Claude Fable 5.1 vs GPT-6 Astra: the real cost comparison (September 2026)

Almost every "which model is cheaper" post compares rate cards and stops. That's the least useful comparison you can make, because the rate card is not what you pay — and in this particular matchup, the rate cards are identical.

Claude Fable 5.1 costs $10 per million input tokens and $50 per million output.^[1] GPT-6 Astra costs $10 per million input tokens and $50 per million output.^[3] Same number, both columns.

So the interesting question isn't "which is cheaper per token." It's which finishes your task for less money — and that turns on four things the rate card doesn't show: how many tokens each model burns, how its tokenizer counts them, what a cached prefix costs to re-read, and which of your workloads can go through a batch queue.

The rate card, for completeness

Claude figures are Anthropic's first-party API rates.^[2] Astra's fast mode doubles to $20 / $100 for up to twice the throughput.^[4] Cache writes cost 1.25x base input for a 5-minute TTL and 2x for an hour; reads cost 10% of base input on every Claude model except Fable 5.1, which reads at 2.5%.^[2]

What each model actually costs per task

Four realistic workload shapes at list rates, no caching or batching:

The ratios are constant, because the two frontier models share a rate card:

• Astra vs Fable 5.1 — 1.00x. Identical at every shape.
• Opus 5 — 0.50x. Exactly half, on both columns.
• Sonnet 5 — 0.20x. One fifth.
• Haiku 4.5 — 0.10x. One tenth.

That last row is the one people skip. If your workload is classification, extraction or routing, you are choosing between $12.50 and $1.25 for the same job.

The tokenizer trap

Here's the thing that quietly invalidates most cross-vendor price comparisons: a "token" is not a fixed unit. Claude 4.7 and later use a newer tokenizer that produces roughly 30% more tokens for the same text than earlier Claude models.^[2]

Read that again in cost terms. A model at $10/MTok on a tokenizer that splits text 30% finer is, for identical English prose, effectively $13/MTok against the older tokenizer. Anthropic is explicit that the change buys performance, but it means you cannot compare Claude 4.6-era rates to Claude 5-era rates by looking at the dollar figure alone.

The same trap applies across vendors, only worse — Astra and Fable 5.1 use entirely different tokenizers, and neither publishes a conversion factor. Two models at $10/MTok can differ by double digits on the same input.

The only reliable move is to measure your own corpus. Anthropic exposes a token counting endpoint for exactly this; run a representative sample through it before you trust any per-token comparison, including the tables above.

Where the price actually diverges

Caching, and when it pays for itself

Long-context agentic work re-reads the same prefix — a system prompt, a codebase map, a spec — on every turn. Cache writes cost more than plain input, so the question is how quickly the write pays back:

In plain terms: the first cache read has already paid for a 5-minute write, and two reads cover the 1-hour write.^[5] If your prefix is stable and you touch it more than once, not caching is simply leaving money on the table.

What that does to a real bill — 10,000 agentic runs a month, 200K in and 20K out each, with 80% of input served from cache:

Fable 5.1 saves a larger share than the rest precisely because of the 2.5% read rate. And note the shape of that table: a cached Fable 5.1 workload ($14,400) costs less than an uncached Opus 5 one would at scale — caching moves you further than model choice does.

I could not find a published cached-input rate for Astra in any source I checked, so it is genuinely not comparable here rather than merely worse. If you're pricing a long-context agent on Astra, that is the number to get from OpenAI before you commit.

Batching: a flat 50% off, both columns

Anything that doesn't need an answer this second — overnight classification, backfills, eval runs — goes through the Batch API at half price on input and output:^[6]

Batched Fable 5.1 costs exactly what standard Opus 5 costs. Batched Haiku 4.5 puts that 500-document job at $0.63. Batching and caching also stack.^[2]

This is the highest-leverage change most teams haven't made, because it costs nothing in quality. You give up latency you weren't using.

Fast mode cuts both ways

Anthropic's fast mode on Opus 5 is priced at $10 / $50 — which is to say, Opus 5 at up to 2.5x output speed costs exactly what Fable 5.1 and Astra cost at standard speed.^[2] Astra's fast mode, by contrast, doubles to $20 / $100.^[4]

If latency is what you're buying, those are very different deals for the same money.

Effort: the lever inside a single model

Before you switch models to save money, try spending less on the one you have. Claude models take an effort parameter — low through max, defaulting to high — that controls how much thinking and output the model spends on a request.^[7]

This matters more than it sounds, because effort and model choice are substitutes. Lower effort on a newer model often matches or beats higher effort on the previous generation, and one model means one cache namespace — caches are model-scoped, so a multi-model cost cascade forfeits cache reuse across its tiers.^[7]

Which workloads repay higher effort is a property of the workload, not a global setting. Coding and long-horizon agentic work respond strongly to it; chat, classification and latency-sensitive routes frequently don't, and run fine at low. Tune it per route, measure on real requests, and raise a default only when the measurement shows headroom.

The line items nobody budgets for

Agentic workloads carry costs that never appear in a rate-card comparison:^[2]

• Tool definitions are input tokens. Declaring the computer-use toolset adds about 4,520 input tokens to every request; the browser toolset adds about 6,610. On Fable 5.1 that's $0.066 per request before the model does anything — $661 a month at 10,000 requests, or $330 on Opus 5. For a computer-use comparison, that overhead is not a rounding error.
• Screenshots are images. Every screenshot and zoom returned to the model bills as image input, and computer-use loops generate a lot of them.
• Web search is metered separately at $10 per 1,000 searches, on top of tokens. Web fetch adds no fee beyond the tokens of what it pulls in — and a research PDF is ~125,000 of them.
• US-only inference costs 1.1x across every category, including cache reads, if you pin inference_geo for data residency.

So which should you pick?

Cost only decides this after capability does. On the benchmarks, Astra is clearly ahead on computer use and terminal work and roughly level with the Claude frontier on conventional coding — I went through those numbers, and which survive an independent harness, in what GPT-6 Astra can actually do.

Given identical rates, that leaves a short decision list:

• Computer use, browser automation, terminal-heavy agents → Astra. Ahead on the benchmarks, same price as Fable 5.1. Budget for the toolset overhead either way.
• Long-horizon agentic work with a big reusable prefix → Fable 5.1, on the 2.5% cache read. A cached-context-heavy loop is where its economics are genuinely unmatched.
• Everyday coding and product work → Opus 5. Within noise of the frontier on conventional coding at exactly half the price. This is the default for most teams, most of the time.
• High volume, simple decisions → Sonnet 5 or Haiku 4.5, batched. A tenth of the price, and the quality gap on classification-shaped work is usually invisible.

The meta-point: caching, batching and effort tuning move your bill more than the model choice does.^[7] A cached, batched Fable 5.1 workload can undercut a naive Opus 5 one. Get those three right before you argue about rate cards.

Frequently asked

Is GPT-6 Astra more expensive than Claude?

Not more expensive than Claude Fable 5.1 — they are identical at $10 / $50 per million tokens. It is twice the price of Claude Opus 5 ($5 / $25) and ten times the price of Claude Haiku 4.5 ($1 / $5).

Why is Astra described as 2.5x the price at launch?

Because that comparison is against GPT-5.6 Sol, its predecessor, at $4 / $20. Against the current Claude frontier model it is a like-for-like rate.

Does a 1M-token context window cost more per token?

No. On Claude 4.6 and later the full 1M-token window is standard-priced — a 900K-token request bills at the same per-token rate as a 9K-token one, and caching and batch discounts apply across the whole window.^[2] The window is a ceiling, not a surcharge.

What's the cheapest way to run frontier-quality work?

Cache aggressively, batch anything not latency-sensitive, tune effort down until quality moves, and drop to Opus 5 unless your evals show it falling short. Those four together typically matter more than which frontier model you picked.

How do I compare cost per task rather than per token?

Log usage on every response — input, output, cache_read_input_tokens and cache_creation_input_tokens — and divide your spend by completed tasks, not requests. A cheaper request that needs three retries to finish the job is not cheaper.

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Rates verified against Anthropic's pricing documentation and OpenAI's Astra announcement on 5 September 2026. Model pricing changes; check the linked pages before committing a budget.