though?
Even before the price cut seems like Sol was price competitive with Kimi
https://artificialanalysis.ai/models?models=gpt-5-6-sol-xhig...
And now it should be considerably cheaper
You cannot just look at the price tags for these models, you must eval and see the price per task. In our previous eval rounds Sol was more expensive than Opus (with its original price), took much longer, and provided worse results. Kimi does not have these issues, it's just as good as Opus with a smaller price tag.
If there's independent data showing this feel free to share a link, I haven't seen it. DeepSWE has been most closely matching what I see in my own use.
Well, DeepSeek just raised prices.