GPT-5.6 Sol Pricing Cut by 50%
openrouter.ai471 points by Topfi 14 hours ago
471 points by Topfi 14 hours ago
The competition is real in pricing. Thanks for the Chinese open models, US big players have to cut their inference pricing. We've done a bunch of evals between the models, and Kimi K3 was the first one that actually could compete or be even better than Opus or Sol in our use cases, with a fraction of the price. All our developers use K3 as their programming model, and it now powers a big part of our systems instead of Opus and GPT. Surprisingly the new Sol pricing is quite similar to K3...
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.
I shifted from DeepSeek v4 Flash 0731 to Gemini 3.7 flash on openrouter and price shoots up almost double with no visible change in outcome. So, today I reverted back.
I was struck by a video ad that Google released yesterday with testimonials by three developers about using Gemini 3.7 Flash [1]. The point they emphasize most is price, followed by latency. The marketing strategy definitely seems to be shifting.
> Opus or Sol in our use cases, with a fraction of the price.
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
Long-context agentic tasks and Rust engineering are our use cases where Kimi definitely is better than Sol. We can measure our own systems and the numbers say that Sol has no chance against K3 or Opus, and K3 is so so so much cheaper than Opus right now.
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.
> And these models are not going away, nor their prices going up [...]
Well, DeepSeek just raised prices.
And Fireworks did not yet. They are still under the limit of not feasible to self host... Let's see if other providers follow DeepSeek with their flash pricing.
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.
I was having it look at creating a driver for some old scanner and it actively looked up exactly where that gray area for my country was wrt decompilation.
Fable is almost unusable for anything but super boring mainstream stuff. I was getting safeguard flagged so often I’ve significantly reduced my usage out of fear they will blacklist/ban me.
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.
I cancelled my Claude max subscription. Somehow every query I sent was flagged as bio or chem, even pure mathematics questions. Not going to waste money paying for a “max” subscription that won’t ever let me use the top tier model…
Sol is great and has never blocked a request, and generally gives great answers. Happily switched over to it now.
The safety is really funny to me. I ask it a lot of extreme stuff and it goes through, but I ask it mundane stuff and hit the filters all the time.
Reminds me of the URL blocking of my company. Nature.com is being blocked but I can access a ton of super sketchy download sites.
The dangerous part isn't that a model refuses extreme requests. It's when mundane requests become unpredictable enough that you stop trusting the model.
I've gotten flagged for asking questions about tokens and tensors. That makes me believe it's not about safety, it's about protecting their turf. I cancelled my subscription - same fear about getting flagged too much leading to a ban.
They said they also block usage of Claude models to build ML models.
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.
OpenAI and Kimi are both pretty okay alternatives! I guess GLM 5.3 on Max reasoning as well but for more limited domains.
Have they made Sol do less unwanted autonomy than the previous Codex models did?
I feel like those examples are considered difficult because they're niche topics, but aren't actually all that difficult in a general sense. What I consider truly difficult are things like taking a ticket and implementing it in a preexisting codebase, using a clean and reasonable design that fits the existing style and makes sense to a human, and avoids the footguns I learned by working with the codebase for over a day.
If you said this in 2025 I would've 100% understood, but to be honest getting AI models to do a pretty good job on day-to-day ticket work has become so boring that we don't even bother using the top tier models and higher effort slots for that anymore. I personally wind up tweaking the results a lot and recursively having fresh agents review the diff, but that's just because I'm picky; in a lot of cases the first diff is actually pretty damn decent.
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.
All non-trivial code terra has generated for me has had at least one serious bug in it. Typically caught by a review from myself or Sol.
But I wouldn't trust lower tier models for end to end solutions.
This is true in some sense.
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 use the AGENTS.md to show it how i want the code to look like. Something like "when implementing hooks adhere to the guidelines in docs/react-hooks.md". And then react-hooks describes your heuristics and what you consider best practices. There is a clear difference in code quality for me when using codex with a well crafted AGENTS.md vs. without one, you can run the experiment yourself pretty easily. As I mentioned in another comment, I think Claude poisoned users to stop relying on their Claude.md files and new codex users might be surprised at how well it adheres to guidelines.
We use skills for similar purposes, with positive and negative examples.
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.
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.
How do you handle context limits? With more thinking tokens you fill it up earlier. Compaction degrades performance too. What's your strategy?
Initially I was planning heavily around context limits, but I've learned to just ignore it completely. Compaction is seamless for me. If details are lost in compaction, the model just re-reads what's needed. My conclusion is that at least for Sol, the summaries (which I've never seen) must be amazing. Every now and then a detail gets lost and I have to repeat it. I don't think there is performance degration, because the model is smart enough to re-read relevant files as needed.
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.
How detailed of a plan? Are you including code snippets or just behavior and letting the lesser model decide how to implement?
Why wouldn’t you use Luna for that? It’s super cheap.
> Why wouldn’t you use Luna for that? It’s super cheap.
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.
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
Yeah, I very much agree on this. I think Sol and Fable code quality is on par. Maybe Fable is just a tiny bit better, but Sol compensates with its ability to work through things, while Fable, in my experience, generally tends to avoid solving problems that require many LOC.
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.
Sol w/ Effort -> Low
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!
Interesting the use of Max and Ultra. I don’t doubt the complexity, but would someone use Max or Ultra on Typescript or Go, for example?
Is it more about just avoiding any mistakes? Seems like that would be costly when medium or high would work fine?
*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.
5.6 Sol is a joy to use for "daily chat" as well. Compared to earlier OpenAI models it catches and corrects its mistakes very reliably. It also seems way smarter in tuning its replies to areas I am more/less knowledgeable about (i.e. when I ask it a law question, it assumes I know as much as a toddler which is true, but on political topics it more easily throws around terminology) and including analogies. On medium thinking, it's a very good compromise between speed and quality.
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’ve never had it refuse anything. Even vulnerability searching in my codebase.
Yeah I feel like I'm living in a different dimension than these people. I wonder what they're working on. I've literally never had it refuse everything and I max out my 20x plan every week
Can I ask which country you're in? I have a theory that the safeguards differ depending on the user's country.
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.
As one random example, today I had it hit a refusal loop when adding a country selector dropdown to a form, presumably because it contained a “bad” country name? I hit refusals at least 2-3 times per day, sometimes many more. The worst part is it is often right in the middle of a multi-stage task, so the only option is really to switch to Opus 5 and let it defecate its absurdly verbose comments all over the rest of the edits in the turn and hope it doesn’t go on one of its tangents, then have Sol do damage control. Oh and I got approved for their “cyber verification program” blessing, which comically does absolutely nothing for Fable.
I too switched to OpenAI after I got sick of Anthropic's constant "safety" downgrades. Sol is definitely a breath of fresh air.
> 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.
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.
I was pleasantly surprised to find that the GPT models are much stricter in adhering to my AGENTS.md guidelines and heuristics than Claude.
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 cancelled my subscription recently and moved to Sol. So far - it has been a great experience. The only aspect where Fable/Claude is better I feel is doing some research from the web and summarising the facts.
I generally prefer Fable but in my experience Sol is a much better web researcher
Sol is my daily driver but there are still times I reach for Fable when Sol doesn’t cut it. Just yesterday for example, I was trying to build a self-modifying hot-reloaded agent harness in Elixir for fun and Sol just kept doing silly things like thin wrappers and unnecessary abstractions. Fable handled the task elegantly. Sol is really good as a reviewer for finding bugs due to its thoroughness however.
It's the complete opposite for me. The model might be the worst model I have ever used when compared to other models in the class. You just can't get it not to just write the most enterprise over complex over engineered solutions for every little thing you ask it to do.
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.
Used claude since 7/2025. Switched to codex after fable got blocked. It was still 5.5 but I knew they had to come up with something. As soon as I switched, wow. It wasn't super intelligent, but it was stable. Every day it was the same performance. This consistency is definitely worth paying for.
you should check out the codex desktop app. people who've been using claude code for a long time will surely be surprised.
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.
> if you ask something too obvious or direct it will stop you other than that, it work flawlessly
What on earth are you asking it?
sol is much better imho than Fable but i can understand if they will perform wildly different for different people with different levels of expertise aswell as different needs. I dislike fable myself it doesnt really work for me.
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.
The final straw for Claude was its refusal to give me a list of the most recent rapes reported by the BBC and basic information about them (location, date, names, just things reported in mainstream media). It outright REFUSED to complete this task.
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.
Claude as a harness at all really spends too much time before giving user feedback
Its a crutch that is no longer competitive
I have a "strategy / life-coach" project, and was surprised at how much better Sol is than Fable on it, as I've found Fable to have the edge for most things for me so far. But Sol: questions were better, insight was better, it got the brief better.
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.
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)
That was an issue at least a year ago. I had signed up for a claude account on my iPhone and then wanted to sign in on my laptop but nope, not possible. Insane they still haven't fixed it.
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.
I signed up on iOS, Sign In with Apple, cause I don't go around giving random companies my actual email if I can help 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: {pic incoming}
All the Claude hype posted on HN seems like a case of the emperor with no clothes
Where is the official source for this?
OpenAI's docs still show non-discounted pricing https://developers.openai.com/api/docs/models/gpt-5.6-sol
It's discounted if used via OpenRouter, not the official API.
Doesn't seem like a smart business move.
You're literally encouraging someone else to come in and steal your customer base,
A surprising amount of companies sell exact the same product through different channels for different prices. A good example is Apple.
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.
Or it's "normal" market segmentation. OpenRouter users are more price sensitive in general, also a lot of enterprise users who can't switch easily are using the official API (or Bedrock or Azure) and you want to squeeze them as much as you can.
Literally all OEMs do this, if you go to Apple site you won't see any sales, but Amazon and other retailers will have stuff at 20% off regularly
This imbalance of exchange means that OpenAI is getting something from this deal. Question what is exactly.
Seems reasonably clear to me? Potentially bringing in more customers who use OpenRouter for trying out all the models with rapid switching, enticing them to use Sol. And anyone being routed on price will immediately be switched to OpenAI's servers instead.
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:
That's weird.
Why is that weird?
How is that not weird?
Has OpenAI struck a deal with openrouter and that's why we're seeing preferred pricing?
Is openrouter taking a loss on sol API calls to grow adoption?
How temporary is the reduction in price?
Selling cheaper through what should be a minor third party, than through the first, party is super weird in commerce.
This sure looks like a race to the bottom to me, and I love it.
If Sol isn't the best model, it is up there...
You don't cut the price of the best model for no reason...
> This sure looks like a race to the bottom
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.
> All flagship models are within like 1-5% of each other
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.
The problem is that most of the volume doesn't come from proprietary products, it comes from API use which has no stickiness.
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).
> All flagship models are within like 1-5% of each other
Depends on your use case. the Chinese models are not there yet.
There is a massive difference even between Opus and Fable, same provider, before various harnesses and other optimizations come into play. Don't be deceived by rankings and benchmarks, try for yourself.
If you believe https://artificialanalysis.ai/
This is basically undercutting KimiK3 and Grok 4.6 where previously utilised gad soke advantages but was a step more expensive
You do it if you can afford to do it and your competitor can't.
Who are OpenRouter’s competitors?
OpenRouter doesn't decide on the pricing.
They do decide on the 5% markup. But as far as I can tell, all other routers just match 5%. Not sure what they're competing on.
Yes but Sol dropping in price by 50% is not OpenRouter deciding. It's OpenAI.
I don’t think that’s true. OpenAI docs don’t have this price change. I assume they’d be the source for this post if it was true. The banner on OpenRouter for me says Gemini 3.7 discounted for a limited time, but if I click through that I get to this page: https://openrouter.ai/models?discount=true
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.
OpenRouter margins are not 50%.
As I understand OAI is offering discount only for users using the model via OR. Not sure why. Maybe they want OR users to try the model and switch to OAI subscription or something.