1 · TL;DR: The Short Version

Two frontier flagships shipped 48 hours apart in September 2026 — Claude Fable 5.1 (Anthropic, Sep 1) and GPT-6 Astra (OpenAI, Sep 3). Same list price ($10/$50 per 1M tokens), same 1M-token context, same 128K max output. The comparison is a genuine split decision, and the two sides disagree with each other about who wins.

2 · The Two Models at a Glance

Sources: DataCamp, Anthropic, Vellum.

3 · The Vendor Tables: Each Side Wins Its Own

The cleanest way to see the disagreement: OpenAI's table shows Astra ahead on nearly every row it published; Anthropic's table shows Fable 5.1 ahead on every row it published. Both are real numbers — they just measure different things, on different task releases, with different harnesses.

OpenAI's comparison (Astra vs Fable 5.1)

*ScreenSpot-Pro figure is for Claude Fable 5 (from Mythos), not 5.1. Source: OpenAI's GPT-6 Astra announcement table, via DataCamp and Vellum. ↓ = lower is better.

Anthropic's comparison (Fable 5.1 vs the field)

Source: Anthropic announcement, via Vellum. Standard error ±3.5–4.5 pts per model on Terminal-Bench-Science. Fable 5.1 ran with production safeguards enabled; where safeguards intervened it scored zero on OSWorld, likely understating raw capability.

4 · The Independent Arbitrator: Artificial Analysis

When both vendors claim victory, the tiebreaker is Artificial Analysis, which runs both models itself. Its verdict, on the revised index (v4.3, September 7, 2026): the two are level on intelligence — 53 points apiece — and Astra gets there at roughly 40% of the per-task cost. AA has now reworked the index twice since launch (v4.1: Fable 5.1 66–61; v4.2: 57–55; v4.3: 53–53), so every AA number below is date-stamped.

*Both AA composites are now dead heats. Coding Agent Index v1.4: Astra 62 in Codex, Fable 5.1 62 in Claude Code — though Astra reaches that score ~40% cheaper per task ($7.09 at max effort). The harnesses still differ, and AA ran Fable 5.1 with Anthropic's default server-side fallback, which served ~4% of output tokens via Opus 4.8/5 on safety-flagged requests — the score is Fable 5.1 "as most people will actually call it." Earlier drafts of this table used Index v4.1, where Fable 5.1 led 66–61 (v4.2 narrowed it to 57–55); v4.3, published September 7, 2026, closed it to 53–53.

Update (Sep 7–9, 2026): Artificial Analysis' revised suite changed that verdict. On Intelligence Index v4.3 both models score 53 — Astra ties Fable 5.1 instead of trailing it — and on Coding Agent Index v1.4 both score 62, with Astra at ~40% (Intelligence) and ~60% (Coding Agent) of Fable 5.1's cost per task.

5 · Coding: A Tie That Costs Very Differently

This is the most contested dimension. On the benchmarks each vendor publishes, Astra leads by a nose; on the independent index the two are now level — 62 apiece on AA's Coding Agent Index v1.4, with Astra getting there ~40% cheaper per task; on cost, Astra wins outright.

The efficiency story is the part that survives scrutiny. Artificial Analysis found Astra matches Fable 5-class results "at less than half the cost, driven by significant token efficiency gains." In the "No Hype Assessment" YouTube comparison, Terminal-Bench 4.0 at max effort came out 56.7% (Astra) vs 55.8% (Fable) — but cost $10.35 vs $19.50. A Reddit agent-work comparison (r/better_claw) measured ~$1.10 per task for Astra vs ~$0.65 for Fable, noting Astra is verbose — "the thinking tax is real" — but OpenAI doesn't charge separately for reasoning tokens.

6 · Reasoning & Science: The Cleanest Split

This is where the two models divide most cleanly — and it goes both ways.

Astra owns graduate-level math and physical science. It saturates FrontierMath Tier 4 (97.6% vs 87.8%), and its launch included Lean-verified proofs of new results on prime gaps. But the caveat from MindStudio is worth keeping: on a harder Epoch AI set of 68 unsolved Erdős problems, Astra solved only 2 in its official run (5 with repeated attempts costing $220,000+ in compute). Saturating one benchmark isn't solving math.

Fable 5.1 owns broad, hard reasoning. It takes Humanity's Last Exam with tools 65.0% to 57.2% — an eight-point gap, "not close" per DataCamp — and the AA evaluations with a knowledge or science flavour still break its way — Fable 5.1 leads the individual evals AA-Briefcase and SciCode, while Astra leads the two newest ones (Terminal-Bench v4.0, 59.1% vs 52.0%, and AutomationBench-AA, completing 41.6% of workflows without a guardrail violation vs 32.1%). On the composite the two are now level: Intelligence Index v4.3 scores both 53. Anthropic's own science story is qualitative: Mythos 5.1 designed protein binders with a ~50% hit rate (vs a 10–15% norm), trained a neural network that produced a 2–3 km resolution elevation map of a third of Venus, and wrote GPU kernels that sped up seven genomics models up to 2.5×.

7 · Computer Use & Professional Artifacts: Astra's Home Turf

If your agents need to click through real software, produce slides, or generate CAD output, this is Astra's dimension — mostly because Anthropic hasn't published comparable numbers.

Astra also completes OSWorld tasks ~47% faster than GPT-5.6 Sol, and its launch demos operated KiCad, Unity, FreeCAD and Blender directly. Anthropic's OSWorld 2.0 numbers (77.9% partial / 41.7% strict) come from the benchmark authors' August 2026 task release and are explicitly not comparable to OpenAI's offline-set 72.6% — Anthropic itself shows no competitor score on that row. For agents that operate real software, DataCamp's verdict: Astra is the stronger pick.

8 · Safety, Cybersecurity & Alignment

This is the dimension where the two companies made opposite product decisions.

Astra is the first OpenAI model rated "Critical" under the Preparedness Framework — it can find unknown vulnerabilities and develop exploits without step-by-step human guidance. That capability is gated behind the Daybreak program, and the shipping model refuses exploit-creation work; safety checks can even pause unrelated tasks. Independent lab Irregular reported Astra solving 86 of 226 FrontierCyber challenges vs 34 for GPT-5.6 Sol, including zero-day findings.

Fable 5.1 sits a tier lower by design. Anthropic loosened it enough to discover software vulnerabilities (a defensive win — cyber safeguards now block 60% fewer false positives), but exploit generation and binary-based vulnerability scanning still route to Opus models. The same underlying model ships as Mythos 5.1 with lighter safeguards, restricted to vetted organizations.

On alignment, Astra posts the better headline numbers (2.4% vs 9.5% on computer-use safety; 0% honeypot cheating vs Sol's 48.2%), but OpenAI itself disclosed a regression: Astra's written reasoning is harder to monitor, and UK AISI found it could evade monitoring under adversarial prompting. OpenAI says it will withhold scaling until it regains confidence in monitoring. Anthropic's Fable 5.1, meanwhile, is praised for staying "readable over long, multi-step tasks" (Jane Street).

9 · Pricing Deep Dive: Same Sticker, Very Different Bills

Both models list at $10/$50 per 1M tokens. The rate card differs in exactly two places — and both favor Fable 5.1. But measured per task, the picture flips completely.

The rate card

What identical workloads cost (list-rate arithmetic)

What each task actually costs (measured)

10 · Real-World Head-to-Heads (People Who Actually Compared Them)

Beyond the vendor tables, several independent testers ran both models side by side. Here's what they found.

11 · Verdict: Pick on Workload Shape, Not the Tables

The one-line takeaway: GPT-6 Astra is the computer operator — faster, cheaper per task, and dominant on math, artifacts and cyber, but with a monitorability regression and gated capabilities. Claude Fable 5.1 is the reasoning workhorse — level with Astra at the top of AA's Intelligence Index (53 apiece, at roughly 2.3× the per-task cost), cheaper cache and long-context economics, and the qualitative edge in front-end polish and code review. If your work is multi-step, tool-heavy and long-horizon, test both on your workload: the rate card says they're identical, the measurements say they're not (DataCamp; MindStudio; Artificial Analysis).

12 · Sources & Data Notes

Vendor benchmark figures are as published September 1–5, 2026. Artificial Analysis figures use Intelligence Index v4.3 and Coding Agent Index v1.4 (published September 7–9, 2026) — both AA composites are now dead heats, at 53 and 62 respectively. Earlier drafts of this comparison cited Index v4.1 (Fable 5.1 66, Astra 61) and v4.2 (57 vs 55), so every AA number here is date-stamped. Headline scores are vendor-reported at maximum effort unless noted; independent checks (Artificial Analysis, ARC Prize, Irregular lab, UK AISI) and the caveats attached to each number are called out inline. Where vendors ran different task releases or harnesses (OSWorld 2.0, ARC-AGI-3, Coding Agent Index), the comparison is flagged rather than treated as apples-to-apples.

• DataCamp — GPT-6 Astra vs Claude Fable 5.1: Benchmarks and Pricing (incl. physics-simulation test)
• Artificial Analysis — GPT-6 Astra vs Claude Fable 5.1 model comparison
• Artificial Analysis — Announcing the Intelligence Index v4.3 (Sep 7, 2026)
• Artificial Analysis — Benchmarking GPT-6 Astra (Sep 9, 2026)
• MindStudio — GPT-6 Astra Benchmarks: Is It Really Better Than Fable 5.1?
• Anthropic — Introducing Claude Fable 5.1 and Claude Mythos 5.1
• Vellum — Claude Fable 5.1 & Claude Mythos 5.1 Benchmarks Explained
• Vellum — GPT-6 Astra Benchmarks Explained
• DataCamp — GPT-6 Astra: Features, Benchmarks, and Pricing
• Reddit r/better_claw — GPT-6 Astra vs Fable 5.1 vs Sonnet 5 on real agent work
• YouTube — GPT-6 Astra vs Fable 5.1 side-by-side (KingBench 3)
• YouTube — I Tested GPT-6 Astra vs Fable 5.1 (No Hype Assessment)
• Theo (t3.gg) — So I've Been Using GPT-6 Astra… (podcast/transcript)
• dev.to — GPT-6 Astra vs Fable 5.1 vs Gemini 3.8 Flash: The Ultimate Comparison
• Vals AI — Claude Fable 5.1 model page
• MarkTechPost — Anthropic Releases Claude Fable 5.1 and Claude Mythos 5.1
• OpenAI — GPT-6 Astra: A new generation of intelligence
• OpenAI — Path to Astra: critical capabilities and frontier safeguards