Model comparisons

GPT-6 Astra vs Claude Fable 5.2: Which Model Fits?

Compare GPT-6 Astra and Claude Fable 5.2 on API cost, prompt caching, Anthropic's benchmark table, plan access, data retention, safeguards and migration changes.

UpdatedNovember 9, 2026

In short

Choose Claude Fable 5.2 first when cost and large repeated inputs matter: it lists 20% below GPT-6 Astra ($8 and $40 per million input and output tokens against $10 and $50), its cache reads cost a fifth as much, and its price stays flat across a 1M-token window, while Astra charges more above 272K input tokens. Choose Astra first when your work already runs through OpenAI's Responses API, ChatGPT Work or Codex, or when you need zero data retention without a model-specific exception. Anthropic's launch table scores Fable 5.2 above Astra on every row that reports both, but those are Anthropic's figures; trial both on your own tasks before moving work.

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Fable 5.2 lists below Astra on every token category

Prices are per million tokens on each vendor's direct API at Standard rates. Read the Astra column as requests up to 272K input tokens; the last paragraph of this section covers longer ones.

Token category	GPT-6 Astra, up to 272K input	Claude Fable 5.2
Uncached input	$10	$8
Output	$50	$40
Cache read	$1	$0.20
Cache write	$12.50, 30-minute lifetime	$10 for 5 minutes; $16 for 1 hour
Batch input / output	$5 / $25	$4 / $20
Fast mode input / output	$20 / $100	$16 / $80

The gap widens when a session rereads cached context. As an illustration, take a session of 20,000 uncached input tokens, 100,000 cache-write tokens, 800,000 cache-read tokens and 10,000 output tokens, with every request under 272K and every repeat a cache hit. Astra costs $0.20 + $1.25 + $0.80 + $0.50 = $2.75. Fable 5.2, with the five-minute cache, costs $0.16 + $1.00 + $0.16 + $0.40 = $1.72, about 37% less. This is ToolColumn's token arithmetic, not a measured bill: real sessions differ in tokenization, reasoning, tool calls and retries, and both vendors bill reasoning as output.

Above 272K input tokens, Astra bills the entire request at $20 input, $2 cache read, $25 cache write and $75 output. Fable 5.2 keeps its standard rates across its 1M window, so large-document work favors it further. Astra's Fast mode is unavailable with EU data residency; Fable 5.2's fast mode runs in Claude Code, on Anthropic's API, Google Cloud and Microsoft Foundry.

Anthropic's table puts Fable 5.2 ahead of Astra

Anthropic's Fable 5.2 launch table includes GPT-6 Astra at its highest available effort. The rows below are those that report both models; Anthropic ran some competitor scores on its own harness, and OpenAI has not published these comparisons.

Benchmark (Anthropic's figures)	Claude Fable 5.2	GPT-6 Astra
FrontierCode 2.0 (agentic coding)	49.1% (xhigh)	42.8%
FrontierSWE v2 (agentic coding)	69.9%	65.5%
AutomationBench (business workflows)	51.4%	41.4%
Terminal-Bench-Science 0.1 (scientific research)	74.6%	68.1%
OSWorld 2.1, offline set, partial (computer use)	89.3%	79.5%
Remote Labor Index (knowledge work)	41.3% (high)	20.8%
Humanity's Last Exam, with tools	74.2%	57.2%
Chartography, no tools (chart reading)	73.4%	71.0%

The narrowest gaps are on chart reading and agentic coding, between two and seven points; the widest are on long knowledge work and Humanity's Last Exam, where Fable 5.2 leads by 17 to 21 points. Anthropic also reports that on FrontierCode 2.0, Fable 5.2 at high effort beat Astra's best score at about the same cost per task. OpenAI's own Astra table, published before Fable 5.2, compared Astra with Fable 5.1 and favored Astra on Terminal-Bench 4.0; no table from either vendor yet compares the two current models on the same terms.

Plan access and data handling decide many teams before benchmarks do

In ChatGPT, Astra is listed for Plus, Pro, Business and Enterprise, and Enterprise administrators have to enable it. In Claude, Fable 5.2 is on every paid plan: up to 50% of the weekly limit on Max, Ultra and Team Premium seats, and usage credits on Pro and Team Standard seats, apart from a launch week that ends November 16, 2026 at 11:59 pm PT. In Claude Code it needs version 2.1.321 or later and the fable alias; it is not the default on any plan.

Retention differs more than access. OpenAI offers Astra zero data retention to eligible API customers. Fable 5.2 requires 30-day retention for safety monitoring, and zero data retention needs Anthropic's express authorization. Anthropic's Enterprise Frontier Safeguards, in private beta, runs that monitoring inside the customer's infrastructure so eligible customers can keep zero data retention. Check the contract before sending restricted data to either model.

Safeguards also affect completion. Fable 5.2 blocks high-risk cybersecurity work and most research-biology tasks and answers those requests with Opus 4.8 or Opus 5 instead; Anthropic says it blocks about 55% fewer requests than Fable 5.1. Astra's monitoring can pause work in ChatGPT or Codex and stop API work, and its most advanced cybersecurity access has separate restrictions.

Moving an integration between them takes code changes

Both models accept text and images, return up to 128,000 output tokens and offer five effort levels from low to max; Astra's context window is 1,050,000 tokens and Fable 5.2's 1M. On the OpenAI side, tool calling needs the Responses API, and a client built for another provider has to adopt its asynchronous tools and in-flight steering. On the Claude side, Fable 5.2 rejects forced tool_choice values and manual thinking budgets, checks returned thinking blocks on every account, binds its thinking blocks to Fable 5.2 and Mythos 5.2, and refuses the older computer_20251124 tool outside Amazon Bedrock. A client already on Fable 5.1 meets the same changes; Claude Fable 5.2 vs Fable 5.1 lists them.

A three-task trial settles it for your workload

Run one coding change, one source-based research question and one document deliverable on both models, with the same inputs, tools, permissions and acceptance criteria. Record the exact model, provider, effort, context tier, cache hits and any fallback, then compare accepted results, total spend, elapsed time and reviewer corrections. Effort labels are not equal compute budgets across vendors, so set them deliberately. If neither model is needed for routine work, Claude Opus 5.5 vs Fable 5.2 vs Sonnet 5.5 and ChatGPT Plus vs Pro cover the cheaper steps on each side.

Edited byRick Finch

Official sources

Editorial guidance grounded in official product sources.

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FAQ

Common questions

Which costs less per completed task, GPT-6 Astra or Claude Fable 5.2?

Fable 5.2 lists lower: $8 input and $40 output per million tokens against Astra's $10 and $50 up to 272K input tokens, and cache reads cost $0.20 against $1. Above 272K input tokens Astra bills the whole request at higher rates, while Fable 5.2 keeps one price across its 1M window. The bill still depends on how many tokens, retries and corrections each model needs on your task, so compare finished tasks.

Does a ChatGPT or Claude subscription cover direct API calls?

No. Both vendors bill their app subscriptions and their APIs separately. Astra in ChatGPT Work and Codex draws on the plan allowance; Fable 5.2 in Claude draws on up to 50% of the weekly limit on Max, Ultra and Team Premium seats, and on usage credits on Pro and Team Standard seats outside the launch week. An API key uses the API account and its rates, though Claude Max, Ultra and Team include fixed monthly API credits. Decide which account pays before comparing plans or token budgets.

Is there a universal benchmark winner between Astra and Fable 5.2?

Not one that applies to every task. Anthropic's launch table puts Fable 5.2 ahead of Astra on every row that reports both, but Anthropic chose the benchmarks, set the harness and ran some competitor scores itself. Treat the table as a reason to trial Fable 5.2, then judge both models on your own acceptance criteria.

For repository work, should I compare the models or the coding agents?

For a model choice inside your own application, hold the execution environment and tools as constant as possible. For a purchase decision involving Codex or Claude Code, evaluate the complete agent: repository access, permissions, tests, review flow and billing affect the outcome. Record the actual model and effort; a model benchmark does not measure the entire product.

Can an organization with ZDR enabled use both models?

Check each model first. OpenAI offers Astra zero data retention to eligible API customers. Fable 5.2 requires 30-day retention, and zero data retention needs Anthropic's express authorization; Enterprise Frontier Safeguards, in private beta, lets eligible enterprise customers keep zero data retention while Anthropic's monitoring runs in their own infrastructure. Model approval also does not cover every storage service or connected tool.

Should a Fable 5.1 user move to Fable 5.2 before trying Astra?

Usually yes. Fable 5.2 keeps Fable 5.1's plan rules, costs 20% less on every API rate and scores higher across Anthropic's table, so it is the cheaper test of whether the Claude workflow still fits. API clients must first handle four changes: the thinking-block prefix check on every account, model-bound thinking blocks, the new computer-use tool version and matching advisor models.

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