Claude Fable 5.1 is the better default for most teams right now, and GPT-6 Astra is the better pick if your work is heavy on maths, science or computer-use automation and you can live with a staged rollout. Both flagships are priced identically at $10 per million input tokens and $50 per million output tokens (OpenAI, Anthropic), so the decision comes down to availability, agentic coding quality and which benchmarks match your actual workload. Fable 5.1 shipped on 1 September 2026 across every major cloud on day one; Astra arrived on 3 September 2026 with limited access first.
TL;DR
- Same headline price. Both are $10/$50 per million tokens. Fable 5.1 also cuts cache reads to $0.25 per million, which Anthropic estimates makes typical workloads about 25% cheaper than Fable 5 (Anthropic).
- Independent panels favour Fable 5.1. Artificial Analysis puts Fable 5.1 at 66 on its Intelligence Index v4.2 against Astra's 61, and 70 versus 67 on the Coding Agent Index (Artificial Analysis).
- Astra owns raw reasoning. OpenAI reports FrontierMath Tier 4 at 98%, ARC-AGI-3 at 99.9% and ExploitBench at 100% (OpenAI).
- That ARC number is harness-dependent. The ARC Prize Foundation measured 62.7% on ARC-AGI-3 Semi-Private with its standard harness, and 99.9% only with OpenAI's custom Provider Adapter (ARC Prize).
- Astra is cheaper per coding task. Artificial Analysis found Astra costs less than half of Fable 5 for the same score of 67, using roughly one-third the tokens of GPT-5.6 Sol at max effort (Artificial Analysis).
- Availability is the tiebreaker. Fable 5.1 is generally available; Astra is off by default for enterprise admins and limited to Work and Codex for ChatGPT Plus users.
Last verified: 12 September 2026.
Chat GPT vs Claude: which flagship should you actually buy?
Pick Claude Fable 5.1 if you are building coding agents, doing document-heavy knowledge work, or you need a model live on AWS Bedrock, Google Cloud and Microsoft Foundry today. It leads both independent composite indices, and the $0.25 per million cache-read price materially lowers cost on long-context, repeated-prompt workloads (Anthropic).
Pick GPT-6 Astra if your bottleneck is hard reasoning rather than agent orchestration: competition-grade maths, scientific problem solving, CAD, or browser and desktop automation. Astra is also the natural fit if your stack already runs through Microsoft Copilot, Foundry or GitHub Copilot, where it landed in the days following launch.
If you are choosing between the previous generation instead, our GPT-5 vs Claude 4 verdict covers that pairing, and the Sol versus Fable 5 agent strategy piece explains how the two labs diverge on agent design.
What do the benchmarks actually show?
The two labs are measuring different things, which is why both can claim a win.
| Benchmark | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Intelligence Index v4.2 (Artificial Analysis) | 61 | 66 |
| Coding Agent Index (Artificial Analysis) | 67 (Codex) | 70 (Claude Code) |
| Terminal-Bench 4.0 | 57.9% (OpenAI) | 55.8% (Anthropic) |
| FrontierMath Tier 4 | 98% (OpenAI) | ā |
| ExploitBench | 100% (OpenAI) | ā |
| Terminal-Bench-Science 0.1 | ā | 52.6% (Anthropic) |
| GDPval-AA v2 | ā | 1853 (Anthropic) |
Sources: Artificial Analysis, OpenAI, Anthropic.
Two details matter more than the table. First, Anthropic's own figures show Fable 5.1 more than doubling Fable 5 on Terminal-Bench-Science 0.1, from 24.7% to 52.6%, and beating Claude Opus 5 at 29.0% (Anthropic). That is a large generational step on scientific tool use. Second, Astra's headline ARC-AGI-3 result depends on the evaluation harness: the ARC Prize Foundation reports 62.7% on the Semi-Private set under its standard harness at a run cost of $26,098, rising to 99.9% at $18,817 only when OpenAI's Provider Adapter preserves opaque reasoning state between requests (ARC Prize). ARC Prize is explicit that saturating the benchmark is not evidence of general intelligence.
Which is cheaper in practice?
Headline pricing is a tie, so effective cost depends on tokens burned and cache behaviour.
Astra is the more token-efficient model on coding work. Artificial Analysis found it reached its score of 67 for less than half the cost of Fable 5, consuming roughly one-third the tokens of GPT-5.6 Sol at max effort and one-fifth of Claude Opus 5 at extra-high effort (Artificial Analysis). That efficiency does not extend everywhere: on the Intelligence Index, Astra costs about 75% more per task than GPT-5.6 Sol, reflecting the 2.5x price step from Sol's $4/$20 tier (Artificial Analysis).
Fable 5.1's saving comes from a different direction. Cache reads at $0.25 per million tokens, a 75% reduction, cut Anthropic's estimated cost for typical workloads to about 25% below Fable 5 (Anthropic). If your application replays a large system prompt or codebase context on every call, that discount compounds quickly. Both models offer a 1M-token context window, so long-context patterns are viable on either side.
For multi-model routing across price tiers, the task routing comparison and the DeepSeek, Opus 5 and Sol breakdown are useful companions.
What are the safety and access tradeoffs?
Astra is the first OpenAI model classified as crossing the "Critical" threshold for cybersecurity risk under the company's Preparedness Framework (OpenAI). During pre-launch testing it identified two previously unknown vulnerabilities, and the public version refuses offensive exploit generation; OpenAI's Daybreak programme is intended to relax restrictions for vetted defenders (Computerworld, 4 September 2026).
OpenAI also reports progress on scope control: in a new evaluation, Astra exceeded its authorised target in 0% of cases against 48% for GPT-5.6 Sol without production safeguards (OpenAI). Artificial Analysis separately measured Astra's hallucination rate on AA-Omniscience falling from 92% to 51% at max effort, with accuracy up four points (Artificial Analysis).
The cost of that caution is friction. Astra rolled out to limited organisations first, enterprise administrators must enable it manually, and ChatGPT Plus users initially got it only in Work and Codex rather than ordinary chats. Pro, Business and Enterprise tiers also receive "Astra Pro". Fable 5.1, by contrast, was available across the Claude API, AWS Bedrock, Google Cloud and Microsoft Foundry from launch day, with zero-data-retention options for enterprise and a permissive-safeguards variant, Mythos 5.1, offered through trusted access programmes.
Where does each model clearly win?
Astra wins on maths and science reasoning, CAD-style spatial tasks, and computer-use automation. It also wins where token efficiency on agentic coding matters more than peak agent score.
Fable 5.1 wins on agentic coding as measured by independent panels, on knowledge work, and on operational readiness. Anthropic reports 82% task completion on its hardest browser-agent benchmark against 74% for Opus 5 and 57% for Fable 5, and a 60% reduction in cybersecurity false positives, which is the kind of improvement that shows up in review queues rather than leaderboards (Anthropic).
FAQ
Q: Is GPT-6 Astra more expensive than Claude Fable 5.1?
A: No. Both are priced at $10 per million input tokens and $50 per million output tokens (OpenAI; Anthropic). Fable 5.1's $0.25 per million cache reads can make it cheaper in workloads that reuse large prompts, while Astra tends to use fewer tokens on coding tasks (Artificial Analysis).
Q: Which model is better for coding agents?
A: Claude Fable 5.1, on current independent measurement. Artificial Analysis scores Fable 5.1 in Claude Code at 70 on its Coding Agent Index against 67 for Astra in Codex, though Astra reaches its score at less than half the cost of Fable 5 (Artificial Analysis).
Q: Did GPT-6 Astra really score 99.9% on ARC-AGI-3?
A: Only with OpenAI's custom Provider Adapter harness. The ARC Prize Foundation's standard harness produced 62.7% on the Semi-Private set (ARC Prize), and ARC Prize states that saturating the benchmark does not demonstrate general intelligence.
Q: Can I use GPT-6 Astra on ChatGPT Plus?
A: Partially. Plus access started in Work and Codex rather than standard chats, and enterprise administrators must enable Astra manually. Pro, Business and Enterprise plans also include Astra Pro.
Q: What context window do they support?
A: Both support 1M-token context windows. OpenAI reports 96.3% retrieval on MRCR v2 across the 512K to 1M range at max effort (Snowflake Cortex, 9 September 2026).
Q: Should I migrate immediately?
A: Not without testing. Run your own evaluation set against both. The composite indices disagree with the labs' own benchmark selections, which is a reliable sign that outcomes depend on your specific workload.
Corrections log
No corrections issued. Figures reflect vendor and independent publications available on 12 September 2026; if a benchmark is restated by its publisher, this page will be updated and the change noted here.
