GPT-6 Astra vs Claude Fable 5.1: Same Price, Very Different Bill

Quick answer: GPT-6 Astra and Claude Fable 5.1 list at exactly the same price — $10 per 1M input, $50 per 1M output. They are not the same cost. Astra charges $1.00 per 1M cached input tokens; Fable 5.1 charges $0.25 — a 4x gap on the line item that dominates agentic bills. Astra then adds a surcharge Fable 5.1 does not have: above 272,000 input tokens, the entire request is billed at 2x input and 1.5x output. On capability, both vendors' own tables agree that Astra leads on terminal, agentic coding, and computer use, while Fable 5.1 leads on Humanity's Last Exam with tools and on the Artificial Analysis Intelligence Index — a lead OpenAI published in its own appendix.

Released two days apart (Fable 5.1 on September 1, Astra on September 3, 2026), these two models are now the default frontier choice for most teams. This is the comparison that reads both vendors' tables instead of one. For the single-model background, see what GPT-6 Astra is and what it does not win and what changed in Claude Fable 5.1.

Credibility note: every number here comes from OpenAI's launch announcement and API model documentation, Anthropic's launch announcement and Claude Platform documentation, and Artificial Analysis's live leaderboard — all read September 6, 2026. We have not benchmarked either model ourselves. Where the two vendors report the same figure for the same model, we say so, because mutual corroboration is the strongest verification available five days into a launch. This comparison was assembled with AI assistance and every cell in every table was verified against the vendor page it is attributed to before publication.

What This Article Solves

The pain point is two marketing departments, zero overlap. OpenAI's page compares Astra against Fable 5.1 on benchmarks OpenAI chose. Anthropic's page compares Fable 5.1 against GPT-5.6 Sol — a model Astra replaced — and does not mention Astra at all, because Astra did not exist when it was published. Read either page alone and you get a clean win. Read both and the picture is genuinely split.

And underneath the benchmark noise sits a cost question that identical list pricing actively hides: at $10/$50 each, which one is cheaper depends entirely on the shape of your workload, and the answer can differ by more than 2x in either direction.

Head to Head: Specifications

Specification-wise these are near twins: same output ceiling, same effort ladder, same modalities, context windows within 5% of each other. Fable 5.1's knowledge cutoff is two months more recent. That is the entire spec-sheet difference.

Head to Head: The Real Price Card

This is where they separate.

Three consequences that matter more than the headline parity:

1. Long agent loops favour Fable 5.1 on paper. Every turn of an agentic session re-reads the same system prompt, tool definitions, and accumulated history. After a few dozen turns, cache reads dominate the bill. At $0.25 versus $1.00, Fable 5.1 charges a quarter as much for the dominant line item. Anthropic cut this rate by 75% on September 1 specifically to make Fable-class models economical for exactly this workload — Cognition's Walden Yan is quoted in Anthropic's announcement saying the new cache-read pricing is what finally made a Fable-class model viable for work they had kept on Opus.

2. Long single requests punish Astra hard. The 272K rule is not a marginal rate on the tokens above the line; it reprices the whole request. Send 300,000 input tokens to Astra and you pay $20 per 1M on all of them, plus 1.5x on output. Send the same 300,000 tokens to Fable 5.1 and you pay $10 per 1M. If you routinely feed large codebases or document sets in one shot, that single rule can double your input cost on Astra while Fable 5.1 stays flat.

3. Astra has two cheaper gears Fable 5.1 does not. Flex processing at 50% of Standard, on top of Batch at 50%. For latency-tolerant bulk work, that is a real structural advantage — and Fast mode gives Astra a paid speed lever at the other end that Anthropic does not offer at all.

The blunt version: if your workload is a long-running agent with a stable cached prefix, Fable 5.1 is structurally cheaper. If it is bulk latency-tolerant processing, Astra is structurally cheaper. If it is huge single-shot requests, Astra gets much more expensive and Fable 5.1 does not. The Fable 5.1 pricing breakdown runs the cache arithmetic out over a 40-turn and an 80-turn session if you want to see where the crossover lands.

Head to Head: Benchmarks, From Both Tables

Below, every row is labelled with which vendor published it. Where both published a figure for the same model on the same benchmark, that is noted — it is the closest thing to independent verification available right now.

Where GPT-6 Astra leads

The corroboration pattern here is worth pausing on. On three of these benchmarks — Terminal-Bench 4.0, Terminal-Bench Science 0.1, and AutomationBench — OpenAI's reported score for Fable 5.1 is identical to Anthropic's own reported score. Two competitors independently arriving at the same number for the same model means those rows are about as trustworthy as launch-week data gets, and it makes Astra's margins on them credible rather than cherry-picked.

Terminal-Bench Science carries one caveat Anthropic supplies and OpenAI does not: the standard error is ±3.5–4.5 points per model. Astra's 12-point lead survives that comfortably. Its 2.1-point Terminal-Bench 4.0 lead is thinner ground.

Where Claude Fable 5.1 leads

Both of the first two rows come from OpenAI's own launch announcement. A vendor publishing two losses to its closest competitor in its own appendix is a meaningful signal — and it means the "Fable 5.1 wins on reasoning" claim does not rest on Anthropic's marketing at all.

The third row is the independent confirmation. Artificial Analysis's live board currently ranks Claude Fable 5.1 (max with fallback) first at 57, GPT-6 Astra (max) second at 55. That is a different index version from the 65.7/61.2 pair OpenAI cited, so the absolute numbers are not comparable across rows — but the ordering is the same in the version OpenAI chose and in the current live board. When a vendor's own citation and a third-party tracker agree, treat it as settled.

The number that reverses the story

Artificial Analysis publishes cost per Intelligence Index task alongside the score:

Fable 5.1 scores two points higher and costs 2.4x more per task. Same list price — very different token consumption. This is the single most decision-relevant pair of numbers in the whole comparison, and it is on neither vendor's launch page.

It also cuts against the naive reading of the pricing section above. Yes, Fable 5.1's cache reads are 4x cheaper. But Fable 5.1 at max effort emits far more tokens per task, and output at $50 per 1M swamps a cache-read discount. Which effect wins is a property of your workload, not of the price card — which is why the effort setting matters as much as the model choice.

Computer Use and Agentic Behaviour

Astra's clearest differentiation is computer use, and OpenAI leaned the entire launch on it: 59.3% on Agents' Last Exam (versus 55.5% for Claude Opus 5), 92.7% on ScreenSpot-Pro, 72.6% on OSWorld 2.0's offline set. The efficiency claims underneath are as important as the scores — roughly 65% fewer output tokens than Opus 5 at top settings on Agents' Last Exam, and a higher OSWorld score in about 47% less time per task than GPT-5.6 Sol.

Direct comparison is not clean here. Anthropic reports OSWorld 2.0 at 77.9% partial / 41.7% strict for Fable 5.1, but explicitly notes those scores are on the benchmark authors' August 2026 task release and are not comparable to previously published OSWorld 2.0 results. OpenAI's 72.6% is on a specified offline set with a partial score. Two different task sets, two different scoring modes — anyone presenting 72.6% vs 77.9% as a head-to-head is comparing numbers that were never designed to be compared. We are not going to do that.

What can be said honestly: OpenAI built its launch around computer use with a broad set of benchmarks and concrete demonstrations (KiCad PCB routing, Blender-to-Unreal pipelines, Form 1040 completion). Anthropic built its launch around long-horizon research and coding stamina, with partner reports of unattended runs measured in days — Ramp describes a 38-hour unattended machine-learning run that diagnosed a label artifact, corrected it, launched six parallel experiments, and returned with next steps.

Those are different bets. Astra optimises for operating software fast and cheaply; Fable 5.1 optimises for staying coherent over very long autonomous horizons.

Rather than adjudicate that from press releases: put both models side by side on glm5.app — GPT-6 Astra and Claude Fable 5.1 are in the same picker on one credit balance — and run the task you actually care about through each.

Security Posture: A Real Difference

If your work touches security, this may decide the comparison outright.

GPT-6 Astra is the first OpenAI model to meet the Critical cybersecurity threshold under the Preparedness Framework. Unrestricted, OpenAI reports it at 100% on ExploitBench and says it found two previously unknown zero-days during internal evaluation. As shipped it will do secure code review and patching, and will refuse to write proof-of-concept exploits. Looser access is promised through OpenAI Daybreak "in the coming weeks." One operational detail: when OpenAI's misalignment monitoring intervenes, ChatGPT and Codex ask you to review, but the API stops the task.

Claude Fable 5.1 moved in the opposite direction on false positives. It can now identify software vulnerabilities, which Fable 5 refused, and Anthropic reports roughly 60% fewer safeguard interventions per session in Claude Code. Penetration testing, exploit generation, and binary-based vulnerability scanning are still redirected to the Opus models. For vetted organizations, Claude Mythos 5.1 — the same model with permissive safeguards — is available through the Cyber Verification Program, currently limited to a set of US organizations.

Net: for defensive vulnerability discovery today, Fable 5.1 has the more permissive shipping posture, plus a documented escalation path (Mythos) for vetted teams. Astra is more capable at the ceiling but more restricted at the surface, with its escalation path still in the future.

Alignment and Working Style

OpenAI's alignment results for Astra are strong and specific: on an internal computer-use safety benchmark it scores 2.4% misaligned outcomes against Fable 5.1's 9.5% (lower is better), 0% on an internal circumvention benchmark, and — on an evaluation built around a difficult-or-impossible task — it went beyond the authorized target in 0% of cases against GPT-5.6 Sol's 48%.

One counterweight OpenAI publishes about its own model: evaluations found Astra's written reasoning harder to monitor than Sol's when explicitly asked to evade monitoring. OpenAI attributes this to Astra solving problems in fewer written steps and says it takes the decline seriously.

Community experience adds a texture no benchmark captures. The most-discussed Astra thread on r/OpenAI in launch week, "Astra (GPT-6) High Intelligence, Low Intuition", describes a developer hitting repeated friction: Astra proposing new infrastructure for a problem the existing architecture already solved, and starting implementation before the approach was agreed. Anthropic's partner quotes push the opposite way — Red Hat and Jane Street both single out Fable 5.1's communication as clearer and easier to follow over long tasks.

Treat all of that as pain-point and preference signal, not measurement: one is uncontrolled week-one anecdote, the other is vendor-selected partner testimony. Neither is evidence. Both point at the same axis — these models have genuinely different working personalities, and the one that suits your team may not be the one that tops a leaderboard.

Which Should You Choose?

Choose GPT-6 Astra when:

• Your work is computer use, browser automation, or GUI-driven tasks — its clearest and best-evidenced lead
• You need frontier math or graduate-level science reasoning (FrontierMath Tier 4 at 97.6% is not close)
• Cost per unit of capability matters more than the top of the leaderboard — 2.4x cheaper per task on independent measurement
• You run latency-tolerant bulk work and can use Flex plus Batch stacking
• Terminal-heavy engineering and scientific workflows are your bread and butter

Choose Claude Fable 5.1 when:

• You want the current top of the independent intelligence leaderboard
• Your agents run long sessions with a stable cached prefix, where a 4x cheaper cache read compounds every turn
• You routinely send single requests above 272K input tokens, where Astra's surcharge bites and Fable 5.1's rate does not move
• You need defensive vulnerability discovery in the shipping model today, or Mythos-level access through a trusted-access program
• Long unattended autonomy is the requirement, and partner reports of multi-day coherent runs describe your use case
• A more recent knowledge cutoff matters (June 2026 vs April 30, 2026)

Choose neither when: your evals already pass on something cheaper. Anthropic's own documentation tells you to start with Claude Opus 5 at $5/$25 and escalate to Fable 5.1 only when Opus at higher effort falls short. Both of these models are $50-per-1M-output tools, and a large share of production traffic does not need that.

Frequently Asked Questions

Is GPT-6 Astra better than Claude Fable 5.1? On terminal work, agentic coding, computer use, math, and science — yes, on both vendors' published numbers. On Humanity's Last Exam with tools and on the Artificial Analysis Intelligence Index — no, and that is OpenAI's own reported result, corroborated by the current live leaderboard. There is no single winner; there is a workload-dependent answer.

Do GPT-6 Astra and Claude Fable 5.1 cost the same? The list rates are identical at $10/$50 per 1M tokens. The effective cost is not. Astra charges 4x more for cached input ($1.00 vs $0.25) and adds a 2x input / 1.5x output surcharge on requests above 272K input tokens. Fable 5.1 emits more tokens per task at max effort — Artificial Analysis measures $6.12 per Intelligence Index task versus Astra's $2.57.

Which has the bigger context window? Astra, marginally: 1,050,000 tokens versus 1,000,000. Both cap output at 128,000 tokens. In practice Astra's larger window is offset by its 272K pricing cliff, which Fable 5.1 does not have.

Which is better for coding? Astra leads on Terminal-Bench 4.0 (57.9% vs 55.8%), Terminal-Bench Science (64.6% vs 52.6%), and DeepSWE v1.1 (74.1% vs 67.4%). But on the Artificial Analysis Coding Agent Index v1.4 that OpenAI itself cites, Astra scores 67.0 against Claude Opus 5's 68.1 — meaning neither of these two tops that particular board. For long unattended coding runs, Anthropic's partner reports are the stronger evidence; for short terminal tasks, Astra's benchmarks are.

Which is safer to use for security work? Fable 5.1 has the more permissive shipping posture for defensive work right now — it can identify vulnerabilities, with roughly 60% fewer safeguard interruptions than Fable 5 — while still routing pentesting and exploit generation to Opus models. Astra is more capable at the ceiling (100% on ExploitBench unrestricted) but refuses proof-of-concept exploit creation as shipped, with looser access promised later via OpenAI Daybreak.

Can I use both? Yes, and for a genuine evaluation you should. Both are available through their own vendors' APIs, plus Azure and Bedrock for Astra and Bedrock, Google Cloud, and Microsoft Foundry for Fable 5.1. Multi-model platforms carry both behind one account — including glm5.app, where both sit in the same model picker on a shared credit balance, which is the fastest way to diff them on your own prompts.

Sources

• GPT-6 Astra: A new generation of intelligence — OpenAI — Astra's launch announcement, September 3, 2026, including the full comparison tables that report Claude Fable 5.1's scores.
• GPT-6 Astra model reference — OpenAI API docs — Astra's context window, cutoff, complete rate card, the 272K threshold rule, Flex and Fast mode pricing, and effort levels.
• Introducing Claude Fable 5.1 and Claude Mythos 5.1 — Anthropic — Fable 5.1's launch announcement, September 1, 2026: benchmark table with standard-error and safeguard caveats, cache-read price change, safeguard posture, and partner reports.
• Claude Fable 5.1 — Claude Platform documentation — Fable 5.1's complete rate card including both cache-write tiers, specifications, and breaking changes.
• Artificial Analysis — model comparison — the independent Intelligence Index, output speed, and cost-per-task figures used to break the vendor tie, checked September 6, 2026.

Community discussion is cited only as a signal about working style, never as a source for specifications or scores. All figures verified September 6, 2026; both models are less than a week old and pricing, availability, and safeguard behaviour are all still moving.