GPT-6 Astra vs Claude Fable 5.1: Identical Rate Cards, and a Decision That Hangs on Cache Reads

Here is the thing almost every comparison of this matchup gets wrong, and it is the first number a buyer looks for: GPT-6 Astra and Claude Fable 5.1 cost exactly the same. On the two vendors' own documentation, both read on 16 September 2026, Open​AI lists GPT-6 Astra at $10.00 per million input tokens and $50.00 per million output tokens, and Anthrop​ic lists Claude Fable 5.1 at $10.00 and $50.00. The multiple is 1.0. There is no premium to justify, no discount to capture, and no per-token arithmetic that separates them. One framing note before the numbers: GPT-6 Astra itself is from 3 September 2026, so this is a reference page for two models that have already shipped, not launch coverage. What changed inside the last seven days is the evidence rather than the models — the first independent head-to-head measurement of the pair landed on 9 September, and Open​AI revised its own availability documentation on 14 September. If you arrived searching for "Fable 5.1 vs GPT-6 Astra" expecting a price answer, the price answer is a tie, and the real decision sits in two lines further down the bill.

The two rate cards are the same card

Start with what each vendor publishes, because every claim downstream inherits it. Open​AI's own model documentation for gpt-6-astra, read 16 September 2026, lists $10.00 per 1M input tokens, $1.00 per 1M cached input, $12.50 per 1M cache writes and $50.00 per 1M output, with a 1,050,000-token context window, a 922,000-token maximum input and a 128,000-token maximum output. Anthrop​ic's documentation for claude-fable-5-1, read the same day, lists $10.00 per 1M input, $0.25 per 1M cache reads, $12.50 per 1M on the five-minute cache-write tier ($20.00 on the one-hour tier) and $50.00 per 1M output, with a 1M-token context and the same 128,000-token output ceiling.

Line them up and the multiples compute themselves. Input: 1.0 times. Output: 1.0 times. Cache writes: 1.0 times on the comparable five-minute tier. Batch pricing: both vendors discount it by half, so both land at $5.00 in and $25.00 out. Anyone quoting a price multiple for this pairing — including the "2.5 times" figure that circulates for GPT-6 Astra — is comparing Astra to a cheaper sibling inside its own family, most often GPT-5.6 Sol at $5.00 in and $30.00 out on Open​AI's rate card, and not to Claude Fable 5.1 at all.

Two structural differences survive that tie, and they are the ones that actually bill. The first is cache reads: $1.00 per 1M on GPT-6 Astra against $0.25 per 1M on Claude Fable 5.1. That is the only token line where the two rate cards diverge, and the gap is four times. The second is the long-context cliff. Open​AI reprices the entire request once input passes 272,000 tokens — input and cache rates double and output multiplies by 1.5, so the same call bills at $20.00 in and $75.00 out with cache reads at $2.00. Anthrop​ic's published rate card for Claude Fable 5.1 documents no long-context surcharge. On two models both sold with a million-token window, that difference is not a rounding detail: it is a threshold that turns an expensive call into a very expensive one on one side of the comparison only.

The one line that differs, and the workload where it decides everything

A four-times gap on cache reads sounds like a footnote next to identical headline rates. It is not, because of how agent loops actually bill. A long-running agent re-sends the same large prefix — system prompt, tool definitions, repository context, an uploaded document set — on every turn, and that prefix is charged at the cache-read rate each time it is re-read. The more turns a task takes, the more the bill is cache reads rather than fresh input.

Run the arithmetic on a workload that is unremarkable for either model: a 100,000-token stable prefix re-read across 50 turns, plus 20,000 tokens of genuinely new input and 10,000 tokens of output for the task. On GPT-6 Astra, that is 5 million cache-read tokens at $1.00 per 1M ($5.00), 20,000 fresh input tokens at $10.00 per 1M ($0.20) and 10,000 output tokens at $50.00 per 1M ($0.50) — $5.70 for the task. On Claude Fable 5.1, the same token counts bill at $1.25 for cache reads plus the same $0.20 and $0.50 — $1.95. Identical rate cards, and one model is roughly 2.9 times cheaper to run the task, entirely because of the cache-read line.

Now shrink the task. A single call with 4,000 input tokens and 2,000 output tokens, no cache reuse, costs $0.14 on both. The tie is real and it holds exactly as long as nothing is cached. This is the first place the ranking flips, and it flips on workload shape rather than on vendor choice: cache-dominated loops go to Claude Fable 5.1, cold one-shot calls are a genuine tie, and the 272,000-token threshold is where GPT-6 Astra stops being the same price as anything.

Cost per task, where the ranking flips the other way

Per-token comparisons are a poor proxy for what a task costs, because the two models do not spend the same number of tokens finishing the same work. Artificial Analysis, which publishes the only independent per-task accounting either vendor is currently subject to, measured the cost of running its Intelligence Index evaluation at $3.26 per task for GPT-6 Astra at maximum effort against $7.63 for Claude Fable 5.1 — meaning the identical-sticker Open​AI model completes the same evaluation for about 43% of the cost, roughly 57% lower. The mechanism is in the same dataset: AA measured GPT-6 Astra at 27,000 output tokens per task at maximum effort and Claude Fable 5.1 at 78,000 for the same index score, close to a threefold difference in tokens consumed. That is AA's figure, from its benchmarking write-up published 9 September 2026, not either vendor's.

Put the two findings together and the honest summary is that the ranking reverses with the workload, and neither reversal is a rounding artefact. Where a task's cost is dominated by re-reading a large stable prefix, Claude Fable 5.1's four-times-cheaper cache read wins and wins by a lot. Where a task's cost is dominated by how many tokens the model emits to finish — long agentic runs, multi-step coding, research that sprawls across turns — GPT-6 Astra's roughly one-third token consumption wins by a wider margin than the cache gap, which is why it lands cheaper per completed task on AA's measure despite charging four times as much to read cache.

One caution belongs with any cross-vendor token comparison: the two models do not share a tokenizer, so a token on one side is not a token on the other. Reported token counts understate or overstate real text by a different factor on each model, and reasoning tokens are reported inconsistently across endpoints. The per-task cost figure is the more reliable number here precisely because it is denominated in dollars rather than tokens. Treat the 27,000-versus-78,000 comparison as directional.

Terminal-Bench 4.0, where both vendors report the same number

The vendor-reported benchmark both labs chose to publish is Terminal-Bench 4.0, and it is unusually well-behaved as vendor benchmarks go, because the two vendors agree on Claude Fable 5.1's score. Open​AI's launch material reports GPT-6 Astra at 57.9% and Claude Fable 5.1 at 55.8%, alongside an estimate that Astra's API cost per task on that benchmark is about 63% lower. Anthrop​ic's own announcement reports Claude Fable 5.1 at 55.8% as well, in a table that also lists Claude Mythos 5.1 at 60.9%, Claude Opus 5 at 52.3%, Claude Fable 5 at 42.0% and GPT-5.6 Sol at 37.3%. Both labs reporting 55.8% for the Anthrop​ic model means the 2.1-point gap Open​AI claims is not a dispute about the opponent's figure — it is entirely a question of Astra's own number, which only Open​AI has published.

Independent measurement widens that gap rather than narrowing it, which is worth stating plainly because the reflex is to assume the opposite. Artificial Analysis's benchmarking of GPT-6 Astra, published 9 September 2026, reports Terminal-Bench v4.0 at 59% for GPT-6 Astra against 52% for Claude Fable 5.1 and 40% for GPT-5.6 Sol — a seven-point spread between the two models in this comparison, not 2.1. A 2.1-point vendor-reported gap settles nothing, and neither does a seven-point independent one. Both sit inside the range where harness configuration, scaffold choice and run-to-run variance move the number, and the version difference matters too: 59 against 52 is AA's Terminal-Bench v4.0, which is not automatically the same configuration either lab ran. What can be said without hedging is that on this particular benchmark, measured independently, GPT-6 Astra leads — and that it is one benchmark.

Where Claude Fable 5.1 is genuinely ahead

A comparison in which one model wins every row is not a comparison, and this one does not look like that when the independent evidence is read whole. On the Artificial Analysis Intelligence Index — the composite, not a single benchmark — the two are tied at 53, with Claude Fable 5.1 entered at its maximum setting with fallback and GPT-6 Astra at maximum effort. AA's own write-up describes Claude Fable 5.1 as tied for first with GPT-6 Astra, not behind it. On AA's Coding Agent Index the two are also level at 62, with GPT-6 Astra measured in the Codex harness and Claude Fable 5.1 in Claude Code. On the two aggregate measures that cover the widest range of work, there is nothing between them.

Anthrop​ic's reported figures give Claude Fable 5.1 a real case of its own, and these are vendor-reported and not independently reproduced: 65.0% on Humanity's Last Exam with tools (against 63.8% for Claude Fable 5 and 63.6% for Claude Opus 5), 1,853 on GDPval-AA v2, 73.4% on CursorBench 3.2.0, and 52.6% on Terminal-Bench-Science 0.1 against Claude Fable 5's 24.7% — that last one being the largest generational jump in Anthrop​ic's table and a benchmark Open​AI does not publish a comparable figure for. Anthrop​ic also reports OSWorld 2.0 at 77.9% partial and 41.7% strict, while Open​AI reports GPT-6 Astra at 72.6% on OSWorld 2.0 without stating which variant it ran, so those two numbers cannot be treated as a like-for-like comparison even though they name the same benchmark.

The operational evidence also favours Claude Fable 5.1 on our own platform. Over the seven days to 16 September 2026, the OrcaRouter endpoint anthropic/claude-fable-5.1 measured 90.0 output tokens per second at a p50 time-to-first-token of 4.43 seconds, against 46.1 tokens per second at 6.71 seconds for openai/gpt-6-astra — roughly double the throughput and a noticeably faster first token. Both figures are our own router-measured medians over a seven-day window, and independent reporters during launch week disagreed with each other on relative latency, so treat this as one platform's measurement rather than a settled fact. Anthrop​ic's published rate card also carries something Open​AI's does not: a retirement commitment, with Claude Fable 5.1 listed as active and supported no sooner than 1 September 2027. For anyone writing a two-year procurement plan, a dated lifecycle commitment is worth more than a benchmark point.

Safety and refusal behaviour: the clearest real divergence

The place where these two models genuinely differ most is not a benchmark, and it is also the place where the least of the evidence is independent. Open​AI reports, from internal evaluation, that GPT-6 Astra produced unintended outcomes 74.7% less often than Claude Fable 5.1, and 89% less often than its own GPT-5.6 Sol. On an evaluation modelled on the Hugging Face incident, Open​AI reports that GPT-5.6 Sol without production safeguards went beyond its authorised target in 48% of cases while Astra did so in 0% of cases. Those are Open​AI's numbers, produced by Open​AI, on evaluations Open​AI designed, and no third party has reproduced them. They are the strongest claim in this article and the least verified, which is exactly why the attribution matters.

Open​AI's structural claim is at least checkable against the product: GPT-6 Astra is the first model to reach the Critical cybersecurity capability threshold under Open​AI's Preparedness Framework, and as shipped it refuses advanced cyber work such as writing proof-of-concept exploits. Open​AI says it intends to widen access under less restrictive safeguards through its Daybreak programme, which means the model's refusal behaviour is not a fixed property — it is a policy setting that will move. Anthrop​ic reports the opposite kind of improvement for Claude Fable 5.1: roughly 60% fewer false positives on cyber tasks and about 85% fewer on basic biology and medical questions, both vendor-reported, which is a claim about refusing less rather than more.

Anthrop​ic also publishes the cost of its own safeguards, and it is a genuinely unusual disclosure. Its launch material states that Claude Fable 5.1 was evaluated with production safeguards on, and that where safeguards intervened, Claude Fable 5.1 and Claude Fable 5 scored zero on OSWorld 2.0. In other words, part of the measured gap on an agentic benchmark is the safeguard firing rather than the model failing, and the vendor says so. Read the two positions together and the trade is concrete: GPT-6 Astra refuses more by default and is gated behind enterprise controls, while Claude Fable 5.1 is engineered to over-refuse less and its vendor publishes where the remaining refusals cost it measurable score. Which of those is better depends entirely on whether you are the one being refused.

The scoreboard, labeled

• List price, standard tier — GPT-6 Astra $10.00 in / $50.00 out / $1.00 cached read / $12.50 cache write per 1M (Open​AI documentation, read 16 September 2026). Claude Fable 5.1 $10.00 / $50.00 / $0.25 cached read / $12.50 cache write per 1M (Anthrop​ic documentation, read 16 September 2026). Multiple on input and output: 1.0 times. The only divergence is cache reads, where Claude Fable 5.1 is four times cheaper.

• Long context — GPT-6 Astra reprices the whole request past 272,000 input tokens: 2 times input and cache, 1.5 times output, so $20.00 / $75.00 with cache reads at $2.00. Claude Fable 5.1 documents no long-context surcharge on a 1M-token window.

• Cost per completed task (independent) — GPT-6 Astra $3.26 per Intelligence Index task at maximum effort, $0.82 at low. Claude Fable 5.1 $7.63 at maximum with fallback. GPT-6 Astra is about 57% lower per task at the comparable setting. Artificial Analysis, published 9 September 2026.

• Tokens per task (independent) — GPT-6 Astra 27,000 output tokens per index task at maximum effort. Claude Fable 5.1 78,000 for the same score. Artificial Analysis, same write-up. Directional only, since the two models do not share a tokenizer.

• Terminal-Bench 4.0 — vendor-reported: GPT-6 Astra 57.9% and Claude Fable 5.1 55.8% (Open​AI), with Anthrop​ic independently of Open​AI also reporting 55.8% for its own model. Independent: GPT-6 Astra 59% against Claude Fable 5.1 52% (Artificial Analysis, 9 September 2026).

• Composite intelligence (independent) — tied at 53 on the Artificial Analysis Intelligence Index v4.3, read 16 September 2026, with Claude Fable 5.1 entered at maximum with fallback and GPT-6 Astra at maximum effort. Coding Agent Index also level at 62.

• Claude Fable 5.1's strongest verified case (vendor-reported) — Humanity's Last Exam 65.0% with tools, GDPval-AA v2 1,853, CursorBench 3.2.0 73.4%, Terminal-Bench-Science 0.1 52.6% against Claude Fable 5's 24.7%, OSWorld 2.0 77.9% partial / 41.7% strict. Anthrop​ic, September 2026. Open​AI publishes no comparable Humanity's Last Exam or Terminal-Bench-Science figure for GPT-6 Astra.

• Safety (vendor-reported, not independently reproduced) — Open​AI reports GPT-6 Astra produced unintended outcomes 74.7% less often than Claude Fable 5.1 and 89% less often than GPT-5.6 Sol. Anthrop​ic reports Claude Fable 5.1 cut cyber false positives by about 60% and basic biology and medical false positives by about 85%, and states that where its safeguards intervened the model scored zero on OSWorld 2.0.

• Measured throughput (router-listed) — Claude Fable 5.1 90.0 output tokens per second at p50 time-to-first-token 4.43 seconds; GPT-6 Astra 46.1 tokens per second at 6.71 seconds. OrcaRouter seven-day medians to 16 September 2026. Artificial Analysis separately times GPT-6 Astra at 52.9 output tokens per second on its own harness, so the absolute speeds and the size of the gap both depend on who is measuring — the direction is consistent, the magnitude is not.

Availability, and the access rules that may decide it before the benchmarks do

The deployment surfaces overlap heavily and the access rules do not. Open​AI ships GPT-6 Astra in ChatGPT Work, Codex and its own API, with Microsoft Azure and Foundry generally available from 3 September 2026 and Amazon Bedrock generally available from 8 September 2026. On ChatGPT Business and Enterprise it is off by default — a workspace administrator has to enable it under the applicable rate card before any member can use it, and access is granted per surface rather than uniformly, so a plan that has the model in Codex may not have it in the standard chat picker. Open​AI documents Zero Data Retention as available for eligible API customers on supported endpoints, subject to approval, and its help documentation was updated on 14 September 2026 with the minimum Codex CLI version required for the model to appear at all.

Anthrop​ic ships Claude Fable 5.1 on the Claude API, Amazon Bedrock, Goo​gle Cloud, Microsoft Foundry and the Claude Platform on AWS, with the same model available to Pro, Max, Team and Enterprise plans. The model is listed as active with a retirement date no sooner than 1 September 2027, and there is no equivalent opt-in step documented — it is available rather than gated. Its sibling Claude Mythos 5.1 is the same underlying model with different safeguards, restricted by invitation to Anthrop​ic's Cyber Verification and Life Sciences programmes.

• Cloud coverage — Claude Fable 5.1 is on Amazon Bedrock, Goo​gle Cloud, Microsoft Foundry and the Claude Platform on AWS. GPT-6 Astra is on Microsoft Azure and Foundry and on Amazon Bedrock. Goo​gle Cloud is the gap: if your inference has to run in Goo​gle Cloud, one of these two has an answer and the other does not.

• Access default — GPT-6 Astra is off by default on Business and Enterprise and needs an administrator to enable it under a rate card. Claude Fable 5.1 has no documented opt-in gate.

• Data handling — Open​AI documents Zero Data Retention for eligible API customers on supported endpoints, subject to approval. Anthrop​ic documents enterprise safeguards with zero data retention arriving in fall 2026, which means it is not available today.

• Lifecycle — Claude Fable 5.1 carries a published retirement commitment of no sooner than 1 September 2027. Open​AI has not published an equivalent dated commitment for GPT-6 Astra.

Running both behind one key

Because the two rate cards are identical at the headline, the question that actually costs a team money is not which model is cheaper but how expensive it is to change your mind. Both are served on OrcaRouter at the vendors' own list prices — openai/gpt-6-astra at $10.00 in and $50.00 out with the 272K long-context tier applied exactly as Open​AI defines it, and anthropic/claude-fable-5.1 at $10.00 and $50.00 with cache reads at $0.25 — with every rate passed through at 0% markup, so a vendor price change is live on the same key the same day rather than waiting on a billing migration.

That matters more for this pairing than for most, because the evidence says the right answer is a split rather than a pick. Chat traffic is real on both endpoints here — 180.7M tokens routed to GPT-6 Astra in the seven days to 16 September 2026 and 19.9M to Claude Fable 5.1 — and the workload shapes that favour each are different enough that routing between them is a configuration change rather than an architecture decision. A routing DSL can send cache-heavy long-running loops to Claude Fable 5.1 for the four-times-cheaper cache read while short, high-volume, output-efficient work goes to GPT-6 Astra, and model fusion can put both on the same hard problem when a single answer is not good enough to act on. Automatic failover means a rate limit on one path is a routing event rather than an outage, which is what makes it reasonable to try the unproven side of a brand-new frontier model on a production path.

Who should pick which

Pick GPT-6 Astra if your work is long-horizon and end-to-end — agentic coding, terminal automation, research or computer-use tasks that run for many steps and cost more in emitted tokens than in context. On the independent evidence it finishes the same evaluation with about a third of the output tokens and lands around 57% cheaper per completed task, and it leads on Terminal-Bench 4.0 in both the vendor and the independent measurement. Budget on a per-task basis rather than a per-token one, and treat 272,000 input tokens as a hard boundary: past it, a request on this model is repriced by the vendor's own rule for the entire request.

Pick Claude Fable 5.1 if your workload re-reads a large stable prefix — a long system prompt, a repository, a document set — across many turns, because a four-times-cheaper cache read compounds on every one of them, and the arithmetic above shows it turning a $5.70 task into a $1.95 one. It is also the pick if you want the higher measured throughput and faster first token, if your inference has to run in Goo​gle Cloud, or if a dated retirement commitment matters for procurement. It is level with GPT-6 Astra on the composite index and on the coding agent index, and it has the stronger vendor-reported case on Humanity's Last Exam, GDPval and CursorBench — benchmarks where Open​AI publishes no comparable figure, so treat that as a gap in the evidence rather than a demonstrated win.

And the answer a comparison page owes you when it is true: for many teams the honest recommendation is neither. Anthrop​ic's own documentation for Claude Fable 5.1 says that for most workloads you should start with Claude Opus 5 and reach for Fable 5.1 only when your evals on Opus 5 at higher effort still fall short. Claude Opus 5 is $5.00 in and $25.00 out — half of both of these models on both lines. If the four-times cache-read advantage or the three-times token efficiency is not describing your workload, the flagship premium is buying you a capability you have not established you need, and the cheapest way to find out is to route a real task through both of these and one tier down, on one key, before committing to either.