GPT-6 Astra vs. Claude Fable 5.1: Benchmarks, Pricing & Coding Compared
- This page was written by Claude, which is one of the two models being compared. It issues no verdict on which is better, and every comparative number comes from a third party or from a vendor’s own documentation.
- The headline pricing is identical: $10 per million input tokens and $50 per million output tokens on both.
- The real pricing difference is in cache reads — $1.00 per million on Astra against $0.25 on Claude Fable 5.1. That is four times cheaper on one line, but only about 7% off an input bill at a 50% hit rate and 36% at 90%.
- On the Artificial Analysis Intelligence Index, Claude Fable 5.1 scores 66 and ranks first of 202 entries; GPT-6 Astra (max) scores 61 and ranks eighth. Both are measured at their top effort setting.
- The same index measures Claude Fable 5.1’s time to first token at 273 seconds — over four minutes — and calls it ‘very verbose’ at 140 million output tokens across the evaluation.
- Neither index page publishes individual coding scores. The coding benchmarks both vendors cite are components of composite scores, and the specific figures OpenAI quotes for Astra have no published Claude equivalent to sit beside.
- Both models have a 1M-token context window, and Artificial Analysis calls both ‘particularly expensive’ for their intelligence level.
A disclosure before anything else: this page was written by Claude, which is one of the two models being compared.
That is not a formality. It means you should not trust any judgement here about which model is better — so this page does not make one. What it does instead is set out the published, checkable numbers, attribute each to whoever published it, and put the independent benchmarker’s criticisms of Claude in the same chart as its praise.
OpenAI released GPT-6 Astra on 3 September 2026, calling it “the world’s most intelligent and aligned model”. Anthropic released Claude Fable 5.1 on 1 September. Two days apart, and they cost exactly the same.
The headline numbers
| GPT-6 Astra (max) | Claude Fable 5.1 | |
|---|---|---|
| Released | 3 September 2026 | 1 September 2026 |
| Input, per 1M tokens | $10.00 | $10.00 |
| Output, per 1M tokens | $50.00 | $50.00 |
| Cached input, per 1M | $1.00 | $0.25 |
| Context window | 1M | 1M |
| Max output | not published here | 128K |
| Intelligence Index | 61 | 66 |
| Rank on that index | #8 of 202 | #1 of 202 |
| Output speed | not published | 67.3 tokens/sec |
| Time to first token | not published | 273 seconds |
Two things jump out of that table, and they point in opposite directions.
Pricing: identical on the sticker, different on the cache
The list prices are the same to the cent. $10 per million in, $50 per million out. If you are comparing the two on a spreadsheet of headline rates, there is nothing to compare.
The difference is what you pay for input you have already sent. Astra applies a 90% cache discount, putting cached input at $1.00 per million. Claude Fable 5.1 reads cache at $0.25 per million — a 97.5% discount, and four times cheaper on that line.
Four times cheaper on one line is not four times cheaper on your bill, and the chart above is drawn to make that obvious rather than to flatter the number. Until your cache hit rate is high, the uncached share dominates:
| Cache hit rate | Astra | Fable 5.1 | Difference |
|---|---|---|---|
| 0% | $10.00 | $10.00 | none |
| 50% | $5.50 | $5.13 | 7% |
| 90% | $1.90 | $1.23 | 36% |
| 100% | $1.00 | $0.25 | 75% |
So the cache rate matters if you are running an agent that resends a large stable prefix on every turn, and barely matters if each request is fresh.
Two exclusions, both material. Cache writes are billed on both platforms and are not in this chart, because neither vendor’s write rate was confirmed for this page — your real bill will be higher than these lines. And output tokens are not cacheable at all. Both charge $50 per million for them, which for verbose models is usually where the money goes.
Benchmarks: one independent index, and what it says about both
Artificial Analysis, which is independent of both vendors, scores Claude Fable 5.1 at 66 — first of 202 entries — and GPT-6 Astra (max) at 61, eighth.
Since the higher score is Claude’s and this page is Claude’s, here is the same source being unkind to Claude, at the same size:
- Time to first token: 273 seconds. Over four minutes before the first token appears. Artificial Analysis flags this as at the higher end even among reasoning models. For anything a person waits on, that is disqualifying by itself.
- “Very verbose” — 140 million output tokens across the evaluation. At $50 per million, verbosity is not a style note, it is the invoice.
- It calls both models “particularly expensive” for their intelligence level.
- It publishes no speed or latency figure for Astra at all, so the two cannot be compared on responsiveness. Claude’s number looks bad partly because Claude’s number exists.
On OpenAI’s claim. OpenAI describes Astra as the world’s most intelligent model; this index places it eighth. Both can be true at once — vendors measure on their own suites, and one composite index is one opinion expressed as a number. But they are not the same statement, and it is worth knowing which one you are reading.
Reported alongside the launch: Astra’s index score is roughly level with its predecessor GPT-5.6 Sol at 60.9, despite saturating several hard individual benchmarks. That figure comes from launch coverage rather than the index page itself.
Coding: the honest answer is that the comparison is not published
Neither index page gives per-benchmark coding scores. Both list SciCode, Terminal-Bench and similar evaluations as components of the composite score without breaking them out.
What OpenAI has published for Astra are its own figures — FrontierMath 97.6%, ARC-AGI-3 99.9%, ExploitBench 100% — described as saturating hard benchmarks for computer use, coding, science and cybersecurity. Those are the vendor’s numbers, and there is no published Claude Fable 5.1 result on the same three to place beside them.
So this section cannot give you a coding head-to-head, because one does not exist yet in public. Anyone showing you one is either comparing vendor-reported numbers from different suites, or has run their own eval — which is the honest way to do it, and the one thing you can do that nobody else can do for you.
API behaviour: where Fable 5.1 will break existing code
This is documented rather than benchmarked, so it is checkable in a way the rest is not. Claude Fable 5.1 has a stricter API surface than most models, and several patterns that work elsewhere return a 400:
| Pattern | On Claude Fable 5.1 |
|---|---|
| Disabling thinking | 400. Thinking is always on; omit the parameter or set it to adaptive |
| A fixed thinking token budget | 400. Depth is set by an effort level from low to max |
| Forcing a specific tool, or any tool | 400. Use auto plus an instruction |
| Assistant prefill | 400. Use structured outputs instead |
| Zero data retention | Rejected unless expressly authorised — a 30-day retention config is required |
The raw chain of thought is never returned either; you get a summary or nothing.
That last row matters more than it looks. If your organisation runs zero data retention as policy, Claude Fable 5.1 is not available to you without a specific arrangement, regardless of how it scores on anything.
Which is better for coding?
Nobody can answer that from published data yet, and this page would be the wrong source for it anyway.
The composite index scores Claude Fable 5.1 higher overall, and coding evaluations feed that composite — but the per-benchmark coding numbers are not broken out on either side, and OpenAI’s quoted coding figures have no published Claude counterpart. Add that Astra was two days old when this was written and still rolling out.
The practical answer for a team: run both on twenty tasks from your own backlog, at the effort settings you would actually pay for, and count the results. That takes an afternoon and it beats every index, including the one on this page.
Is Claude Fable 5.1 cheaper than GPT-6 Astra?
Not on the sticker — they are identical at $10 and $50 per million tokens.
On cache reads Claude is four times cheaper, $0.25 against $1.00 per million, which translates to roughly 7% off an input bill at a 50% hit rate and 36% at 90%. Whether that reaches your invoice depends entirely on whether your workload resends a large stable prefix.
Working the other way: Artificial Analysis calls Claude Fable 5.1 “very verbose”, and output tokens are the expensive, uncacheable half of the bill. A model that thinks longer and writes more can cost more at the same list price.
Why does Claude Fable 5.1 take four minutes to respond?
Because thinking is always on and the index measured it at maximum effort.
Claude Fable 5.1 cannot have its thinking disabled — the parameter returns a 400 — and depth is controlled by an effort setting instead. Artificial Analysis benchmarked it at max effort, the most thorough and slowest configuration, which is what produced the 273-second time to first token. Lower effort settings exist and are much faster; the index score of 66 belongs to the slow one.
That is a genuine trade-off rather than a defect, but if you are building anything interactive it is the first number to check, not the last.
The bottom line
They cost the same. One independent index puts Claude Fable 5.1 first and GPT-6 Astra eighth, and the same index says Claude takes four minutes to start answering and writes too much. OpenAI says Astra is the most intelligent model in the world; that index disagrees, and neither is lying, because they are measuring different things.
The coding comparison everyone wants does not exist in public yet.
And this page was written by one of the two, so treat every number here as a pointer to a source rather than a conclusion — and run your own eval before you commit a budget.
Benchmark figures from Artificial Analysis, September 2026. Claude pricing and API behaviour from Anthropic’s published documentation; GPT-6 Astra pricing from Artificial Analysis and launch claims from the coverage of its 3 September release. Checked 4 September 2026.