GPT-6 Astra + Fable 5.1: How to Route AI Models
GPT-6 Astra + Fable 5.1: How to Route AI Models the Smart Way
The question isn't whether GPT-6 Astra or Claude Fable 5.1 is the better model. It's how you get the most out of both — and out of the cheaper models sitting underneath them. Most people pick one frontier model, run every task through it, and burn money on problems that never needed that much horsepower. I do the opposite: I route each task to the model that actually fits it, and I have one model check the other's work. Here's exactly how that works, and the two skills that make it dead simple.
Why Not Just Use One Frontier Model?
Right now there are two clear frontier players: GPT-6 Astra and Claude Fable 5.1. When you're planning a big project from scratch — something ambiguous, high-stakes, built from nothing — these are the two you lean on. They're also priced identically and priced high: roughly $10 per million input tokens and $50 per million output tokens.
Here's the thing. A huge chunk of the work you do inside Claude Code or Codex doesn't need that. You're renaming a function, wiring up a simple component, writing a small script. Paying frontier rates for that is lighting money on fire, especially if you're on API pricing instead of a flat plan.
So the real skill isn't picking a favorite. It's knowing when to reach for the expensive model and when to reach for something cheap that gets the job done anyway.
What Are the Cheap Models — and Is Anthropic Missing One?
This is where it gets interesting, because the two labs are not in the same position.
On the Anthropic side you've got Claude Sonnet 5. If you've used it lately, you probably agree it leaves something to be desired — it doesn't live up to the benchmarks, and Haiku got abandoned months ago. So if you've been an Anthropic-only user, there's a real gap: there's no cheap-and-effective model in the Anthropic library right now.
OpenAI fills that gap with two models: Terra and Luna.
- Terra — slightly more expensive than Sonnet 5 token-for-token, but cheaper overall because it's far more token-efficient.
- Luna — the standout. This is what Haiku should have been. It runs around 20 cents per million input tokens with cached input near 2 cents, and it delivers performance that matches or beats Sonnet 5 for a tiny fraction of the cost.
If you've been living inside Anthropic models, the ability to call OpenAI's cheap models is the single biggest cost lever you're not pulling. Even if you're on Anthropic's 20x plan and pay OpenAI nothing today, a $20/month OpenAI plan purely to make Luna calls can pay for itself in saved tokens.
How Much Cheaper Are Terra and Luna, Really?
Look at the DeepSuite benchmark, which measures performance and cost per task across effort levels. The numbers make the case better than I can.
On raw performance at max effort:
- Sonnet 5 tops out at 54%
- Terra hits 70%
- Luna hits 67%
So both OpenAI cheap models beat Sonnet on the benchmark. But the cost gap is the real story. Average cost per task at max effort:
- Sonnet 5 — about $26
- Terra — about $4
- Luna — about 60 cents
Even at low effort, Sonnet runs about $2.19 per task versus roughly 1 cent for Luna. The pattern that matters most: with Luna, going from low effort all the way to max barely moves the cost while effectiveness spikes. Sonnet, by contrast, is always at risk of going nuts — burning way too many tokens to finish a task. There's just no great bang-for-your-buck model in the Anthropic lineup, and Terra and Luna don't have that problem.
Why Should One Model Check Another Model's Work?
The second reason to combine OpenAI and Anthropic has nothing to do with cost. It's about blind spots.
The model that builds should never be the model that evaluates. Models grade their own work in an absurdly favorable light. Fable always thinks Fable's work is great. Astra always thinks Astra's work is great. Ask a model to review what it just built and it'll wave it through.
So you split the roles. Have Fable 5.1 draft a plan, then bring in Astra with completely blank context — none of the idiosyncrasies the Anthropic models share — to tear that plan apart. This is good, this is bad, here's what to fix. That feedback goes back to Fable. Fable agrees or pushes back, sends it to Astra, Astra agrees or pushes back. You get an adversarial loop of the two models going back and forth until they land on "okay, this is good to go."
And you can run the same pattern one tier down: have Opus build something and let Terra or Luna review it. On the surface an extra review pass looks expensive. In practice it saves tokens, because catching a flaw in the plan is far cheaper than discovering it after you've built the wrong thing.
What Is the Claudex Route Skill?
Claudex Route is the new skill, and it solves the one-off question: which model should I use for this task?
You run it inside Claude Code or Codex, hand it a brief description of what you're trying to do, and it tells you the model that makes the most sense. Simple task? It routes to Luna. Ambiguous, difficult, lots of unknowns? It routes to Astra. It bases this on the actual published model information — including the labs' own usage guidelines — and the real cost of each model.
Here's what it looks like in practice. Inside Claude Code you run /claudex route, say "I'm starting a new project, I think it's going to be pretty complicated, what model should I use," and it comes back with Astra. From there it sends all the relevant context to a headless instance of Codex — think of Codex running in the CLI, but invisible. Codex does the work and reports back to Claude Code: here's what I did, here's where the code lives. It works in reverse too — start from Codex and it can dispatch to Claude Code. The point is it removes the mental overhead of that gray-area routing decision.
What Is the Claudex Loop Skill?
If Claudex Route is for one-off features, Claudex Loop is for the big implementations — it holds your hand from planning all the way through execution. The updated version now includes Astra.
Whichever tool you start in, the loop uses the opposite model as the inspector. Start in Codex and Astra drafts the plan while Claude Code reviews it. Start in Claude Code and it's the other way around. From there it runs four stages:
- Reconnaissance — it does deep research first, spawning subagents to figure out what you're actually building. Say you want to clone something like Whisper Flow. It goes and learns what Whisper Flow is, what you need to think about, whether anyone's already cloned it, and validates your assumptions.
- Questions — it asks what you want to change, what you care about, what your vision for the project is.
- Plan + adversarial review — one model builds the plan and sends it to the other. They go back and forth for several rounds until they reach an approved verdict, with safeguards so it doesn't loop forever and drain your tokens.
- Execution + cross-check — it asks whether Astra or Fable should build. One executes the plan, then the opposite model reviews the finished work: here's what you missed, here's what looks good.
The whole point is having opposite models catch the blind spots you can't. Let's be honest — most of what we build inside these tools goes beyond our own coding ability. You're probably not going to line-by-line audit what Astra or Fable produces. But knowing Astra reviewed Fable's work, or Fable reviewed Astra's, gets you most of the way there without a convoluted setup.
All the skills live in the Claudex Loop GitHub repo. Point Claude Code or Codex at the repo URL and it installs them for you.
Do You Need to Pay for Two 20x Plans?
No. And this is where I think the pricing scares people off unnecessarily.
You do not need to be on a $400/month setup with a 20x plan on each side. If you've been on a $200 Anthropic plan for the last 6 to 12 months, my suggestion is to go half and half — do the 5x plan with OpenAI and the 5x plan with Anthropic, and actually see what you like. Maybe you love using them in tandem. Maybe you find you prefer GPT-6. Either way you've got both in your toolbox.
Because here's the real lesson underneath all of this: with how fast these tools move, you need to be tool-agnostic — and you can't be tool-agnostic if you've only ever played with one of them.
Frequently Asked Questions
Is GPT-6 Astra or Claude Fable 5.1 better?
Neither is universally better — that's the wrong question. They're comparably capable frontier models at identical pricing (~$10 per million input, ~$50 per million output). The win comes from using both: pick one to plan or build, and use the other to adversarially review that work with fresh context.
What is the cheapest good AI coding model right now?
OpenAI's Luna is the standout value — roughly 20 cents per million input tokens with cached input near 2 cents, and it matches or beats Claude Sonnet 5 on the DeepSuite benchmark (67% vs 54% at max effort) at a fraction of the cost. Terra is another strong cheap option that's more token-efficient than Sonnet 5.
Why shouldn't the same model review its own work?
Because models grade their own output in an overly favorable light. Fable thinks Fable's work is great; Astra thinks Astra's is great. Using a different model — ideally from a different lab, with blank context — surfaces blind spots the original model will happily skip over.
What's the difference between Claudex Route and Claudex Loop?
Claudex Route is for one-off tasks — you describe the task and it recommends and dispatches the right model. Claudex Loop is for large implementations — it runs a four-stage flow (reconnaissance, questions, adversarial plan review, execution with cross-check) using opposite models as inspectors throughout.
Do I need to pay for both OpenAI and Anthropic?
You don't need two maxed-out plans. A practical setup is splitting a single budget in half — a 5x plan on each side — so you can route cheap tasks to Luna or Terra and keep frontier models for the hard problems. Even a $20/month OpenAI plan purely for Luna calls can pay for itself in saved tokens.
If you want to go deeper into multi-model routing and adversarial AI workflows, join the free Chase AI community for templates, prompts, and live breakdowns. And if you're serious about building with AI, check out the paid community, Chase AI+, for hands-on guidance on how to make money with AI.
