GPT-6 Astra vs Claude Fable 5.1: Same Price, 1.5x the Tokens

GPT-6 Astra and Claude Fable 5.1 share a list price of $10 per million input tokens and $50 per million output tokens, but they no longer score alike. On the Artificial Analysis Intelligence Index v5.0, the nine-evaluation version that replaced v4.3.2 on October 27–28, 2026, Astra scores 57 to Fable's 49 at maximum effort and leads by 5–8 points at every effort level. The bills differ too. In a same-prompt test on October 26, 2026, Fable counted about 1.45 times as many tokens as Astra for the same English text and cost 1.49–1.61 times as much on eight small coding and reasoning tasks that both models passed. Astra has its own surcharges: it writes long prompts to its cache by default at a premium rate, and it bills an entire request at $20/$75 once input passes 272K tokens.

So start with gpt-6-astra for short or uncached requests, output-heavy agent turns, and agents built on OpenAI's Responses tools. Start with claude-fable-5-1 when single requests regularly exceed 272K tokens, because Fable stays cheaper there for English text even after its extra tokens, or when your stack already runs on Claude and its 30-day retention requirement fits your data. For a long, reused English prefix the two come out roughly even on cost, and Artificial Analysis's composite now favors Astra, so Fable has to win on your own tasks to earn that case.

Before either, ask whether you need a flagship at all. Anthropic now tells most teams to start with Claude Opus 5.5 and move to Fable only when Opus at higher effort still falls short, and OpenAI describes GPT-6.1 Sol as near-Astra performance at $2/$10. On the same eight tasks, gpt-6.1-sol cost about one-fifth of what Astra did. Run a small acceptance test on your own work before you move traffic to either flagship.

Anthropic released Claude Fable 5.2 on November 9, 2026 at $8 input and $40 output per million tokens, and Fable 5.1 is still available. This page stays about Fable 5.1; for the newer model, see Claude Fable 5.2 vs GPT-6 Astra: When Each Costs Less per Task.

What is available now

Both models can be called through their providers' APIs as of October 26, 2026, but neither API page proves that your account can use them. OpenAI's GPT-6 Astra model page lists gpt-6-astra as the only snapshot, and Astra is still the first row of the flagship table on the OpenAI API pricing page. API access follows the organization and project behind your key, so a first request is the real check; GPT-6 Astra API Access: Check Your Project and Make a First Request walks through it.

In ChatGPT, access depends on the plan and the surface. OpenAI's ChatGPT pricing documentation estimates 5–45 Astra local messages per five hours in Work and Codex for Plus and standard Business, and Pro has no five-hour limit. The ChatGPT models page notes that some paid plans omit Astra Extra High and points Free and Go users to GPT-6 Luna. Astra is off by default in eligible Enterprise and Edu workspaces until a workspace administrator enables it, and enabling it there doesn't grant API access. OpenAI's learn pages don't spell out which plans get Astra in Chat itself; third-party reports say Pro, Business, and Enterprise, with Plus limited to Work and Codex.

Anthropic released claude-fable-5-1 on September 1, and its model page lists it as Active and latest, with availability on the Claude API, Amazon Bedrock, Claude Platform on AWS, Google Cloud, and Microsoft Foundry. Retirement is listed as not sooner than September 1, 2027. On Claude plans, Anthropic's plan article says Max and premium Team seats can spend up to 50% of their weekly usage limits on Fable, Pro and standard Team seats pay for Fable with usage credits from the first message, and the Free plan doesn't get it. Claude Code needs version 2.1.255 or later. Claude Usage Limits by Plan: What 5x and 20x Actually Multiply covers how those limits work.

Before building an evaluation, make one minimal request through the path you intend to run in production. Record the requested model, returned model, HTTP status, stop reason, usage fields, and any organization or region error. "Announced," "visible in a picker," "accepted by a gateway," and "completed a valid response" are four different states.

The specifications that change a real decision

The two models match on headline rates and output ceiling, and they differ on almost everything that decides the final bill: what a cache write costs, whether caching happens without being asked, what happens above 272K tokens, and how many tokens the same text becomes. Rates below are direct-API Standard prices in USD per million tokens as of October 26, 2026.

The Astra rates come from the OpenAI API pricing page. The 12-hour write was added on October 20, 2026, according to the OpenAI API changelog, and the OpenAI prompt caching guide says caching is enabled by default, that writes on GPT-5.6 and later cost 1.25 times the uncached input rate, and that the 12-hour lifetime is chosen in the Responses API. Batch and Flex cost half the Standard rates, Fast processing costs twice as much, and Ultrafast, offered since September 29, costs six times as much.

The Fable rates come from Anthropic's model pricing table and context-window guidance. Batch is 50% off, US-only processing through inference_geo costs 1.1 times as much, and Fast mode isn't offered for Fable. Anthropic's token counting guide says Fable's tokenizer counts roughly 30% more than Claude models before Opus 4.7, but neither provider publishes a Claude-versus-OpenAI figure. The measured ratio is in the next section. Marketplace prices and negotiated discounts may differ from all of these.

How many more tokens Fable counts for the same text

Fable counted 1.23–1.90 times Astra's input tokens for the same passage, depending on the language. On October 26, 2026, the same short passage, written in six languages with the same meaning, was sent to both models, and the input token counts each API reported were compared after subtracting each model's count for the instruction alone (Astra 23 tokens, Fable 34). A 6,723-word English article came out at 10,106 tokens on Astra and 15,335 on Fable, a ratio of 1.52x, and a short code-style prompt came out at 1.46x.

Each language rests on one passage of about 120–150 words, and the counts are the usage fields returned through one gateway, so treat the ratios as a starting estimate rather than a constant. They point the same way as an independent test by Crazyrouter, which found 1.91x on English filler text. If your prompts mix languages or carry a lot of code, send a few real ones to both APIs and read the usage fields, because the ratio decides the calculations that follow.

Two calculations expose the hidden difference

Equal list prices invite the assumption that both providers count the same tokens. They don't, so let r be the number of tokens Fable counts for text that Astra counts as one token, and measure every quantity below in Astra tokens. The formulas stay visible so you can rerun them with your own ratio.

Take a repeated agent turn that reads 250K cached tokens, adds 10K new input tokens, and produces 5K output tokens. Astra charges 0.25 × $1 + 0.01 × $10 + 0.005 × $50 = $0.60, and Fable charges r × (0.25 × $0.25 + 0.01 × $10 + 0.005 × $50) = r × $0.4125.

The break-even is r = 0.60 / 0.4125 ≈ 1.45, which is almost exactly the English ratio measured above. English text therefore costs about $0.60 on both, or $0.63 on Fable at the long-article ratio of 1.52x. A Japanese prefix (1.23x) costs Fable about $0.51, and Korean or Spanish (about 1.9x) about $0.78. If each turn's new tokens are also written to cache, as Astra does by default and Fable does with top-level cache_control, the turn costs $0.625 on Astra and r × $0.4375 on Fable, and the break-even moves only to 1.43. Fable's four-times-cheaper cache read is real, but for English its higher token count cancels it.

Now take a one-off request with 300K uncached input tokens and 10K output tokens. Astra crosses its 272K threshold, so the whole request costs 0.30 × $20 + 0.01 × $75 = $6.75 with caching turned off, or 0.30 × $25 + 0.01 × $75 = $8.25 if default caching writes the prompt at the long-context write rate. Fable charges r × (0.30 × $10 + 0.01 × $50) = r × $3.50, which is $5.08 at 1.45x, $5.25 at 1.5x, and $6.65 at 1.9x.

Here the break-even is r = 6.75 / 3.50 ≈ 1.93, so Fable stays cheaper for long one-off inputs in all six languages measured. Korean and Spanish get close to even unless Astra's default cache write applies, which raises the break-even to 2.36. Capacity runs the other way. Fable's 1M-token window, counted in Fable tokens, holds roughly 670K Astra tokens of English prose and about 525K of Korean or Spanish, an estimate from the one-passage ratios. Astra accepts up to 922K input tokens, so the very largest English inputs fit only on Astra.

Neither calculation is a quote, and both assume the same text produces r times as many tokens in output as in input. Real output also differs by model: in the test below, Fable wrote 1.6–1.9 times as many output tokens, and output is $0.25 of Astra's $0.60 turn. The first cache write matters too. Writing the 250K prefix costs Astra 0.25 × $12.50 + 0.01 × $10 + 0.005 × $50 = $3.475 at the 30-minute rate and Fable r × $3.475 at the 5-minute rate, about $5.04 for English, and Fable's shorter lifetime means more rewrites when turns are far apart. Add tool fees, failed attempts, cloud markup, and taxes, then divide spend by usable work:

cost per accepted task = cost of every attempt / number of accepted results

A $5.08 Fable request that needs two retries costs more than one $6.75 Astra request that passes immediately. Review time and rollback work belong beside the API bill even when they aren't token charges.

Same-prompt test on October 26, 2026: Fable cost 1.49–1.61x Astra

On tasks that both models solved, Fable cost about one and a half times as much as Astra. On October 26, 2026, the same eight tasks were sent to both models through the LaoZhang API gateway (laozhang.ai): Astra through /v1/chat/completions, Fable through /v1/messages, both streamed, with no system prompt, tools, cache settings, or effort fields, so each model ran at its default effort. The tasks were a word problem, merge_intervals, parse_duration, top_words, a five-bullet summary of a 6,723-word article, and, in a second round with two samples each, an expression evaluator, a CSV line parser, and a digit-sum count. Hidden tests graded every answer, and all 11 answers from each model passed.

Disclosure: laozhang.ai is our own service. Its prices are platform pricing, not an independent comparison.

Fable's cost was 1.49x Astra's in round 1 and 1.61x in round 2, and it produced 1.87x and 1.61x as many output tokens. Part of that output is thinking you can't see on this path. Fable's adaptive thinking is always on, and its output_tokens include it even though no thinking text came back through the gateway: a one-word "ok" reply billed 132 output tokens and took 15.4 seconds, against 5 tokens and 2.8 seconds on Astra. Astra reports its reasoning separately in reasoning_tokens, for example 256 of the 412 output tokens on the word problem. Speed went slightly Fable's way, with each round finishing 8–14% sooner and the first text usually arriving earlier on short tasks.

Caching behaved as the documentation describes. On the 10,106-token article prompt, Astra wrote 10,103 tokens to cache without being asked, and the gateway billed them at the $12.50 write rate, about $0.025 more than plain input. Fable wrote nothing because no cache_control was set. Astra's other prompts, all under about 1,000 tokens, weren't written, which fits OpenAI's 1,024-token minimum for caching. For scale, the same eight tasks on gpt-6.1-sol on October 25, 2026 cost $0.0387 and $0.0368 for the two rounds, so Astra cost 5.8x and 5.2x as much as Sol, and Sol passed all of them too.

The test has clear limits. It used one gateway on one day with one or two samples per task, and the tasks were easy enough that both models passed everything, so it measures cost, tokens, and latency at an equal pass rate, not which model is smarter. Astra ran through Chat Completions, not the Responses API, with no tools. Token counts are the usage fields the gateway returned, since Anthropic's token-counting endpoint isn't available there. Caching beyond the one automatic write and inputs above 272K tokens weren't exercised, so the two calculations above are arithmetic on list prices, not measurements. The 38 calls cost $1.10 at list price.

The APIs ask you to build different agent loops

GPT-6 Astra accepts reasoning efforts low, medium, high, xhigh, and max through reasoning_effort, and none returns HTTP 400. OpenAI's GPT-6 guidance says Astra supports Chat Completions but tool calling requires the Responses API, which also carries async tools and mid-turn steering. OpenAI's documentation doesn't state Astra's default effort, and the ChatGPT and Codex guidance suggests starting Astra at Light, the low setting. If your agent depends on computer use, GPT-6 Astra Computer Use: Build the Right Responses API Loop covers that loop.

Claude Fable 5.1 uses adaptive thinking that is always on, with effort from low to max and high as the documented API default. Anthropic's Fable 5.1 changes page lists several requests that return HTTP 400: forced tool_choice values any and tool, assistant prefill, budget_tokens or disabled thinking, and temperature, top_p, or top_k. auto and none still work for tool_choice. Caching is opt-in through cache_control, either once at the top level or per block, with a 512-token minimum prefix.

Matching a label such as "high" therefore doesn't create a fair test. The model plus its native tool protocol is what you are choosing. Give each model equivalent permissions and the same result requirements, then log the exact provider settings rather than pretending the controls are identical.

Security and retention can decide before quality does

If a dataset can't sit in a 30-day retention window, Fable is out before quality matters. Anthropic's API data retention page lists Fable 5.1 as a Covered Model that requires 30-day retention, and says zero data retention (ZDR) isn't available for it unless Anthropic expressly authorizes it. Organizations that don't meet the requirement get HTTP 400, and a ZDR organization can enable 30-day retention for a single workspace to use Fable there.

On the OpenAI side, the OpenAI data controls guide sets ZDR per endpoint with prior approval. /v1/responses and /v1/chat/completions are eligible, no Astra-specific exclusion is listed, and OpenAI reserves the right to make models ineligible for specific customers. Eligibility belongs to your organization and project, not to the model name.

Fable also has a safeguard fallback. When its safeguards flag a request, Claude apps can switch the reply to another model and show which model answered. In an API integration, log the returned model on every response so a fallback completion is attributed to the model that actually produced it.

What the launch benchmarks do—and do not—show

Astra leads the composite score, though not every test in it. On the Artificial Analysis Intelligence Index v5.0, as of October 28, 2026, Astra scores 57 at maximum effort against Fable's 49 and leads by 5–8 points at every lower setting. Fable's best score, 50 at xhigh, sits below Astra at medium (51). The tie at 53 that Artificial Analysis showed on October 26 came from its previous v4.3.2 index, and the launch-week figure of Fable 65.7 versus Astra 61.2 from version 4.1; neither can be compared with v5.0 scores. Cost widens the gap: at each effort level, Artificial Analysis's cost per index task for Fable runs 2.0–3.2 times Astra's.

Individual tests split by task type. OpenAI's launch table, run at maximum effort under OpenAI's own setup, has Astra ahead on Terminal-Bench 4.0 (57.9% versus 55.8%), AutomationBench (41.4% versus 31.4%), and DeepSWE v1.1 (74.1% versus 67.4%), and Fable ahead on Humanity's Last Exam with tools (65.0% versus 57.2%). In Artificial Analysis's v5.0 runs at maximum effort, Fable leads only AA-Briefcase (1669 versus 1568 Elo) and AA-Omniscience (46 versus 42). Astra leads the other seven, including Terminal-Bench 4.0 (59.1% versus 52.0%), AutomationBench-AA (68.5% versus 59.4%), FrontierCode 2.0 (42.4% versus 31.6%), and CritPt (70.5% versus 51.0%). GDPval-AA, SciCode, and the long-context AA-LCR, where Fable led under v4.3.2, are no longer part of the index. Anthropic's Fable announcement publishes yet another provider-run setup. These numbers are not one neutral tournament, so don't average unrelated tests into a "best model" score.

Smaller tests point in different directions. Crazyrouter's 29-item test on September 5, through an OpenAI-compatible gateway, had Fable pass 29 and Astra 16, with Astra under-answering three-way ties and accepting a false premise while matching Fable on short rewrites. A 15-use-case comparison posted on X reported better output from Fable at $26.61 and 37 minutes against Astra's $12.14 and 23.5 minutes, and Reddit r/codex users report Fable at max effort using up a whole five-hour window where Astra didn't. These are single reports with their own setups. On OpenRouter's live comparison, Fable's median throughput is 57 tokens per second against Astra's 36, with similar median latency.

Use a benchmark to choose a task for your pilot. Do not use it to skip the pilot.

A pilot that can reject your favorite

Pick three expensive, recurring jobs rather than a bag of trivia: for example, one cross-file repair with tests, one large-document synthesis with citation checks, and one multi-step tool workflow with a recoverable failure. The October 26 test shows why the tasks must be hard. Eight well-specified tasks both models passed told you only that Fable costs more on easy work, so your pilot needs jobs where at least one model sometimes fails.

Before either model starts, lock these conditions:

1. The repository commit or document snapshot and the exact task request.
2. Equivalent tool permissions, network scope, time box, and human-help budget.
3. The reasoning or effort setting for each provider, written down, and a maximum number of attempts.
4. The caching setup: whether Astra keeps default caching or uses explicit mode, and where Fable's cache_control sits.
5. Acceptance checks that can fail: tests, required citations, numerical reconciliation, layout inspection, or a named business outcome.
6. A fresh start for every run. Never give the second model the first model's patch, error discoveries, or reviewer feedback.

Capture accepted-task rate, first-pass acceptance, wall time, tail latency, input, cache-read, cache-write, and output tokens from each API's usage fields, tool failures, refusals or fallback, manual repair minutes, and total spend. Divide each model's input tokens on identical prompts to get your own r, and rerun the two calculations above with it.

Stop when one candidate violates a hard requirement, when the predeclared budget is spent, or when more runs are unlikely to reverse a meaningful gap. Continuing until a preferred model finally wins is not evaluation.

Convert the result into a routing rule

A single default isn't mandatory. Astra may earn the lane for short tasks, output-heavy agent turns, and OpenAI-native tools; Fable may earn the lane for one-off inputs above 272K tokens or for Japanese-heavy reused prefixes, where its token ratio is lowest. Keep both only when the quality gain exceeds the cost and complexity of two integrations.

Check the cheaper model in each family first. If Fable is the candidate, Claude Fable 5.1 vs Opus 5.5: Which Should You Use? covers when Opus 5.5 is enough. If Astra is, GPT-6.1 Sol vs GPT-6 Astra vs GPT-6 Sol: When Astra Is Worth It covers the Sol trade-off, and Claude Sonnet 5.5 vs GPT-6 Astra: When the Cheaper Model Wins compares Astra with a cheaper Claude. For single-model bills, see GPT-6 Astra API Pricing: $10/$50 per 1M Tokens, Caching Costs and Claude Fable 5.1 API Pricing: What Changed and What You'll Pay.

Promote a model after it clears four gates: your account can call it, the data terms permit the task, repeated work meets acceptance criteria, and cost per accepted task fits the budget. Keep the current model or a tested fallback until rollback has been exercised. If you are moving from Fable 5, Claude Fable 5.1 vs Opus 5: Pricing and Cost per Task covers the model-specific API changes, and Fable 5.5 vs Gemini 4 vs GPT-6.1: Fable 5.2 Is Out, 5.5 Isn't separates released models from rumored ones.