Astra and Fable 5.1: Same Token Rates, Different Workloads
MODELS & COSTS
Astra and Fable 5.1: Same Token Rates, Different Workloads
The same $10/$50 rate can produce different bills when models use different amounts of cached input and reasoning output. Astra and Fable 5.1 also lead different benchmark rows. This comparison is most useful when those workload differences stay visible rather than becoming an overall winner.
In this article
Astra leads more of the shared vendor rows, while Fable leads the cited broad-reasoning and independent index results. Cache rates and long-context thresholds then change the price comparison. A single winner would conceal those workload-dependent differences.
Astra’s stronger shared rows
Start with the rows both vendors published or agree on, because those are the only ones that count as a fight rather than a press release.
Astra's genuine territory is wider than this chart. FrontierMath Tier 4 at 97.6% against 87.8% is a real math gap, though it carries the Epoch funding caveat I flagged in the Astra post: OpenAI funded the benchmark's development and holds exclusive access to part of it, so keep that in mind while reading the number. Computer use is Astra's dimension almost by default: 72.6% on the OSWorld offline set with no Fable 5.1 number published against it, and 92.7% ScreenSpot-Pro grounding. Anthropic's own 77.9% partial comes from different grading and cannot be set beside it, which I say explicitly so nobody builds a chart out of it. And cyber is not a contest: 100% ExploitBench aggregate coverage plus 86 of 226 FrontierCyber challenges solved independently, against Fable 5.1's deliberate find-but-don't-exploit posture. Different philosophies, and Astra's ships behind Daybreak gates for exactly that reason.
Fable’s reasoning and index results
Fable's side of the ledger is narrower in row count and heavier in independence, which is the more interesting combination.
Humanity's Last Exam with tools is the cleanest Fable win in the whole matchup: 65.0% against 57.2%, roughly eight points, and it survives every sourcing variant (OpenAI's own table had it 63.8%, DataCamp 65.0%; either way Fable clears Astra). Artificial Analysis then backs the direction independently: Fable 5.1 at 66 on the Intelligence Index, the highest score it has ever measured, against Astra's 61. One honest footnote rides along: Anthropic's default server-side fallback routed roughly 4% of output tokens through Opus 4.8 or Opus 5, so that 66 is Fable 5.1 as you actually call it, not the raw model in a vacuum. And on the coding-agent side, Fable 5.1 in Claude Code at 70 is the highest Coding Agent Index score on record, against Astra's 67 in Codex, with the harness caveat doing real work: different scaffolding owns part of that 3-point gap, and Astra gets there spending far fewer tokens.
Cache rates and long-context charges
This is the section that surprised me most while assembling the numbers, and it is why the title leads with price instead of benchmarks.
Read the left panel first. Input, output, cache writes, and batch discounts match to the cent; the only rate-card daylight is cache reads, $1.00 against $0.25, and the long-context surcharge above 272K input tokens where Astra doubles to $20/$2/$75 for the whole request while Anthropic charges standard rates across the full 1M window. On identical tokens that means small requests tie exactly ($22.50 both on the balanced workload), big requests punish Astra (+83% on over-threshold retrieval), and cache-heavy loops favor Fable by $75 on the modeled shape. That threshold detail deserves emphasis: 272K is roughly a quarter of Astra's own 1.05M window, so anything using more than a quarter of the context it ships with bills at the higher tier.
Then read the right panel, because it flips the story. Measured per Intelligence Index task, Astra costs $1.67 against Fable's $3.76 at max effort: 44%, less than half, at the same list price. The mechanism is token frugality. Astra spends far fewer tokens per solved task (a third of Sol's in Codex at max effort, a fifth of Opus 5's at xhigh), while Fable 5.1 runs ~1.7x the output tokens of its predecessor, which is why it costs 20% more per task than Fable 5 despite the cache cut doing real work (without the cut it would be roughly $5.16). OpenAI's own task-cost captions point the same way, for what a vendor self-report is worth: ~31% below on Terminal-Bench Science, ~63% on Terminal-Bench 4.0, ~86% on BenchCAD, at effort settings OpenAI chose. So the rate card favors Fable wherever cache or context dominates, and the meter favors Astra wherever output volume dominates. Which half matters is a property of your workload, not of the models.
What the tables cannot isolate
Three things that changed my own mental ranking while writing this. First, the harness asterisk applies to both champions, not just Astra's ARC number. The 62.7%-vs-99.9% ARC Provider Adapter swing is the famous one, but the AA Coding Agent gap (70 vs 67) also mixes Codex against Claude Code scaffolding, and OpenAI admits Astra reasons in fewer, more compressed steps that are harder to monitor. System-inclusive scoring is the water both fish swim in now. Second, availability is asymmetric: Fable 5.1 was GA on day one with an effort dial (low/medium matching Fable 5 quality cheaper) while Astra rolled out staggered behind Daybreak gates with enterprise admins having to opt in. If you need the model this week rather than eventually, that is not a footnote. Third, migration has teeth on the Anthropic side: forced tool_choice now 400s, thinking blocks unreadable to older models, append-only history. Budget a migration afternoon, not just a string swap.
Choosing by workload
I'll say it plainly: on the numbers in this post, Astra looks like the stronger model right now. Seven of eight shared rows, the computer-use dimension largely to itself, the cyber bracket, and less than half the measured cost per solved task. Fable 5.1's wins are real, HLE by eight points and both independent crowns, but they read as the narrower specialist case next to Astra's broader sheet. That is a decision on points, not a knockout, and a matched-harness replication round could still move it.
The default pick is Astra. Agents that click through real software, math and science work, CAD and artifact output, defensive-security tooling behind Daybreak, plus the lowest measured dollars per solved task. Carrying the caveats along: harder-to-monitor reasoning by OpenAI's own admission, the 272K surcharge biting giant requests, staggered availability.
Fable 5.1 is the pick in three shapes. Long cache-dominated loops where quarter-price cache reads decide the bill, giant retrieval requests that live above 272K at standard rates, or Claude Code shops where Fable 5.1 holds the top native-harness score and GA with cost-first effort levels. Its caveats travel with it: 2.25x the measured per-task cost where output dominates, migration breaking changes, fallback-tinted index score at the edges.
Everyone else checks the meter first. If a cheaper model already clears your bar, both launches agree you should stay put: Astra's own HLE loss and Fable's own output-volume bill say the frontier premium needs a workload reason. Run your traffic shape through both panels of the price chart before committing; identical $10/$50 stickers do not survive contact with measurement, in either direction.
Use the matching benchmark for the job and model the actual token shape. The mixed-harness coding comparison—70 versus 67—cannot isolate the model from Claude Code or Codex. This article’s dated results support a set of workload choices, not an overall points victory.
Sources: OpenAI Astra launch + system card + API rate card; Anthropic Fable 5.1 announcement + platform docs; ARC Prize verified Astra runs; Artificial Analysis Intelligence/Coding Agent indices and per-task costs; DataCamp comparison (rate-card arithmetic, workload shapes, hands-on test); The New Stack (launch tables, settings notes). Earlier on this blog: GPT-6 Astra launch post · Claude Fable 5.1 post · $10 coding subscriptions. Images: OpenAI via The New Stack; Anthropic; charts self-rendered (2 new + 3 reused). Prices checked Sept 5, 2026.
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