GPT-6 Astra vs Claude Fable 5.1: Cost per Successful Agent Task

Cost per Successful Task Worksheet

Replace the illustrative token shape with your own traces in the AI API Cost Calculator. Keep success-rate and retry assumptions separate so you can see which variable actually changes the decision.

Why Another General Model Comparison Would Add Little

The broad search result is already crowded with benchmark tables, launch summaries, and the same observation: the models share headline standard prices but differ on cached input and long-context economics. Repeating those tables would create little information gain and would overlap AICostBudget's existing model-comparison coverage. The unanswered buyer question is narrower: at what measured success rate does one model overcome the other's attempt cost for a specific agent workflow?

That question has a falsifiable answer. It does not require an invented universal token multiplier or a cherry-picked benchmark. It requires one representative task set, one harness, a complete cost trace, and an acceptance rubric. This article therefore treats the price card as an input to a break-even test rather than as the verdict.

The Current Price Facts That Matter

The live table above is the pricing source for this page. It reads the site's verified canonical records rather than duplicating a second set of article constants. At the current standard tier, both models list the same new-input and output rates. Claude Fable 5.1 lists a lower cached-input read rate. GPT-6 Astra applies higher input, cache, and output rates to the full request when prompt input exceeds 272,000 tokens.

Verify the vendor terms in the official GPT-6 Astra model page and Anthropic's official API pricing. For the field-level website record, verification date, and source references, use the AI API Pricing Dataset.

Do not silently treat cache writes as reads. OpenAI documents separate uncached input, cached input, and cache-write token categories for supported models. Anthropic separately prices five-minute writes, one-hour writes, and cache hits. A calculator that applies the read discount to every prompt token will understate the bill.

The Break-Even Formula

cost_per_success = total_task_cost / accepted_tasks

astra_break_even_success_rate
  = fable_success_rate * (astra_attempt_cost / fable_attempt_cost)

Total task cost means the entire chain under one parent task ID: initial request, later tool-loop calls, prompt cache writes and reads, reasoning and visible output, failed attempts, fallbacks, provider-priced tools, and any model call used to judge or repair the result. Accepted tasks means outputs that pass the same pre-declared tests or review rubric. Do not use requests completed, HTTP 200 responses, or cache hits as the denominator.

Multiply Fable's measured success rate by the worksheet's Astra-to-Fable attempt-cost ratio. That is the Astra success rate needed to tie on API cost for the fixed token shape. Because success cannot exceed 100%, divide 100% by that ratio to find the highest Fable success rate at which reliability improvement alone could still make Astra break even. The worksheet reports this feasibility ceiling explicitly. Above it, Astra must also consume fewer tokens, avoid retries or tool loops, stay below its long-context pricing threshold, or deliver another measurable benefit.

This is why a universal winner would be false precision. The ratio changes when your cache share, output length, prompt size, effort level, retry policy, and acceptance rate change. A break-even equation makes the uncertainty inspectable instead of hiding it inside a recommendation.

Use the calculator to check the break-even success rate for your own model pair before treating a lower attempt price as a production saving.

What to Capture From Every Agent Run

Aggregate the fields twice: once by provider request for invoice reconciliation, and once by parent task for unit economics. The second view is what tells you whether a cheap first attempt merely moved cost into a fallback. It also exposes workflows where a small number of pathological tasks consume most of the budget.

When extra paid attempts or a fallback route are material, estimate retry and fallback overhead separately before comparing cost per successful task.

For a repeated business process, model the same fields at monthly volume in the AI Workflow Cost Calculator. Keep the worksheet assumptions labeled as estimates until production traces replace them.

Cache Economics: Rate, Hit Share, and Time

The cached-input rate is not the same as the effective cache saving. Effective saving depends on how much of the prompt is actually eligible, how often the stable prefix is reused, how many writes are required, and whether the next call arrives before expiration. Prompt order matters: frequently changing content placed before stable instructions can shorten the reusable prefix.

OpenAI's current prompt-caching documentation describes a 30-minute default TTL for supported GPT-5.6-and-later cache writes and reports cached, cache-write, and total input token details. Anthropic documents a five-minute default write and an optional one-hour write at a higher rate. Those are product rules, not a reason to assume your application achieves a particular hit percentage.

Read the current implementation details in OpenAI's prompt caching guide and Anthropic's prompt caching documentation. Then inspect your own inter-call gaps and cache usage fields.

Community reports show why the distinction matters. A recent Reddit post described a 97.5% cache-hit rate that still obscured major output-cost changes, and commenters recalculated the claimed unit economics using the delivered-item denominator. A GitHub issue from a tool-heavy agent project reported uncached tool schemas dominating repeated input. These are useful failure modes to test, not representative market averages.

Examples: the Reddit cache-hit cost discussion and the Hermes agent caching issue.

The 272K Long-Context Cliff

GPT-6 Astra's context window is large, but the billing step arrives much earlier than the maximum window. OpenAI states that a prompt above 272,000 input tokens receives twice the input and cache rates and 1.5 times the output rate for the entire request. This is not a marginal surcharge applied only to tokens beyond the boundary.

The worksheet makes that discontinuity visible by comparing one 200K prompt with one 300K prompt under an otherwise controlled shape. Both examples are illustrative. They do not predict the tokenization of your repository, the amount of context either model needs, or the probability that a longer prompt improves the answer.

• Log total prompt tokens before model selection, including cached prompt tokens.
• Create an alert band below the threshold so tokenization variance does not surprise the router.
• Test retrieval, context editing, or compaction against the same acceptance rubric.
• Do not trim context solely to save tokens if the change increases retries or repair work.
• Recalculate the whole request with the selected tier; do not price only the excess tokens at the higher rate.

Reasoning Tokens and Tool Loops

For both models, reasoning can create billed output that is not identical to the text shown to the user. OpenAI's response limit includes visible output and reasoning tokens. Anthropic states that thinking tokens are billed as output and that prior thinking blocks retained in context can be billed as input. A comparison based only on visible answer length will therefore miss part of the bill.

Anthropic documents the inclusive billed output total in its thinking cost guide. OpenAI's Astra documentation lists the supported reasoning effort levels; verify usage semantics in the response payload used by your SDK version.

Tool loops add another multiplier. Each call can produce output arguments, return new uncached context, and trigger more reasoning. Fix a maximum loop count and classify early exits. If one model finishes in fewer turns, that token-efficiency advantage belongs in the workload trace. If it stops early but fails acceptance, it does not.

Which Model Wins for Different Workload Shapes

The word starts is deliberate. A rate-card advantage is not a production verdict. The model with the lower attempt price can lose if it consumes more tokens or succeeds less often. The model with the higher attempt price can lose even with a small quality lead if the task has a cheap deterministic validator and easy retry.

Use the live OpenAI API pricing page and Anthropic API pricing page to audit current model records before you rerun the evaluation.

A Decision-Grade Seven-Step Test

• Sample real tasks by value, difficulty, context size, and expected tool count. Do not benchmark only the easy majority.
• Freeze prompts, tools, permissions, effort policy, timeouts, and acceptance criteria for the first controlled pass.
• Run both models on the same tasks and retain raw provider usage fields plus request-level price selection.
• Group all attempts, retries, and fallbacks under the parent task, then reconcile totals with provider billing.
• Calculate cost per attempt, cost per accepted task, latency per accepted task, and human repair minutes.
• Segment results by prompt size and cache-read share, especially around Astra's 272K boundary.
• Choose a default and an escalation rule, then repeat on fresh tasks before increasing traffic.

Do not optimize the router against the same evaluation set indefinitely. Hold back a fresh validation set and watch production drift. Model behavior, prices, SDK fields, and cache rules can change. Store the model ID, pricing verification date, and policy version with each result so a future comparison remains auditable.

Limitations and Source Policy

The worksheet is an illustrative pricing calculation, not observed production traffic and not a benchmark. Equal token shapes are intentionally artificial. The page does not claim an average success rate, retry rate, cache-hit rate, reasoning-token count, or human repair cost for either model. Those values require your application data.

Price facts come from current official provider documentation and the site's verified canonical pricing records. Community posts and issue reports are cited only as examples of operational pain. They are self-reported, may use different harnesses or incentives, and should not be generalized. Your provider invoice is the final billing source.

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