5 Tricks to SAVE Your Claude Fable 5 Usage (80% Cheaper?!)
CChase AI
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Transcript
00:00:00What if I told you we could reduce Fable 5's cost by 80% while still beating Opus 4.8?
00:00:05Well, that is just one of five tricks I'm going to show you today that are all about reducing
00:00:10Fable 5's usage and token cost without losing what makes this model great. Because we all know
00:00:16the clock is ticking. We just got a few days left until Fable 5 is kicked off the pro and the max
00:00:21plan and we are stuck paying API prices. And on top of that, we're also usage capped. So it is
00:00:27imperative that we figure out quickly how to get the best bang for the buck with this new model.
00:00:34So that is exactly what we are going to talk about today. And let's dive in.
00:00:38So tip number one, this is the easiest one and arguably the highest leverage. It is changing
00:00:43the effort level. It is reducing the effort level with Claude Fable 5. Now by default, we are on high
00:00:50and some of you are crazy out there and I see you pushing it to extra high or even max. And the truth
00:00:56is you probably do not need that. First of all, what are we looking at here? Well, we're looking
00:01:01at a benchmark. This is deep sweet. This is one of my favorite benchmarks. It's all about long horizon,
00:01:06long running agentic tasks. And we see Fable 5 here, Opus 4.8, as well as GPT 5.5. Now I said in the
00:01:14intro, I said you could reduce costs by 80%. When we look at the max effort level, which by the way,
00:01:20doesn't give you that much of an upgrade in terms of, you know, pass percentage from extra high,
00:01:25it's costing us $22. Our average cost per task when looking at deep sweet. If we compare that to low
00:01:32is $3 and 76 cents, more than an 80% reduction in cost. Yet at low effort level with Claude Fable 5,
00:01:42we're doing better than max at Opus 4.8. So we're at 60% at Fable 5 low and we're at 59% with max Opus 4.8.
00:01:52And Opus in this case is $13 versus $3 and 76 cents. That's crazy. Like you could argue that's crazier
00:02:02than the jump from 59 to 70 is the fact we're doing this so much more efficiently. And when we pop up to
00:02:09medium, we're going up from 60% to 65% pass rate. And when we go up to high, we're at 69%. And on extra
00:02:16high, we're at 70% versus 59%. But at low, again, we're getting really solid outputs that are extremely
00:02:24cheap, even compared to GPT 5.5, which honestly is such a sleeper model. It's so good. I'm super excited
00:02:29to use 5.6 when it comes out. It's still doing better than medium, slightly more expensive, but not
00:02:35by a ton. Now we see this reflected in other benchmarks as well. Here's a look at frontier
00:02:39code accuracy versus cost. And this is coming from Anthropic itself. So in the orange, we have Fable
00:02:44in the green, we have Opus 4.8. And then down here at the bottom, we have 5.5. Look at low. All right,
00:02:50just a shade over $5. And the score is about 11%. If we look at Opus 4.8 on max, it's call it $11.
00:02:59And it's the same exact score percentage. So I'm getting the same pass rate as Opus 4.8 on max at
00:03:06half the cost. And if I go up to medium, I'm blowing Opus 4.8 out of the water while still
00:03:11being less expensive than extra high, which is where a lot of people sit for just default Opus
00:03:15settings. So with that in mind, does it make sense for us to sit on the default high level
00:03:19with Fable 5? I think when you look at both of these benchmarks, the answer is probably no.
00:03:24And we look at DeepSuite. DeepSuite is rather complicated tasks. Are you doing something very
00:03:30complicated? The less complicated of the task you're doing, if you're doing web design, you probably
00:03:35should be on medium or low. And that right away is going to reduce your costs and reduce your usage
00:03:41substantially. Substantially. So out of everything you see today, if nothing else, I want you to try
00:03:47doing a task on medium with Fable. Try doing a task on low and see how well it does. I think you'd
00:03:52be surprised and you're not going to get to that 50% of your weekly limit nearly as quickly as you would
00:03:57otherwise. And of course, to change the effort level, all you have to do is go into the terminal,
00:04:01do forward slash effort, and then set it where you want to. Now, before we jump into tip number
00:04:06two, quick word from today's sponsor, me. So I just released my Cloud Code Masterclass, and it is
00:04:11the number one way to go from zero to AI dev, especially if you don't come from a technical
00:04:15background. We focus on real use cases. It's updated every single week, and it also includes a
00:04:20Codex Masterclass and an agentic OS Masterclass. So if you're someone who's really trying to level up
00:04:26your AI game, you want to get serious about this, make sure to check it out. It's inside of Chase AI
00:04:30Plus. There's a link to that in the pinned comment. Now, tip number two, when it comes to reducing Fable's
00:04:35usage is also pretty straightforward. And that is stop using Fable to both plan everything and execute
00:04:42everything. Instead, make Fable the architect. Have it come up with the plan. And then depending on the
00:04:49complexity of the plan, have it divvy up the work to the appropriate model, whether that is Opus,
00:04:53whether that's Sonnet, or it's an outside model. It could be GPT 5.5. It could be something local.
00:05:00Fable's also smart enough to know which model is best for the job. And you can have Fable 5 explicitly
00:05:05call out those models in the plan. So Fable 5 does the plan, and then it says, hey, for the first part,
00:05:12I want Opus. For the second part, Sonnet makes sense. And for the third part, let's send that to
00:05:16OpenAI and bring in GPT 5.5. This is a perfect use case for something like the Codex plugin within
00:05:23Cloud Code. And if you haven't used that before, I highly suggest you do. You can totally bring in
00:05:27something like the Codex rescue function and have Fable call on that to give features to GPT 5.5,
00:05:34which again, awesome model. But if all that's too complicated and having it call out all these agents,
00:05:38you can do something as simple as simply throwing it in plan mode, having it come up with the plan,
00:05:44having it create some sort of markdown file, set the stage for your code base, and then simply
00:05:48spinning up another session with Opus and having it execute the plan that Fable laid out. You don't
00:05:53need to overcomplicate it, but that stops Fable from burning a bunch of tokens on low-level tasks that
00:05:58are going to be necessary for whatever you're creating. Now, tip number three is to bring in outside
00:06:02tools and skills like Ponytail that are all about reducing token count. Now, if you don't know what
00:06:07Ponytail is, I did a full video on it and its thing is like, Hey, Claude's pretty verbose. What if we
00:06:13gave it a set of guidelines to follow so that we still get the same outputs? It's still just as
00:06:18effective. It just writes less code to get there. Now, the thing with Ponytail is it gives us a bunch
00:06:23of benchmarks. The thing with the benchmarks are they've only been tested on Haiku 4.5 and Fable 5 is a
00:06:29different beast entirely. In my last video, I tested the numbers using Opus 4.8 and found that
00:06:35using Opus 4.8, these numbers were actually even better. It actually wrote less code. It consumed
00:06:41less tokens and it was faster. And so I went ahead and I ran some of the same benchmarks using Fable.
00:06:47So these numbers on the left right here that are underlined, this is the baseline. And then these
00:06:53over here on the right is what Fable 5 got using Ponytail. And this was on a medium setting. So
00:07:00across the board, essentially it put out less tokens. And in terms of cost, which is what we really care
00:07:06about because, you know, the tokens, like we do care if they're input versus output. At the end of the
00:07:11day, it was essentially 22% cheaper, which funny enough is actually even better than what they claim
00:07:17for Haiku. Now there's other skills like Caveman that claim to do the same thing. But the big picture
00:07:22with this tip is this is an expensive model. If there's stuff out there that can give us a 20% boost,
00:07:28it's worth experimenting with. So even if something like this is sort of like suspect to you,
00:07:32I think we shouldn't dismiss it out of hand because 20% is a lot of money when we're talking about
00:07:38thousands of dollars. Now on the surface, tip number four is the exact opposite of what I told you
00:07:43tip number two. Remember tip number two, I said, hey, Fable doesn't need to plan and execute,
00:07:48just have it planned. Well, in this tip I'm saying, let's not have a plan. Let's actually have Opus
00:07:53plan for Fable. Now, what I mean by this isn't every plan should be done by Opus and we have Fable
00:08:00actually create everything. I'm saying for a lot of our plans, they require research. And one of the
00:08:05best ways to research these days is with ultra code in dynamic workflows. Specifically, I'm talking
00:08:11about deep research. So for those of you who don't know, forward slash deep dash research is a built-in
00:08:17dynamic workflow you can use. And it's going to spawn a ton of sub agents. I use deep research in
00:08:22preparation for this video and it spawned 109 sub agents. First of all, would I want to run deep
00:08:28research with Fable 5 as every one of those sub agents? Absolutely not. I would blow through my
00:08:34limits. That makes no sense. But the real point here is I want to use a lower level model like Opus
00:08:39for deep research because research isn't something that requires some like super high in select reasoning
00:08:45level like Fable. However, Fable 5 doesn't necessarily have all the context of today. It's knowledge
00:08:51cutoff wasn't yesterday. We still need something to go out there on the web, gather information,
00:08:57do some baseline adversarial work to make sure that information even makes sense,
00:09:01and then hand that to Fable. And then Fable makes the plan, right? If we're going to plan something,
00:09:07we need information to start. And so I don't think it makes a lot of sense to have Fable go
00:09:12out and gather all the information. Let the lower level peons like Opus and Sonnet gather all that
00:09:18context and then hand it to Fable. And then Fable creates the plan and then they can hand it off.
00:09:24So Fable doesn't have to do everything in the planning stage. We can kind of give it a leg
00:09:29up, let it do the high level intellectual architecture work, and let these dumber models do everything
00:09:34else. And in that sense, dynamic workflows, ultra code, deep research is perfect for these low level
00:09:39models and saves that Fable usage for the more important things. Now, tip number five is something
00:09:44that actually came out a few months ago, and that is advisor mode. Advisor mode was originally shown
00:09:49with Opus and Sonnet working together. The idea is, this should sound familiar, is we have a smart model
00:09:56that is the advisor, that is the planner. It is handing off its plan to an executor, a lower level
00:10:03model, in this case, Sonnet. It is executing tools, it is reading, it is writing, but anytime it gets stuck,
00:10:11what does it do? Well, it shares its context with the advisor. The smarter model says, "Hey, here's what's
00:10:17going on. I'm stuck. What should I do?" And if this sounds like a more sophisticated version of
00:10:22everything we've been talking about up until this point, you would be correct. Now, Anthropic hasn't
00:10:26put out any official numbers of what this looks like with Fable as the advisor and having Opus be the
00:10:31executor or Sonnet, but we can make a few assumptions. What you see here is a graph from Opus and Sonnet 4.6.
00:10:40And this is one of the all-time graphs from Anthropic. I mean, just look at these axes,
00:10:43but what you got using advisor mode was a Sonnet that performed better for cheaper. So it was overall,
00:10:49it was just more effective. And you see that reflected here is well across multiple benchmarks.
00:10:54Now to actually use advisor in this way, you can't have your model set to Fable 5 because whatever
00:10:59model you have set, that is the model that is the executor. That's the model that's actually writing
00:11:04the code. So if I want Fable 5 as the advisor and I want Opus actually doing everything, then I need to
00:11:10make sure my model is set to Opus. Then I just need to do forward slash advisor. And then that's when
00:11:15you set the advisor model. So I do forward slash advisor Fable. Now Fable is the one that's going to
00:11:22be essentially telling Opus what to do. So if you're someone who really loves the idea of Fable purely
00:11:26acting as the architect, the conductor, and letting the lower level models do everything, this is
00:11:31definitely a path you should try out. So those are five quick tips for reducing your Fable 5 usage
00:11:36while still getting the most you can out of this amazing model. Hopefully Anthropic is nice to us
00:11:41and then just keep it on the pro and max plan. That would be great. And also, by the way, if you
00:11:45give us more than 50% of the weekly limit, that would be awesome too. But until then, we're going to
00:11:50work with what we have. So as always, let me know what you thought. Make sure to check out Chase AI
00:11:55Plus if you want to get your hands on my Cloud Code Masterclass. And besides that, I'll see you around.