GPT-6 Astra vs Claude Fable 5.1 vs Opus 5.5: Why Model Routing Wins

Prices checked September 26, 2026.

GPT-6 Astra and Claude Fable 5.1 cost the same per token, and neither is the best buy for most of your workload. Both list at $10 per million input tokens and $50 per million output tokens. Three weeks after they shipped, Anthropic released Claude Opus 5.5 at $4 and $20, and OpenAI released GPT-6 Sol at $2 and $10 and GPT-6 Luna at $0.10 and $0.50. Gemini 3.8 Flash sits at $0.75 and $3.75 on promotional pricing.

That spread, 100x on input between the cheapest and most expensive model here, is the whole argument for model routing. This guide lays out the verified prices, what each vendor says each model is for, what one independent index shows, and how to split your tasks across them.

Which models are we comparing, and when did they ship?

Seven models matter for this decision in late September 2026. Claude Fable 5.1 shipped September 1, Gemini 3.8 Flash September 2 and GPT-6 Astra September 3. On September 22, Anthropic added Claude Opus 5.5 and OpenAI added GPT-6 Sol and GPT-6 Luna. Claude Sonnet 5 dates from June 30.

The first wave was flagships at flagship prices. The second wave changed the math. Anthropic's own models overview now tells developers to "start with Claude Opus 5.5 for most workloads" and to use Fable 5.1 for demanding reasoning and long-horizon agentic work, "or when your evals on Claude Opus 5.5 at higher effort still fall short." OpenAI split GPT-6 into three tiers the same day. For a closer look at each release, see our posts on Claude Opus 5.5 and GPT-6 Sol and Luna.

(Release dates as listed on the Artificial Analysis leaderboard, checked September 26, 2026.)

What does each model cost per million tokens?

List prices range from $0.10 to $10 per million input tokens and from $0.50 to $50 per million output tokens across these seven models. GPT-6 Astra and Claude Fable 5.1 share the top price. Claude Opus 5.5 costs 40% of that. GPT-6 Luna costs 1% of it.

Standard, non-batch API prices, checked September 26, 2026, on OpenAI's pricing page, Anthropic's pricing page and Google's Gemini API pricing page.

Four details change what you actually pay:

• OpenAI long context. On the GPT-6 Astra, Sol and Luna model pages, requests over 272K input tokens are billed at 2x input and 1.5x output for the whole request. Astra becomes $20 and $75.
• Gemini 3.8 Flash promo. Google lists $0.75 and $3.75 through December 31, 2026, then $1.50 and $7.50 from January 1, 2027.
• Anthropic long context. Anthropic bills its 4.6-and-later models at standard rates across the full 1M-token window.
• Batch. OpenAI, Anthropic and Google all list a 50% batch discount on these models.

GPT-6 Sol and Luna also cut the price of the tier they replace. OpenAI's page lists GPT-5.6 Sol at $4 and $20 (promotional) and GPT-5.6 Luna at $0.20 and $1.20. Sol halves both numbers. Luna halves input and cuts output by more than half.

Is GPT-6 Astra or Claude Fable 5.1 the smarter model?

On the one cross-vendor index we cite here, the two are tied, and the newer, cheaper Claude Opus 5.5 scores above both. Artificial Analysis lists Astra and Fable 5.1 at 53 on its Intelligence Index at maximum effort, and Opus 5.5 at 58, ranked first of 172 models on September 26, 2026.

Source: Artificial Analysis LLM leaderboard, checked September 26, 2026. Scores move as the index is revised, so check the live page before you quote them.

Two things stand out. Astra matches Fable 5.1 on the index at under half of Fable 5.1's cost per task in Artificial Analysis's run. And GPT-6 Sol lands 5 points below Astra for about a third of the cost.

Why should you be careful with launch-week benchmark tables?

Because the numbers can move after publication. Fortune reported on September 4 that OpenAI changed several evaluation figures in its GPT-6 Astra announcement across archived snapshots taken between 2:23 p.m. and 5:20 p.m. on launch day. OpenAI told Fortune that most evaluations "have noise within a few percentage points."

That does not make Astra a weak model. It means a vendor's own launch table is a starting point. Fortune's report is a good reason to prefer an index run by a third party, and a better reason to run your own evals on your own tasks. A public score tells you which models to try first. It does not tell you which one handles your invoices, tickets or codebase best.

Why does model routing beat picking one model?

Routing sends each task to the cheapest model that passes your evals for that task, instead of running everything through one default. With list prices 100x apart on input, a single-model setup either overpays on easy work or underpowers hard work. Most production workloads have plenty of both.

Here is list-price arithmetic for one example job: 10 million input tokens and 2 million output tokens a month of ticket classification. This is a calculation from the prices above, not a measured result.

If Luna or Flash classifies those tickets well enough on your eval set, running them through a flagship costs 13 to 100 times more for no gain. If your hardest multi-step agent task only passes on Opus 5.5 or Astra, a cheap model saves nothing, because failed runs and retries cost more than the token difference.

How should you split tasks across these models?

Start from the vendor's stated purpose, then let your own evals decide. Use a flagship for hard multi-step agent work, a mid tier for everyday coding and agent steps, and the cheapest tier for high-volume classification, routing and extraction. Promote a task to a bigger model only when the smaller one fails your test set.

The router itself can be a cheap model. A Luna or Flash call that labels a request "simple" or "hard" costs a fraction of a cent and decides where the expensive tokens go. Pair it with evals per task type and a re-check whenever a lab ships, which in September 2026 meant twice in one month.

If you run into Astra rate limits while testing, our guide to avoiding GPT-6 Astra usage limits covers the fixes, and the Astra API update post explains async tool calling and mid-turn steering. For Anthropic's pricing history, see Claude Fable 5 pricing explained.

What does it take to run a routing setup in production?

You need three parts: an eval set per task type, a router that picks a model per request, and logs that show cost and pass rate per model. Without the evals, routing is guesswork. Without the logs, you cannot tell when a new release should take over a task.

The ongoing work is the part teams underestimate. Each release changes which model is cheapest for a given pass rate, so someone has to rerun the evals and move tasks. AY Automate builds agents with evals, sandboxing and approval gates through our AI agent development service, and builds n8n workflows and AI agents on your existing stack through our AI automation agency work. A routing layer fits inside either one. If you would rather have an engineer own it inside your team, our forward deployed engineers do that work embedded.

FAQ

Is GPT-6 Astra better than Claude Fable 5.1? Neither leads on the cross-vendor index cited here. Artificial Analysis lists both at 53 on its Intelligence Index at maximum effort as of September 26, 2026, with Astra at $3.26 per task against $7.63 for Fable 5.1 in its run. Both list at $10 and $50 per million tokens. Your own evals should decide per task.

Is GPT-6 Astra cheaper than Claude Fable 5.1? Not per token. Both list at $10 per million input tokens and $50 per million output tokens on standard API pricing, checked September 26, 2026. Astra costs more above 272K input tokens, where OpenAI bills 2x input and 1.5x output. Anthropic bills Fable 5.1 at standard rates across its full 1M-token window.

How much do GPT-6 Sol and GPT-6 Luna cost? OpenAI lists GPT-6 Sol at $2 per million input tokens and $10 per million output tokens, and GPT-6 Luna at $0.10 and $0.50. Both are half or less of their GPT-5.6 counterparts on the same pricing page. Cached input costs $0.20 on Sol and $0.01 on Luna.

Should I use Claude Opus 5.5 or Claude Fable 5.1? Start with Opus 5.5 for most work. Anthropic's models overview recommends it for most workloads and says to move to Fable 5.1 when your evals on Opus 5.5 at higher effort still fall short. Opus 5.5 lists at $4 and $20 per million tokens, 40% of Fable 5.1's $10 and $50.

What is AI model routing? Model routing sends each request to the cheapest model that passes your evals for that task type. A small model can act as the router, labeling requests before an expensive model sees them. With September 2026 list prices ranging from $0.10 to $10 per million input tokens, routing keeps flagship spend on the tasks that need it.

Which model should handle high-volume classification and extraction? Try the cheapest tier first. GPT-6 Luna lists at $0.10 and $0.50 per million tokens and OpenAI describes it for "focused, high-volume tasks." Gemini 3.8 Flash lists at $0.75 and $3.75 through December 31, 2026. Escalate to GPT-6 Sol or Claude Sonnet 5 only for items that fail validation.

Where does Gemini 3.8 Flash fit? Gemini 3.8 Flash is the second-cheapest model in this group, at $0.75 per million input tokens and $3.75 per million output tokens until December 31, 2026, then $1.50 and $7.50. Google describes it for "long-horizon software engineering, autonomous agents, and complex enterprise workflows." It scores 41 on the Artificial Analysis index at high effort.

How often should I re-check my model routing? Re-check after every major release that touches a model you use. In September 2026 that happened twice: Fable 5.1, Gemini 3.8 Flash and Astra shipped September 1 to 3, then Opus 5.5, Sol and Luna shipped September 22. Rerun your evals and move a task only when a cheaper model passes them.

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Should you standardize on one model?

No. The better bet is a routing layer that you re-tune after each release. Flagship prices stayed at $10 and $50, while the tiers below them got cheaper and better in the same month. If you want help building the router, evals and logs against your real workload, book a 30-minute call.

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Sources, all checked September 26, 2026: OpenAI API pricing, OpenAI GPT-6 Astra model page, OpenAI GPT-6 Sol model page, OpenAI GPT-6 Luna model page, Anthropic pricing, Anthropic models overview, Gemini API pricing, Gemini models, Artificial Analysis LLM leaderboard, Fortune on OpenAI revising Astra's evaluation figures