Transcript
00:00:00I'm going to teach you how to combine the powers of GPT-6 Astra and Claude Fable 5.1
00:00:05because the question you should be asking is not which of these two models is better,
00:00:09it's how can we get the most out of both of them.
00:00:12Furthermore, how can we get the most out of all the models under the open AI and anthropic umbrella?
00:00:17Because oftentimes, we are working on features and problems that don't even require the power of Astra
00:00:22or require the power of Fable.
00:00:24What about things like Luna and Terra?
00:00:26When do we bring those into the fold when we need cheap models to execute simple tasks,
00:00:31even if we're working inside of something like Claude Code?
00:00:33Well, luckily, this is a riddle that is pretty simple to solve,
00:00:36and I'm going to walk you through it in today's video.
00:00:38Now, I will be giving you a set of skills that make this entire process super easy to execute.
00:00:44I have an updated version of Claudex Loop, which includes Astra,
00:00:47and a brand new Claudex Route skill,
00:00:49which makes it really easy to figure out which of these models I should be using.
00:00:52But before we go into the skills section, let's kind of talk about why we even need this in the first place.
00:00:58Now, at this point, we know there's kind of two big players when it comes to the best frontier model in the game.
00:01:02We have GPT-6 Astra and Claude Fable 5.1.
00:01:05We are coming up with huge tasks.
00:01:08We're planning something from scratch.
00:01:09It's a big project, and we want to know who's going to come up with it.
00:01:12These are the two models we are going to lean on.
00:01:15But another area that doesn't get talked about as much is what do we do when we have some sort of simpler feature or simpler task
00:01:22that just doesn't require Fable 5.1, just doesn't require Astra,
00:01:25because these are extremely expensive models.
00:01:27They both cost the same.
00:01:28We're looking at $10 per input and $50 per output.
00:01:31So if you're someone who's on like API pricing, for example, this can be prohibitive.
00:01:36On the other end of the spectrum, we have models like Claude Sonnet 5, as well as Terra and Luna.
00:01:43If you're someone who has used Claude Sonnet 5 lately,
00:01:46I think you can agree that this model leaves something to be desired,
00:01:50especially when we compare it to the supposed benchmarks and the performance that we've seen.
00:01:54Like, it just really doesn't feel great.
00:01:56And so if you're someone who has been using primarily Anthropic models over the last few months,
00:02:01there is this gap where if I want a model that's going to be cheap and effective,
00:02:06I just can't really find it in the Anthropic library.
00:02:10But if we look at OpenAI, they have multiple models that satisfy this requirement
00:02:16when it comes to Terra and Luna.
00:02:18Now, Terra is slightly more expensive than Sonnet 5 when we talk about token to token,
00:02:24but as I'll show you in a minute, it's cheaper overall because it's more token efficient.
00:02:28And what really is a standout is Luna.
00:02:32This is like what Haiku should be if they hadn't completely abandoned Haiku
00:02:35and just left it in the dumpster months ago.
00:02:37We're looking at $0.20 per input and the cash is $0.02 and output is $1.20.
00:02:44Like, this is basically free compared to everything else.
00:02:48And like I'm going to show you, this has outputs and this has performance
00:02:51that matches or exceeds Sonnet 5.
00:02:54So this idea of being able to call on OpenAI models is really, really important
00:02:59if you are someone who's been mostly an Anthropic user, especially if you use APIs.
00:03:04And even if you're someone who's on, say, the 20x plan with Anthropic
00:03:08and isn't on any plan with OpenAI,
00:03:11well, maybe it makes sense to just have sort of a $20 a month plan
00:03:13specifically to make these sort of Luna calls and save on tokens.
00:03:17Now to quickly illustrate this idea, we're taking a look at the DeepSuite benchmark.
00:03:20We have Sonnet here in the orange and then Terra and Luna.
00:03:24So straight up in terms of performance, Terra and Luna perform better than Sonnet.
00:03:30At max, Sonnet is hitting 54%.
00:03:33Max on Terra is 70%.
00:03:35Max on Luna is 67%.
00:03:37But what really is the difference, though, is the cost.
00:03:41You know, when we look at max at Sonnet, $26 for average cost per task versus Luna, 60 cents.
00:03:48So, and then for Terra, $4.
00:03:52Now this becomes a little less pronounced as we go down sort of the effort level.
00:03:56But even on low, you know, $2.19 for Sonnet versus one cent for Luna,
00:04:02granted it's not doing anything for you.
00:04:04But with Terra, 34 cents.
00:04:07And so there is this issue with the Anthropic models where there is no great bang for your buck model.
00:04:13And Sonnet is just like always at risk of just going nuts in terms of the cost
00:04:18and then just like taking way too many tokens to complete these things.
00:04:21We just don't see that with Terra and Luna.
00:04:23In fact, with Luna in particular, going from low all the way to max barely increases the cost
00:04:27and just like completely spikes in terms of its effectiveness.
00:04:30So again, real value to be had here and being able to call down these models.
00:04:34Now the second reason you want to be able to combine the power of open AI and Anthropic
00:04:37is something I've talked about at length in some of my previous videos dealing with CloudX Loop.
00:04:42And that is the idea that if I have one model execute, I should have a different model take a look and evaluate its work.
00:04:49So for example, if I have Claude Fable 5.1 come up with a plan for some sort of project,
00:04:56then I probably want Astra to come in there with completely blank context without any of the idiosyncrasies
00:05:02that all the Anthropic models have.
00:05:04And I want it to take a look at that plan, Fable created and say, all right, this is good.
00:05:08This is bad.
00:05:09Here's what we should fix.
00:05:10And ideally, and this is what the CloudX Loop skill does.
00:05:14It gives that feedback back to Fable 5.1.
00:05:17And then Fable says, mm, agree, disagree, sets it back to Astra.
00:05:21Astra says, agree, disagree.
00:05:22And you have this continuous cycle, this loop of these models going back and forth adversarily
00:05:27till they finally reach a point where like, okay, this is good to go.
00:05:31Now there's safeguards in place to make sure that it just doesn't go on and on forever and burn all your tokens.
00:05:36But even though on the surface that might seem kind of expensive, it's going to save you tokens in the long run
00:05:41versus having to iterate after we've already spent a bunch of time building things.
00:05:46And this is something we can extend to a lower level, right?
00:05:48We could have Opus build something and have Terra take a look or have Luna take a look, so on and so forth.
00:05:53The idea is we never want the model that builds to be the model that evaluates
00:05:58because frankly, it's just not going to do a great job.
00:06:00And these models in general tend to grade their own work poorly.
00:06:05I mean, in a very favorable light.
00:06:07Fable's always going to think Fable's work is great.
00:06:09Astra's always going to think Astra's work is great, so on and so forth.
00:06:13So that's why we're doing it.
00:06:14And now we'll jump into the skill.
00:06:15But first, a quick word from today's sponsor, me.
00:06:19So inside of Chase AI+, I have just released both a Claude code and a Codex masterclass.
00:06:24So if you're someone who's trying to figure out how to use either of these tools
00:06:28and you don't come from a technical background, this is the perfect place for you.
00:06:32I update this every single week.
00:06:33We focus on real examples.
00:06:36So if this sounds like something you really want to master, definitely check us out there.
00:06:40It will be a link in the pinned comment.
00:06:42Hope to see you there.
00:06:43So all the skills you need are found in this GitHub repo, the Claudex loop GitHub repo.
00:06:47I will put a link to it in the pinned comment.
00:06:49Now, the two big ones we're going to talk about today is Claudex loop,
00:06:52which I've done videos on the past on, and the new Claudex route.
00:06:56So let's talk about Claudex route first because this is the new one.
00:06:58Big picture, if you run Claudex route, and you can run this inside of Claude code or inside
00:07:03of Codex, and you give it a brief description of what you're trying to do, like, hey, I'm
00:07:07trying to do this particular task.
00:07:09Can you use Claudex route to figure out what model I should use?
00:07:11Well, the skill isn't going to tell you, here's the model that makes the most sense for you.
00:07:15Maybe it's a very simple task, so it's going to use Luna.
00:07:18Or maybe it's something that, hey, requires a lot of ambiguous work.
00:07:21It's pretty difficult.
00:07:22Let's use Astra.
00:07:23So it takes away any sort of thought you need to do in terms of what model to use, because
00:07:27sometimes it's kind of like a gray area.
00:07:29And this is basing it off the actual model information that has been posted for all the
00:07:34models by the Frontier Labs, including some of the usage guidelines.
00:07:37And it takes into account, like, the actual cost.
00:07:40And what that looks like in reality is, let's say I'm inside of Claude code, and I do slash
00:07:44Claudex route, and I say, hey, I'm starting this new project.
00:07:48I think it's going to be pretty complicated.
00:07:49What model should I use?
00:07:50And it says, go use Astra.
00:07:52Well, it's just going to send all the relevant information to a headless instance of Codex.
00:07:58So think of it pulling up Codex in the CLI, but it's invisible.
00:08:01Codex is going to do its thing, do all of its work, and then it will send all the information
00:08:05back to Claude code saying, here's what I did.
00:08:07Here's where that sort of code is.
00:08:09Now, this also works in the reverse.
00:08:11Like I said, you can start this from Claude code or Codex, doesn't really matter.
00:08:15Now, the next skill you need to pay attention to is Claudex loop.
00:08:18I've done full videos on Claudex loop, taking you from the very beginning to the end.
00:08:22So I will link that above if you want to watch that.
00:08:24For now, I'll kind of just go over some of the changes I've made to it and what it sort
00:08:28of does at a high level.
00:08:30So if Claudex route is for like one-off features, like, hey, what model should I use?
00:08:33Go send them to do it.
00:08:35Claudex loop is sort of for really big implementations.
00:08:38That walks you through, that kind of holds your hand from planning stage all the way to execution.
00:08:43And it's all about that idea I talked about earlier of, hey, if model A executes, then
00:08:49model B checks its work.
00:08:51So first things first, if I'm inside of Claude code or inside of Codex, it's going to use
00:08:57the opposite model to sort of be the inspector.
00:09:00So, hey, if I'm inside of Codex, right?
00:09:03Who's going to be doing the plan?
00:09:05Well, it's going to be Astra.
00:09:05Who's going to take a look at their plan?
00:09:07It's going to be Claude code and vice versa, depending on what platform you're on.
00:09:11From there, it goes through four stages.
00:09:13So first things first, it's going to do some reconnaissance, which just means it's going
00:09:17to do deep research.
00:09:18And let's say you're saying, hey, I want to clone something like Whisperflow.
00:09:22Well, it's going to spawn a bunch of subagents, figure out what Whisperflow actually is, what
00:09:26we need to think about.
00:09:27Has anyone already cloned this?
00:09:29And like really sort of validate your assumptions.
00:09:31From there, it's going to ask you a few questions.
00:09:34Be like, okay, what do you want to change about it?
00:09:36What do you care about?
00:09:37What is your vision for this project?
00:09:39Before finally, it executes the plan.
00:09:41Well, it doesn't execute the plan.
00:09:42It builds out the plan.
00:09:43So Astra builds out the plan, sends it to Claude code.
00:09:46They have their back and forth for several rounds until they finally reach an approved verdict.
00:09:51From there, we kind of repeat that process.
00:09:53And it's going to ask you if you want Astra or Fable to actually execute it.
00:09:58One of them executes the plan.
00:09:59And then the opposite one takes a look at the executed plan and says, hey, here's what you
00:10:04missed.
00:10:04Here's what looks good.
00:10:06So at the end of the day, the Claudex loop is really just meant to have these opposite
00:10:12models sort of check for blind spots that you're not going to be able to figure out.
00:10:15Because let's be honest, for many of us, what we're building inside of Claude code and Codex
00:10:20goes well beyond the scope of our coding abilities.
00:10:24So are you going to double check what Codex comes up with and Astra comes up with?
00:10:29Are you going to double check what Fable comes up with?
00:10:31Probably not.
00:10:32But it will give you something of like a warm and fuzzy feeling.
00:10:35Knowing that Astra looked at Fable's work or Fable looked at Astra's work.
00:10:40And that is sort of the whole point of the Claudex loop.
00:10:42I think it's the easiest way to kind of get the best out of both of these models without
00:10:46some like super convoluted setup.
00:10:49So if you want to get this working for yourself, just go ahead, copy the URL here, point Claude
00:10:55code at it, point Codex at it, and it will install for you.
00:10:58So that's where I'm going to leave you guys.
00:10:59Remember, if you want to see the Claudex loop actually executed from beginning to end,
00:11:03check out that video I linked earlier.
00:11:04I go into depth showing here's what GPT found.
00:11:07Here's all the errors.
00:11:08Here's what it looks like when they have their back and forth.
00:11:10And here's sort of what the final setup sort of looks like.
00:11:12But besides that, I think we're in such an awesome place where we have these dueling models.
00:11:17Like Fable 5.1 is great.
00:11:19GPT-6 Astra is also great.
00:11:21So I don't really think there should be an either-and thing.
00:11:24And I think the sort of pricing of all these can kind of scare you off.
00:11:28Like, should I be on a 20X here and a 20X there?
00:11:30Should I be paying 400 bucks a month?
00:11:32I don't think so.
00:11:33I think for a lot of people who have kind of been, you know, Claude-pilled for the last
00:11:376, 9, 12 months, you've been probably on a $200 plan.
00:11:41I would suggest going half and half.
00:11:42Do the 5X with GPT, do the 5X with Anthropic, and really see what you like.
00:11:48You know, maybe you like using them in tandem.
00:11:50Maybe you love GPT-6.
00:11:51I think in general, though, when it comes to all these AI tools and how fast everything
00:11:55is moving, we need to be tool agnostic.
00:11:58And it's really hard to be tool agnostic if you don't play around with all of them.
00:12:01So that's my suggestion.
00:12:03Definitely check out the skill.
00:12:04Let me know what you think.
00:12:05Let me know if you have any improvements for it.
00:12:07Always trying to keep it updated.
00:12:08And besides that, I'll see you around.
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