I've been offering Deepseek V4 Flash for free in www.freepi.ai and I've started using it as my main driver as well.
Besides trying to dogfood my own product I've hit a wall in terms of my patience with a)how slow fable is b)how expensive fable is. Not to mention how often it refuses totally legitimate work.
So yeah- I've moved to DeepSeek and I actually ask the freepi harness to delegate planning to fable but then move back to doing implementation in it's own harness. My current providers are super fast so it's a joy to use.
The real revolution is Deepseek v4 flash and similar models (GPT 5.6 Luna, muse spark 1.2, mimo, etc...) - Genuinely good performance for a tiny fraction of the cost of Fable and even GLM etc...
I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster. Of course, both things continue to happen on a seemingly monthly basis
If they could be cheap+fast and not try to do too much, that's a good spot for me. I don't use the smarter models as much because of cost and because they're still not good enough to let loose on a lot of problems. For assistance I prefer something that can very quickly spit out a specific piece I can review on the spot and keep going. I let smarter models handle things that I treat as external dependencies and don't care how they're written, but in my core domain I'm still mostly hand coding
As models train up the intelligence ladder, many common tasks will hit fully diminished returns, and instead it'll just get progressively cheaper to do that task. But the tasks that AI is capable of doing are also expanding. I'm not sure 'Some tasks don't require the peak of the frontier' is worth worrying about, from an AI finance perspective.
Exactly; so far, we've only replaced the need to design algorithms and hand-write code; what if we apply the same effort towards the skill needed for system architecture, project management, and the rest of the SDLC? Or even outside of software!
Right now, it feels like all of that is today where coding was a year or two ago, and we're on the cusp of some massive improvements outside of coding.
Yes. I use Opus for tasks that Sonnet could probably handle, but I'm not hitting my quota. Whatever minor incremental gain is "worth it", since marginal cost is zero.
Even now, I use Fable as the planner and coordinator, with it farming out to agents. I don't hit my Fable limits either.
Which means I could accomplish more, but these are side projects so I don't need 30x productivity. Still, claude is constantly churning away at something.
I operate mostly in the devops arena. Lots of things opus is fine for. But there is just things where I can hand hold Opus through changes, or I can ask Fable to do it and it gets it right on the first try. People will say let fable plan and validate with opus doing the work. I found that burns fable tokens even faster because opus makes so many mistakes, fable has to review things 4-5 times before opus gets it right. A single fable implementation at medium or low effort would have one shot it.
>> GLM 5.2 is worth focusing on. It came out the same week as Fable and is roughly 1/9th the cost (and ~1/5th the cost of Opus 5). Is GLM 1/9th the quality of Fable? Perhaps, for certain classes of tasks. But for most rote coding it’s more than sufficient. Especially when provided with great context. I frequently chat with Fable to interrogate and shape a design, before handing off a brief to GLM.
People say stuff like this a lot, but I have a different take.
The whole "such-and-such model is 90% as good as Fable at 1/10th the price" assumes that the value increase of intelligence is linear. But I think it's exponential: that last 10% makes a massive amount of difference. It can result in a key insight that helps you strategize more effectively, a novel approach that saves a huge amount of time, a feature design that is lot more user-friendly (because top models like Fable also possess substantial non-software domain knowledge that help bridge the gap between user and software), or the depth and breadth of engineering expertise that helps avoid a nasty bug that would otherwise have cost you users and revenue.
Yes, it is totally possible to use Fable as the planner and delegate implementation to lesser models. I do that. But, my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by Opus 5, GPT 5.6 Sol, GLM, Qwen, Deepseek, etc. The reason I believe this is because I read the code Fable writes and compare it to code that any other model writes and the difference is night and day. It's not just 10% better. It's mid-level engineer vs. principal/staff-level engineer. And the thing is, even for rote tasks, a more senior engineer is going to be more likely to come up with a clean design than a mid-level engineer. They will also be much more likely to take a step back and ask important questions or propose different approaches.
So if you're using Fable and everyone else is using lesser models, sure they might be saving a lot of money, but there's a higher likelihood that your product will be higher quality, perhaps to a significant extent. And models that are released in the future will benefit from it as well.
Something I’ve found comparing between Fable and Opus is that Fable has impressively good analysis skills, but both of them seem to go way way overboard with “present state” comments “# We’re making this change here because of this issue blah blah, here’s what you need to know about np.percentile, blah blah” that I end up significantly pruning before making a PR. I let it do the same style verbose commit messages (because a contextual history is cool there). I haven’t actually noticed a ton of difference in the code that they write personally, but have found that Fable does find nuances during data analysis that Opus misses.
In that light, I often go the other way: let Opus (and Haiku subagents) do most of the heavy lifting and then give Fable a shot at finding holes, especially if there are holes or unanswered questions or unearned assertions that I’ve caught on my own in Opus’ output. This, so far, seems like a clean tradeoff that doesn’t burn my Fable credits as hard and still gives solid results.
Those "present state" comments are the bane of my existence. It was present in 4.7/etc but i put in a ton of guards against that into my global memory and it worked quite well. Fable and Opus 5 regressed badly in this space though and i can't keep it from making those types of comments again.
> my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by [others]
I don't have proof, only my anecdotal experience: I leave plenty of Fable usage on the table because I do not think its implementations of code have been better to Opus 4.8, not even close. It overengineered, obscured and picked awkward constructs all the time over plain, simple, perfectly clean and performant code patterns. Code was smarter AND worse in the kind of way that a brilliant and overeager recent grad often does. (I know I did)
As a counterpoint (data point of one code base), I had Fable lead development of a complex system recently (an end-to-end insurance claims billing system) as a test project. It blew me away. Opus could not have done the same, given the feedback Fable had to give when Opus would implement individual features.
Granted, I laid out a document with coding practices, architecture, and technical design recommendations to steer it towards good engineering. And it's a domain I know super well, so I could give very nuanced feedback on trade-offs + architecture. If it had been left to its own devices, maybe it would have over-engineered the h*ck out of it.
But the code it produced—and the implementations it guided Opus towards—were excellent.
over time greater intelligence will be expressed in smaller and cheaper models. we are still somewhat near the beginning of this bc we are finally starting to understand what makes a model truly intelligent/capable.
With Sol we see openai making the model extremely slow and paranoid about process/ceremony. Sure this is a good guardrail against AI going rogue, but it also sets the stage for companies to charge for 2x, 4x, 8x performance, with 1x being barely tolerable and frankly slower than last year's models (though less error prone).
The irony is that the smarter the model, the more it can be trusted to do with less supervision, so one engineer can manage a team of 20 fable subscriptions more effectively than a team of 3 of last year's model subscriptions.
It's been cheap again on openrouter for the past few days. No idea how long it will last, but I've been using it from Baidu over the weekend, and it was about half the cost of the old DS prices, before the increase. Looks like people are figuring out how to offer it for peanuts.
Somewhat weird that the article was released today but did not mention GLM 5.3.
If you're telling me to focus on something, why not focus on the actual latest thing that is the same as 5.2 but better?
I get the "came out at the same time as fable" thing, but still.. no mention at all?
Yes, weights aren't out yet, but neither are the ones of Fable.
I can’t host GLM 5.3 yet, so my agents still run on 5.2. But the fact that 5.2 is sufficient and there’s another gen in the wings kinda proves my point, imo.
I don't think everything has to be Thought Leadership. OP compared the same generation of models to show that the latest open model—at the time of the latest closed model—was Good Enough.
I agree that the opener to their reply wasn't productive, but neither is "Weak."
Idk man, but an engineer would've taken that and said something like: "Damn, yeah, good point, I shall add a sentence mentioning 5.3"
Because an engineer feels secure in their knowledge so that such an oversight doesn't make them suddenly defend their identity - it's just an oversight after all. Happens.
5.3 isn't available as open weights yet, and only became available via API three days ago. Prior to that the only way to access it was via a Z.ai subscription.
I've been offering Deepseek V4 Flash for free in www.freepi.ai and I've started using it as my main driver as well.
Besides trying to dogfood my own product I've hit a wall in terms of my patience with a)how slow fable is b)how expensive fable is. Not to mention how often it refuses totally legitimate work.
So yeah- I've moved to DeepSeek and I actually ask the freepi harness to delegate planning to fable but then move back to doing implementation in it's own harness. My current providers are super fast so it's a joy to use.
The real revolution is Deepseek v4 flash and similar models (GPT 5.6 Luna, muse spark 1.2, mimo, etc...) - Genuinely good performance for a tiny fraction of the cost of Fable and even GLM etc...
I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster. Of course, both things continue to happen on a seemingly monthly basis
If they could be cheap+fast and not try to do too much, that's a good spot for me. I don't use the smarter models as much because of cost and because they're still not good enough to let loose on a lot of problems. For assistance I prefer something that can very quickly spit out a specific piece I can review on the spot and keep going. I let smarter models handle things that I treat as external dependencies and don't care how they're written, but in my core domain I'm still mostly hand coding
As models train up the intelligence ladder, many common tasks will hit fully diminished returns, and instead it'll just get progressively cheaper to do that task. But the tasks that AI is capable of doing are also expanding. I'm not sure 'Some tasks don't require the peak of the frontier' is worth worrying about, from an AI finance perspective.
Exactly; so far, we've only replaced the need to design algorithms and hand-write code; what if we apply the same effort towards the skill needed for system architecture, project management, and the rest of the SDLC? Or even outside of software!
Right now, it feels like all of that is today where coding was a year or two ago, and we're on the cusp of some massive improvements outside of coding.
Yes. I use Opus for tasks that Sonnet could probably handle, but I'm not hitting my quota. Whatever minor incremental gain is "worth it", since marginal cost is zero.
Even now, I use Fable as the planner and coordinator, with it farming out to agents. I don't hit my Fable limits either.
Which means I could accomplish more, but these are side projects so I don't need 30x productivity. Still, claude is constantly churning away at something.
I operate mostly in the devops arena. Lots of things opus is fine for. But there is just things where I can hand hold Opus through changes, or I can ask Fable to do it and it gets it right on the first try. People will say let fable plan and validate with opus doing the work. I found that burns fable tokens even faster because opus makes so many mistakes, fable has to review things 4-5 times before opus gets it right. A single fable implementation at medium or low effort would have one shot it.
It’s worth considering for companies paying API prices, and not relying on a subscription quota
What are all these rote coding tasks people do that they can farm it out to lesser models?
Write a detailed plan using a more expensive model and implement it using the cheaper one.
I just use Fable for reviews of specs and code then hand off to Opus to work on. Works well.
Does it not silently degrade to Opus if it does not like some word?
>> GLM 5.2 is worth focusing on. It came out the same week as Fable and is roughly 1/9th the cost (and ~1/5th the cost of Opus 5). Is GLM 1/9th the quality of Fable? Perhaps, for certain classes of tasks. But for most rote coding it’s more than sufficient. Especially when provided with great context. I frequently chat with Fable to interrogate and shape a design, before handing off a brief to GLM.
People say stuff like this a lot, but I have a different take.
The whole "such-and-such model is 90% as good as Fable at 1/10th the price" assumes that the value increase of intelligence is linear. But I think it's exponential: that last 10% makes a massive amount of difference. It can result in a key insight that helps you strategize more effectively, a novel approach that saves a huge amount of time, a feature design that is lot more user-friendly (because top models like Fable also possess substantial non-software domain knowledge that help bridge the gap between user and software), or the depth and breadth of engineering expertise that helps avoid a nasty bug that would otherwise have cost you users and revenue.
Yes, it is totally possible to use Fable as the planner and delegate implementation to lesser models. I do that. But, my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by Opus 5, GPT 5.6 Sol, GLM, Qwen, Deepseek, etc. The reason I believe this is because I read the code Fable writes and compare it to code that any other model writes and the difference is night and day. It's not just 10% better. It's mid-level engineer vs. principal/staff-level engineer. And the thing is, even for rote tasks, a more senior engineer is going to be more likely to come up with a clean design than a mid-level engineer. They will also be much more likely to take a step back and ask important questions or propose different approaches.
So if you're using Fable and everyone else is using lesser models, sure they might be saving a lot of money, but there's a higher likelihood that your product will be higher quality, perhaps to a significant extent. And models that are released in the future will benefit from it as well.
Something I’ve found comparing between Fable and Opus is that Fable has impressively good analysis skills, but both of them seem to go way way overboard with “present state” comments “# We’re making this change here because of this issue blah blah, here’s what you need to know about np.percentile, blah blah” that I end up significantly pruning before making a PR. I let it do the same style verbose commit messages (because a contextual history is cool there). I haven’t actually noticed a ton of difference in the code that they write personally, but have found that Fable does find nuances during data analysis that Opus misses.
In that light, I often go the other way: let Opus (and Haiku subagents) do most of the heavy lifting and then give Fable a shot at finding holes, especially if there are holes or unanswered questions or unearned assertions that I’ve caught on my own in Opus’ output. This, so far, seems like a clean tradeoff that doesn’t burn my Fable credits as hard and still gives solid results.
Those "present state" comments are the bane of my existence. It was present in 4.7/etc but i put in a ton of guards against that into my global memory and it worked quite well. Fable and Opus 5 regressed badly in this space though and i can't keep it from making those types of comments again.
Really frustrating.
> my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by [others]
I don't have proof, only my anecdotal experience: I leave plenty of Fable usage on the table because I do not think its implementations of code have been better to Opus 4.8, not even close. It overengineered, obscured and picked awkward constructs all the time over plain, simple, perfectly clean and performant code patterns. Code was smarter AND worse in the kind of way that a brilliant and overeager recent grad often does. (I know I did)
As a counterpoint (data point of one code base), I had Fable lead development of a complex system recently (an end-to-end insurance claims billing system) as a test project. It blew me away. Opus could not have done the same, given the feedback Fable had to give when Opus would implement individual features.
Granted, I laid out a document with coding practices, architecture, and technical design recommendations to steer it towards good engineering. And it's a domain I know super well, so I could give very nuanced feedback on trade-offs + architecture. If it had been left to its own devices, maybe it would have over-engineered the h*ck out of it.
But the code it produced—and the implementations it guided Opus towards—were excellent.
over time greater intelligence will be expressed in smaller and cheaper models. we are still somewhat near the beginning of this bc we are finally starting to understand what makes a model truly intelligent/capable.
With Sol we see openai making the model extremely slow and paranoid about process/ceremony. Sure this is a good guardrail against AI going rogue, but it also sets the stage for companies to charge for 2x, 4x, 8x performance, with 1x being barely tolerable and frankly slower than last year's models (though less error prone).
The irony is that the smarter the model, the more it can be trusted to do with less supervision, so one engineer can manage a team of 20 fable subscriptions more effectively than a team of 3 of last year's model subscriptions.
Are people still using deepseek-v4-flash everywhere? I found after the price increases, mimo-v2.5 seems far more attractive.
It's been cheap again on openrouter for the past few days. No idea how long it will last, but I've been using it from Baidu over the weekend, and it was about half the cost of the old DS prices, before the increase. Looks like people are figuring out how to offer it for peanuts.
Awesome. Thanks for the heads up.
Somewhat weird that the article was released today but did not mention GLM 5.3.
If you're telling me to focus on something, why not focus on the actual latest thing that is the same as 5.2 but better? I get the "came out at the same time as fable" thing, but still.. no mention at all?
Yes, weights aren't out yet, but neither are the ones of Fable.
Doesn't feel well informed enough to give advice.
Ok, buddy.
I can’t host GLM 5.3 yet, so my agents still run on 5.2. But the fact that 5.2 is sufficient and there’s another gen in the wings kinda proves my point, imo.
I'm just saying that thought leadership needs leading thoughts.
.. and not "Ok, buddy" when being called out for that. Weak.
I don't think everything has to be Thought Leadership. OP compared the same generation of models to show that the latest open model—at the time of the latest closed model—was Good Enough.
I agree that the opener to their reply wasn't productive, but neither is "Weak."
I think it’s a fine response when you say, “Doesn't feel well informed enough to give advice,” because I said 5.2
Idk man, but an engineer would've taken that and said something like: "Damn, yeah, good point, I shall add a sentence mentioning 5.3"
Because an engineer feels secure in their knowledge so that such an oversight doesn't make them suddenly defend their identity - it's just an oversight after all. Happens.
5.3 isn't available as open weights yet, and only became available via API three days ago. Prior to that the only way to access it was via a Z.ai subscription.
> Yes, weights aren't out yet, but neither are the ones of Fable.
Granted, edited in by me, but definitely before you - another thought leader - arrived.
he did already tell on himself btw. No point in this charade.
FWIW you’re not looking good in this engagement, feels very childish, looking for a gotcha that doesn’t mean much
I think that depends on the audience. Thank you for caring though :)