GPT-6 Astra vs Claude Fable 5.1: same $10/$50, different things for the money
These two carry identical official list prices: $10 per 1M input tokens and $50 per 1M output. Context is near-identical too (Astra 1,050,000, Fable 1,000,000) and both cap output at 128,000. So this page can skip price entirely and ask what the same money buys. Three real differences: Fable's thinking is "Adaptive (always on)" with a default effort of high, while Astra's reasoning tier is selectable from low to max; Fable's reliable knowledge cutoff is June 2026 against Astra's 2026-04-30; and Anthropic's own documentation marks Fable's comparative latency as the slowest of its four tiers.
Updated 2026-09-10
Four official specifications, side by side
Price: identical
OpenAI's documentation lists Astra at $10 per 1M input and $50 per 1M output; Anthropic's documentation lists Fable 5.1 at exactly the same figures. A clean price collision across vendors is rare, and it turns the selection question from "which is better value" into "which capability do I want for the same money".
Fable's thinking cannot be turned off
Anthropic's docs state that Fable 5.1's Thinking is "Adaptive (always on)" with a default effort of high. Astra's reasoning.effort can be set to low, medium, high, xhigh or max (also with no none tier). When you want to hold down per-call reasoning overhead, Astra at least has a dial.
Fable's knowledge cutoff is later
Anthropic gives Fable 5.1 a reliable knowledge cutoff of June 2026; OpenAI gives Astra 2026-04-30, about two months earlier. Note that the two vendors do not necessarily define or measure "knowledge cutoff" the same way, so treat this gap as a pointer rather than a precise scale.
Anthropic's own latency label
Anthropic's model comparison table marks Fable 5.1's comparative latency as Slower, the slowest of Fable, Opus 5, Sonnet 5 and Haiku 4.5. That is the vendor's own label, not a third-party measurement.
The most useful sentence in the official docs
Anthropic's models overview says: if you are unsure which model to use, start with Claude Opus 5 for most workloads; use Claude Fable 5.1 for demanding reasoning and long-horizon agentic work, or when your evals on Claude Opus 5 at higher effort still fall short. In other words, the vendor itself does not recommend its most expensive tier as the default. Opus 5 lists at $5/$25, exactly half of Fable and of Astra. So before putting Astra and Fable side by side, it is worth asking whether your workload has actually reached this tier at all. If it has not, the comparison that matters may be Opus 5 against GPT-5.6 Sol ($4/$20) rather than these two $10/$50 flagships.
How each vendor positions this tier
OpenAI describes Astra as "our most capable model, built for the hardest end-to-end work", recommended for complex reasoning, coding, computer use, research and document creation. Anthropic describes Fable 5.1 as being "for demanding reasoning and long-horizon agentic work". Both point at the same class of task: multi-step work where the model decides for itself when it is done. What differs is the accompanying advice - OpenAI does not tell you to try a cheaper tier first, Anthropic does. That says nothing about which vendor is more honest, but it does mean that following each vendor's own guidance leads you to different defaults.
How to choose: sort by your binding constraint
If your scenario is sensitive to time-to-first-token (interactive completion, live assistants), note that Anthropic itself marks Fable as its slowest tier and that its thinking cannot be disabled. Astra's selectable reasoning tier leaves more room here, or you drop to a cheaper tier entirely.
If the work depends on facts between April and June 2026, Fable's later cutoff is a genuine advantage. But because the two vendors may not define cutoffs comparably, test it with a handful of your own specific questions rather than trusting the labels alone.
The two are the same price, so budget does not separate them - it separates "should I be on this tier at all". Anthropic suggests Opus 5 ($5/$25) for most workloads; the cheaper option on the OpenAI side is GPT-5.6 Sol ($4/$20, marked promotional at least through 2026-11-21). Confirm you need a flagship before choosing whose flagship.
Specification table
GPT-6 Astra (OpenAI)
Model id gpt-6-astra · $10 per 1M input and $50 per 1M output · context 1,050,000 · max output 128,000 including reasoning tokens · knowledge cutoff 2026-04-30 · reasoning.effort supports low / medium / high / xhigh / max, with no none · positioned as the most capable model, built for the hardest end-to-end work. Released 2026-09-03.
Claude Fable 5.1 (Anthropic)
Model id claude-fable-5-1 · $10 per 1M input and $50 per 1M output · context 1M · max output 128K · reliable knowledge cutoff June 2026 · Thinking is Adaptive (always on) with a default effort of high · comparative latency labelled Slower, the slowest of four tiers · positioned for demanding reasoning and long-horizon agentic work. Neighbouring tiers on the same page: Opus 5 $5/$25, Sonnet 5 $2/$10, Haiku 4.5 $1/$5.
Two trade-offs at the same price
Reasons to pick Astra
A selectable reasoning tier means one model covers a cost range from low to max, so you do not pay high-tier reasoning on every trivial subtask; nominally 50,000 more context tokens; and if your tooling already speaks the Responses API, integration friction is lower. The cost is a knowledge cutoff roughly two months earlier.
Reasons to pick Fable 5.1
A knowledge cutoff roughly two months later; always-on thinking means you never decide per call whether to enable reasoning, so behaviour is more predictable; and if your evaluations are already built on the Claude family, moving up within it beats crossing vendors. The cost is the vendor's own Slower latency label and no option to turn thinking off.
How to compare without being fooled by a small sample
The classic mistake in same-price comparisons is running a handful of hand-picked prompts once and concluding from that. Three things make it reliable: use a day of your own real request distribution rather than constructed hard cases; record the three token types separately (input, output, reasoning), because both models produce reasoning tokens billed at the output rate and a single total hides the difference; and record the share of tasks that came out right on the first pass. At equal unit prices, the real cost difference usually comes from rework rather than from the rate card. What you end up with is a conclusion that holds for your workload, which need not match any published benchmark and is considerably more useful.
Running this comparison on QCode
One QCode key calls both gpt-6-astra and claude-fable-5-1; switching is a model id change, with no second set of credentials, no second integration, and no need to open accounts at both vendors. That is the cheapest possible shape for a same-price comparison: send the same batch again with a different model id, and read billing broken out per model in one console. One caveat: the parameter conventions differ - Astra drops several sampling parameters and requires tool calls through Responses, while thinking is always on for Claude - so align those before the run, or you end up comparing integrations rather than models.
Frequently asked questions
Are the prices really identical?
Yes. OpenAI's documentation lists Astra at $10 per 1M input and $50 per 1M output; Anthropic's documentation lists Fable 5.1 at exactly the same figures. Note that cache read and write billing rules differ between the vendors, so long-run bills can still diverge, but the base rates match.
Which has the bigger context window?
Astra is listed at 1,050,000 and Fable at 1M, so Astra nominally carries 50,000 more, a difference of about 5 percent. Both cap output at 128K. At this scale a 5 percent difference is rarely a deciding factor.
Is Fable's non-disableable thinking a problem?
It depends on the scenario. For long-horizon complex work, always-on means predictable behaviour and no risk of quality dropping because a tier was set too low on some call. For high-frequency short tasks, it means you cannot take reasoning overhead to zero, and reasoning tokens bill at the output rate. Astra has no none tier either, but it can at least be set to low.
Should I use Fable 5.1 or Opus 5?
Anthropic's documentation is direct about this: when unsure, start with Opus 5 for most workloads, and move to Fable 5.1 for demanding reasoning and long-horizon agentic work or when evals on Opus 5 at higher effort still fall short. Opus 5 lists at $5/$25, exactly half of Fable. That advice comes from the vendor itself and is worth taking seriously.
Does a two-month cutoff gap matter?
Only if your work genuinely depends on facts between April and June 2026. Also, the vendors may not define or measure cutoffs the same way - Anthropic's wording is "reliable knowledge cutoff" while OpenAI writes "knowledge cutoff". Test with a few questions you actually care about rather than subtracting the two dates.
Can I use both?
Yes, and at equal prices that is often the sensible answer: route by task type and treat them as two styles within one budget tier. The prerequisite remains that the model name is a configurable value in your code. On QCode this is simpler because both models sit behind one key, so routing does not involve a second set of credentials.
Sources
Claude Fable 5.1's specification, price, thinking mode, latency label and knowledge cutoff come from Anthropic's official models overview on docs.claude.com; the "start with Opus 5 for most workloads" guidance is quoted from the same page. GPT-6 Astra's specification and price come from OpenAI's official model documentation at developers.openai.com/api/docs/models/gpt-6-astra. Both were retrieved on 2026-09-10. This page cites no third-party benchmarks and publishes no performance comparison it has not measured.
One key calls both of these models
Change the model id and the same-price comparison runs itself, with billing broken out per model and no second vendor account.
Related pages
Complete GPT-6 guide
Astra specification, pricing and practical usage.
GPT-6 Astra vs GPT-5.6 Sol
Choosing within one vendor: identical windows, 2.5x price gap, split by reasoning tier.
GPT-6 Astra migration guide
The four parameter changes needed to adopt Astra.
Specifications and prices on this page come from both vendors' official documentation, with retrieval dates given under Sources. The vendors may not define or measure knowledge cutoffs comparably, and the Slower latency label is Anthropic's own rather than a third-party measurement. This page publishes no unmeasured performance comparison: how these two models differ on your workload has to be measured with your own traffic.