GPT-6 vs. Claude Fable 5.1: Benchmarks, Speed, Price and Which to Pick

The artificial intelligence landscape has reached a fascinating inflection point. Anthropic launched Claude Fable 5.1 on September 1, 2026, and OpenAI followed just two days later with GPT-6 Astra on September 3, 2026. Both flagship models occupy the highest-priced $10 per million input tokens and $50 per million output tokens tier, yet they represent fundamentally different engineering philosophies. That split is exactly why this GPT-6 vs Claude Fable 5.1 comparison matters. There is no single universal winner in this head-to-head showdown. Claude Fable 5.1 leads independent intelligence metrics across a wider array of reasoning benchmarks, while GPT-6 Astra dominates LLM Stats composites, token efficiency, output speed, and computer use execution.

To help you navigate this massive generational leap, this article breaks down every shared benchmark, output speed, context window, tokens per task, and cost per task using live data from Artificial Analysis, llm-stats compare page, Epoch-tracked FrontierMath, and the BenchLM taxonomy. For deeper context on OpenAI’s flagship architecture, explore our GPT-6 Astra benchmarks overview, or review Anthropic’s side in our Claude Fable 5.1 deep dive.

At a glance: specs compared

Before diving into complex reasoning evaluations, it helps to examine the baseline hardware and API specifications of both models side by side. Both maintain proprietary architectures, identical output limits, and symmetric tier pricing.

Independent intelligence: who ranks #1 where

When evaluating raw intelligence, rankings fluctuate depending on the testing harness and composite scoring methodology. According to the Artificial Analysis Intelligence Index v4.1.1, Claude Fable 5.1 captured the #1 position out of 202 evaluated models with a score of 66, while GPT-6 Astra sat at #8 with a score of 61. In the subsequent v4.2 update, which added harder tasks and more private test sets, scores rebased to 57 for Fable and 55 for Astra, preserving the relative ordering with Fable still on top. Similarly, in the Coding Agent Index, Fable registers 70 compared to Astra’s 67 in native agentic harnesses, though Astra costs roughly half as much per coding task ($4.72 versus $9.18).

However, the narrative flips completely when examining LLM Stats composites. LLM Stats places GPT-6 Astra at #1 overall with a composite score of 60.7, edging out Fable at 56.8. A breakdown of subcategories reveals Astra leading in Reasoning (58.7 vs 53.9), Coding (49.0 vs 36.0), Agents (46.4 vs 41.9), and Tool Use (36.6 vs 30.7), while Vision ties at 41.5. This divergence explains why both companies can legitimately claim top-tier dominance: Fable excels in independent academic reasoning composites, whereas Astra excels in pragmatic developer workflows and multi-step tool execution.

Fable leads 9 of the 16 directly comparable v4.2 components: the Intelligence Index itself, the Coding Agent Index, AA-Briefcase Elo and rubric, GDPval-AA v2, Tau3-Banking, SciCode, AA-Omniscience Accuracy, and AA-LCR v1.1. Astra’s outright intelligence wins are GDP.pdf, CritPt, and the Omniscience Index via a much lower hallucination rate (51% versus 73%). On cost and time Astra sweeps: $2.57 versus $6.12 per intelligence task, $4.72 versus $9.18 per coding task, and 4.0 versus 9.5 minutes per task. Harvey LAB-AA and EnterpriseOps-Gym-AA have published Fable scores with no GPT equivalent yet.

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Every shared benchmark, head-to-head

A granular look at all 32 reported benchmarks highlights distinct operational specializations. Where only one vendor published a score, the other column shows a dash, meaning unpublished rather than zero. Shared rows where both vendors agree are the fairest head-to-head signal, and Astra leads most of them, while Fable’s lonely rows (HLE without tools, CursorBench) and Astra’s lonely rows (cyber, BrowseComp, ScreenSpot-Pro) show where each lab chose to compete. The five Artificial Analysis standardized rows at the foot of the table retest both models in one neutral harness, and there Fable takes Terminal-Bench v2.1, SciCode, MMMU-Pro and HLE while Astra takes GPQA Diamond.

Researchers must also account for harness discrepancies across standardized suites. For example, in ARC-AGI-3 evaluations, provider adapter configurations achieve up to 99.9% effectiveness, whereas standardized evaluations record 62.7%. On OSWorld the vendors ran different subsets (Astra 72.6% offline versus Fable 41.7% strict), and llm-stats tracks a separate OSWorld 2.0 lane where Fable posts 77.9%, proving that benchmark victories often depend heavily on execution harness design. Similarly, GDPval-AA v2 shows 1629 on the Artificial Analysis run for Astra against 1853 on Anthropic’s own run for Fable, so the table keeps both figures with their harnesses labeled.

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Speed, tokens per task, and cost per task

Raw model intelligence is only half the equation when deploying production workloads. Operational velocity and token economy dictate real-world infrastructure expenses. Artificial Analysis live comparisons demonstrate that GPT-6 Astra generates output at 87.5 tokens per second, outpacing Claude Fable 5.1 at 68.7 tokens per second.

Furthermore, Astra completes standardized index tasks in an average of 4.0 minutes, compared to 9.5 minutes for Fable.

Token consumption per task reveals an even starker efficiency gap. GPT-6 Astra utilizes approximately 20,985 total output tokens per task (comprising 8,372 answer tokens and 12,614 reasoning tokens), whereas Claude Fable 5.1 consumes roughly 64,072 output tokens (25,478 answer tokens and 38,594 reasoning tokens). This means Astra achieves top-tier results using roughly one-third of the reasoning overhead. Consequently, the cost per index task sits at $2.57 for Astra versus $6.12 for Fable, making Astra significantly cheaper per workflow execution despite similar per-token API pricing. However, for applications relying heavily on cached context reads, Fable holds an advantage with its $0.25 prompt cache read price compared to Astra’s $1.00.

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What Epoch.ai and BenchLM add

To contextualize these figures, independent research trackers provide crucial verification. Epoch.ai actively monitors FrontierMath v2 across Tiers 1 through 3, as well as the ultra-difficult Tier 4 research-level mathematics suite. GPT-6 Astra’s remarkable 97.6% score on Tier 4 is directly backed by Epoch’s verification framework, ensuring the figure is free from data contamination.

Concurrently, the BenchLM.ai directory has established a comprehensive taxonomy spanning 422 distinct evaluation benchmarks. BenchLM formally standardizes suites like Terminal-Bench 4.0, Terminal-Bench-Science 0.1, OSWorld 2.0, FrontierCode, and ExploitBench. While many entries in the directory serve as display-only references without active scores, they provide engineers with an indispensable dictionary for understanding harness definitions and prompt constraints.

Which should you pick?

Choosing between OpenAI GPT-6 Astra and Anthropic Claude Fable 5.1 depends entirely on your specific workload requirements:

• Pick Claude Fable 5.1 if: You require maximum neutral intelligence, long-form agentic research, complex knowledge synthesis, superior Humanity’s Last Exam (HLE) performance, or specialized academic coding suites like SciCode and GDPval.
• Pick GPT-6 Astra if: You need lightning-fast computer use execution, advanced mathematical problem solving, rigorous cybersecurity analysis, extreme token efficiency, superior raw output speed, and a slightly larger 1.05M context window.

Frequently Asked Questions

When were GPT-6 Astra and Claude Fable 5.1 released?

Anthropic released Claude Fable 5.1 on September 1, 2026, closely followed by OpenAI launching GPT-6 Astra on September 3, 2026. Both models arrived within 48 hours of each other, initiating a fierce new generation of flagship AI capabilities.

Why do Artificial Analysis and LLM Stats disagree on the #1 spot?

The disagreement stems from weighting methodology. Artificial Analysis emphasizes independent academic reasoning indices where Fable leads, whereas LLM Stats composites focus heavily on pragmatic developer workflows, multi-step tool use, and agentic speed, where Astra dominates.

How do their pricing models and cache read costs compare?

Both models charge $10.00 per million input tokens and $50.00 per million output tokens for standard calls. However, prompt cache read pricing differs significantly: Claude Fable 5.1 charges $0.25 per million tokens, while GPT-6 Astra charges $1.00.

Which model is better for coding and agentic workflows?

For raw multi-step coding execution and task efficiency, GPT-6 Astra uses approximately one-third of the reasoning tokens per task and completes benchmarks faster. For deep academic code reasoning and complex agentic research, Claude Fable 5.1 remains exceptionally competitive.

Bottom Line

The rivalry between OpenAI GPT-6 Astra and Anthropic Claude Fable 5.1 highlights two distinct paths to AI excellence. Anthropic’s Fable 5.1 is an intelligence powerhouse that excels in academic reasoning and rigorous knowledge benchmarks. OpenAI’s GPT-6 Astra is an efficiency juggernaut that delivers blistering output speeds, advanced computer use, and profound token economy that cuts task execution costs in half. By matching your operational priorities to their respective strengths, developers and enterprise leaders can harness the optimal frontier model for any workflow.