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Quick Answer

GPT-6 Astra vs Claude Fable 5.1: Benchmarks & Cost

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The Short Answer

OpenAI and Anthropic shipped flagship models on the same day — September 3, 2026 — at the identical headline price of $10 per million input tokens and $50 per million output tokens.

The verdict splits cleanly:

  • Claude Fable 5.1 wins on measured capability. It leads the Artificial Analysis Intelligence Index (65.6 vs 61.1) and the Coding Agent Index (70.4 vs 67.0).
  • GPT-6 Astra wins on cost per completed task, by roughly 3x, because it is dramatically more token-efficient.
  • Fable 5.1 wins on availability. It is GA on every major cloud today; Astra started in enterprise Trusted Access.
  • Astra wins on abstract reasoning, more than doubling Opus 5 on ARC-AGI-3.

Last verified: September 4, 2026.

Side by Side

GPT-6 AstraClaude Fable 5.1
LabOpenAIAnthropic
ReleasedSep 3, 2026Sep 3, 2026
Input / MTok$10.00$10.00
Cached input / MTok$1.00 (writes ~$12.50)$0.25
Output / MTok$50.00$50.00
Context window1,050,0001,000,000
Max output128,000
AA Intelligence Index61.1 (max)65.6
AA Coding Agent Index67.0 (xhigh)70.4 (max)
Cost per coding task$3.27$9.18
Output tokens per task2,200–14,00014,500–45,000
ARC-AGI-3 (standard)62.7%not scored
AvailabilityTrusted Access firstGA: AWS, GCP, Azure, API

Where Fable 5.1 Wins: Verified Benchmarks

Anthropic’s numbers are official, reproducible and were published with production safeguards enabled — which is a meaningfully higher evidentiary bar than the leaked table that accompanied Astra’s launch.

BenchmarkFable 5.1Opus 5GPT-5.6 Sol
Terminal-Bench Science 0.152.6%29.0%22.4%
Terminal-Bench 4.055.8% (Mythos: 60.9%)52.3%37.3%
GDPval-AA v2 (knowledge work)185318241711
OSWorld 2.0 (partial)77.9%75.4%not tested
Humanity’s Last Exam (no tools)60.9%56.6%not tested
CursorBench 3.2.073.4%70.0%67.2%

The Terminal-Bench Science jump is the standout: 52.6% against Fable 5’s 24.7%, more than doubling in one release. Anthropic also reports concrete scientific results — protein binders designed at nearly a 50% hit rate across 12 targets where 10–15% is typical, a new elevation map of a third of Venus derived from 30-year-old Magellan radar data, and GPU kernels that sped up seven open-source biology models by up to 2.5x.

Where Astra Wins: The Efficiency Frontier

Astra’s advantage is not price and it is not raw score. It is token parsimony.

Running the full Artificial Analysis Intelligence Index takes Astra (high) about 16 million output tokens. Gemini 3.8 Flash (high) needs about 123 million. Fable 5.1 needs 14,500–45,000 output tokens per task against Astra’s 2,200–14,000.

That compounds three ways: fewer output tokens billed at the expensive rate, less context re-read on subsequent turns, and fewer tool calls churned. The result on the coding-agent leaderboard:

Agent configurationCoding Agent IndexCost per task
Codex · GPT-5.6 Luna (max)57.1$0.29
Codex · GPT-6 Astra (low)62.6$1.41
Muse Code · Muse Spark 1.3 (xhigh)64.2$1.72
Codex · GPT-6 Astra (medium)65.1$2.19
Codex · GPT-6 Astra (xhigh)67.0$3.27
Codex · GPT-5.6 Sol (max)65.0$5.00
Claude Code · Opus 5 (xhigh)68.1$8.17
Claude Code · Fable 5.1 (max)70.4$9.18

Read the last two rows together: Fable 5.1 buys you 3.4 more index points for 2.8x the money. Whether that is worth it is a workload question, not a leaderboard question.

The Cache Gap Decides Most Real Deployments

Identical sticker prices hide the number that actually moves production bills.

Claude Fable 5.1 cache reads cost $0.25 per million tokens — a 75% cut from Fable 5. GPT-6 Astra cached input costs $1 per million, with cache writes reported around $12.50.

Long-running agents re-read their own accumulated context on every turn, so cache reads typically dominate the token bill on research sweeps, codebase work and multi-hour sessions. Anthropic estimates typical workloads land about 25% cheaper than Fable 5, and highly agentic workloads up to roughly 45% cheaper.

The two efficiency stories therefore compete directly: Astra wins by generating fewer tokens, Fable 5.1 wins by making re-read tokens nearly free. Which one wins for you depends on your input-to-output ratio. Output-heavy generation favours Astra. Context-heavy agent loops favour Fable 5.1.

The Abstract-Reasoning Gap Is Real

On ARC Prize’s official standard ARC-AGI-3 harness, GPT-6 Astra scores 62.7% for roughly $26,000 of compute, against 30.2% for Claude Opus 5 and 7.8% for GPT-5.6 Sol. Fable 5.1 is not scored on this benchmark.

Be careful with the near-perfect figure you may have seen. With a provider adapter that preserves opaque reasoning state and compacts long conversations, ARC Prize measures Astra at 99.9% for about $19,000. That is a different harness class and not comparable to other models’ standard-harness results. The 62.7% is the apples-to-apples number — and it is still more than double Opus 5.

Which One Should You Pick?

Pick Claude Fable 5.1 if you need the best measured agentic coding and knowledge work available today, you are running long context-heavy agent loops where $0.25 cache reads compound, or you need multi-cloud GA availability right now.

Pick GPT-6 Astra if your workload is hard abstract reasoning or research synthesis, you are output-token-heavy rather than context-heavy, or you need Fable-5-class coding quality at roughly a third of the per-task cost and can accept staged access.

Pick neither for volume. Both are $10/$50 flagships. High-volume extraction, classification and routing belong on Gemini 3.8 Flash ($0.75/$3.75), GPT-5.6 Luna ($0.20/$1.20) or an open-weight Flash tier. The strongest 2026 pattern is a router that sends the bulk of traffic to a cheap tier and reserves a flagship for the hard tail.

One caveat on both: the leaked Astra benchmark table remains unverified, and OpenAI’s own evaluations ran at max effort unless noted. Anthropic’s Fable 5.1 numbers are official. Weight them accordingly until LMArena and further independent runs land.

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