Claude Fable 5.2 vs GPT 6 Astra: Cheaper per Token, Pricier per Task (November 2026)

	Claude Fable 5.2	GPT 6 Astra
Vendor	Anthropic	OpenAI
Released	November 9, 2026	September 2026
Input per 1M tokens	$8	$10
Output per 1M tokens	$40	$50
Cached input read	$0.20	$1.00
Long context surcharge	None listed	Above 272K input tokens
Context window	1M	1.05M
Max output	128K	128K
Effort levels	Low, medium, high, xhigh, max	Low, medium, high, xhigh, max
AA Intelligence Index (max effort)	65	57

Claude Fable 5.2 arrived on November 9, 2026, two months after GPT 6 Astra, and it was clearly built to contest the top of the market. The two models are close in context and output limits, support the same five effort levels, and both carry a 50% batch discount. The differences are in price structure, where each one is strong, and what each costs to finish an actual task.

Pricing: Fable is cheaper on every line

At list prices Fable 5.2 is 20% cheaper on input and output, and cache reads cost one fifth as much ($0.20 against $1.00). Astra adds a long context surcharge: above 272K input tokens the whole request bills at double the input and cache rates and 1.5 times the output rate. Fable has no long context tier.

Anthropic's workload examples show how the structure compounds:

Monthly workload	Fable 5.2	Astra	Astra premium
Balanced assistant (1M in, 250K out)	$18.00	$22.50	+25%
Retrieval, requests under 272K (10M in, 1M out)	$120	$150	+25%
Retrieval, requests over 272K (10M in, 1M out)	$120	$275	+129%
Cache heavy agent loop (100K prefix, 1,000 cached reads)	$101	$201	+99%

For retrieval heavy and cache heavy systems, which describes most production agents, the structural gap is much larger than the headline 20%. Fast mode follows the same pattern: Fable's costs $16 and $80 for up to 2.5 times the speed, while Astra's costs double its standard rates for up to twice the speed.

Benchmarks: Fable leads, with caveats

Benchmark	Fable 5.2	GPT 6 Astra
FrontierSWE v2	69.9%	65.5%
FrontierCode 2.0	49.1% (45.3% at high effort)	42.8%
Terminal Bench Science 0.1	74.6%	68.1% (OpenAI reports 64.6%)
AutomationBench	51.4%	41.4%
Humanity's Last Exam (with tools)	74.2%	57.2%
Remote Labor Index (high effort)	41.3%	20.8%
AA Briefcase v1.1	66.1% (1785 Elo)	52.0% (1569 Elo)
OSWorld 2.1 (offline set, partial)	89.3%	79.5%
Chartography (no tools)	73.4%	71.0%
RoboDojo Sim	64.9%	29.8%
AA Intelligence Index (max effort)	65	57

Most of these figures come from Anthropic's launch table, so apply the usual discount. Three things support taking the direction seriously. OpenAI reports the same Astra score on Humanity's Last Exam and AutomationBench. The independent Artificial Analysis index agrees, with an 8 point gap at max effort. And the margins are widest on long horizon work (Remote Labor Index, Humanity's Last Exam) and narrowest on agentic coding, where FrontierSWE v2 is 4.4 points apart.

Astra's strengths sit mostly on rows Anthropic did not publish. OpenAI reports 96.3% on the 8 needle MRCR v2 long context test in the 512K to 1M token band, and describes FrontierMath Tier 4 and ARC AGI 3 as near saturated. It also holds the record Math score on the Epoch Capabilities Index, which we examined in our Epoch comparison. One caution on OSWorld: OpenAI reports Astra on version 2.0 of that benchmark, while Anthropic's comparison uses version 2.1, so the numbers are not strictly comparable.

Cost per task flips the story

Artificial Analysis measured the cost to run its Intelligence Index at max effort. Fable 5.2 cost about $26.54 per task. GPT 6 Astra cost about $6.24 and scored 57. The reason is output volume: Fable generates far more tokens per task, and those tokens bill at $40 per million.

Effort level narrows the gap sharply:

Configuration	Index score	Cost per index task
Fable 5.2 medium	56	$3.80
Fable 5.2 high	62	$9.08
Fable 5.2 max	65	$26.54
GPT 6 Astra max	57	$6.24

At medium effort Fable is cheaper than Astra at max effort and one point behind it. At high effort it is five points ahead for about 45% more. At max effort it buys eight points for four times the spend. A hands on shader test by DataCamp showed the same shape at high effort: Fable scored 5.0 on a five point rubric for $4.91 and 95,683 output tokens, while Astra scored 3.7 for $1.75 and 22,934 tokens. Fable was clearly better and nearly three times the cost.

The practical reading is that these models occupy different points on a quality and spend curve, and the right comparison is at matched quality, not matched effort label. If you need Astra level results, Fable at medium effort is competitive on cost. If you need the best result either vendor can produce, Fable at max effort is the strongest option and the most expensive one.

Safeguards and data retention

This is where many enterprise decisions will actually be made.

1. Cybersecurity. Both block offensive security work by default. Fable 5.2's classifiers block penetration testing, exploit generation, and most research level biology. Astra is rated at the Critical cybersecurity threshold under OpenAI's Preparedness Framework, with exploit creation gated behind its Daybreak program.
2. Data retention. Astra supports Zero Data Retention for eligible API customers. Fable 5.2 requires 30 day retention for safety monitoring unless an enterprise uses Enterprise Frontier Safeguards. For regulated workloads this can settle the question by itself.
3. Availability. Both are on their vendors' apps and APIs, the three major clouds, GitHub Copilot, and OpenRouter. Astra is off by default for ChatGPT Enterprise workspaces, and Pro, Business, and Enterprise plans also get GPT 6 Astra Pro.

How to choose

If your workload is	Lean toward	Why
Cache heavy agents or long retrieval prompts	Fable 5.2	No long context surcharge, cache reads at one fifth the price
Hard long horizon autonomous work	Fable 5.2 at high effort	Widest benchmark gaps, 62 on the index for $9.08 per task
Cost sensitive hard reasoning	Astra, or Fable at medium effort	Astra's $6.24 per task is hard to beat at max
Math heavy work and very long context recall	GPT 6 Astra	Record Math index score, 96.3% on 8 needle MRCR
Zero data retention required	GPT 6 Astra	Fable requires 30 day retention by default
Security research	Neither by default	Both gate offensive work

If you are weighing Astra's premium against a cheaper Anthropic option, Fable 5.2 vs Opus 5.5 covers the step down, and our guide to when Astra's premium tier pays off still applies, with Fable 5.2 now setting the bar it has to clear.

FAQ

Is Claude Fable 5.2 better than GPT 6 Astra? On Anthropic's benchmark table and on the independent Artificial Analysis index (65 against 57 at max effort), yes. Astra keeps advantages in long context recall, math, and data retention terms.

Which is cheaper, Fable 5.2 or GPT 6 Astra? Fable is cheaper per token on every line. Per task at max effort, Astra is cheaper ($6.24 against $26.54 per index task). At medium effort Fable drops to $3.80 per task.

Does GPT 6 Astra have a long context surcharge? Yes. Above 272K input tokens the whole request bills at double the input and cache rates and 1.5 times the output rate. Fable 5.2 lists no such tier.

Can I use Fable 5.2 with zero data retention? Not by default. It requires 30 day retention for safety monitoring unless an enterprise uses Enterprise Frontier Safeguards. Astra supports Zero Data Retention for eligible API customers.

When this applies to your stack

Comparing these two on list price or on a single benchmark misses most of what decides the bill: effort setting, output token volume, prompt length relative to the 272K threshold, and how much of your context is cacheable. The reliable approach is to run a sample of your real tasks through both at two or three effort levels and compare cost per completed task. Building that evaluation harness and the routing layer behind it is the kind of applied AI work we do at Contra Collective.