There is no task-independent winner between GPT-6 Astra and Claude Fable 5.1. Both list a base API price of $10 per million input tokens and $50 per million output tokens, plus roughly one million tokens of context and 128,000 maximum output tokens. The important differences appear after the headline: Astra raises rates for an entire request above 272,000 input tokens, while Fable 5.1 offers $0.25-per-million cache reads and does not list a separate long-context multiplier.
The execution layer matters just as much. GPT-6 Astra has a native path through Codex, while Claude Fable 5.1 has a native path through Claude Code. A model result measured inside one Agent harness does not automatically transfer to another harness with different tools, permissions, prompts, context management, and retry behavior.
This comparison is based on official documentation and vendor-published evaluations checked on September 7, 2026. It is not an Agent.Space benchmark, and it does not claim that either model is available in Agent.Space. For a standalone Astra overview, start with the GPT-6 Astra release and coding-agent guide.
Specifications and availability
The OpenAI model page and Anthropic model page establish these contracts. They do not guarantee that your account, cloud marketplace, region, or product surface exposes every option today.
OpenAI's launch notice and ChatGPT release notes still described Astra as a staged rollout when this comparison was prepared. Anthropic lists Fable 5.1 as its active latest model and makes it available through the Claude API and several cloud platforms. In either case, verify the exact route before designing a production workflow around it.
Price and context: the headline rates tie, the request shapes do not
For a short, uncached request, the two published token prices can produce the same arithmetic. A request with 200,000 input tokens and 20,000 output tokens costs $3.00 before tools or other charges on either model:
At 300,000 input tokens, Astra's long-context rule changes the comparison. The entire Astra request—not only the portion above 272K—is charged at 2× input and 1.5× output rates. With 20,000 output tokens, the normalized example becomes:
That does not prove Fable is cheaper for the completed task. Astra could need fewer turns, produce less output, or avoid a failed repair. It does show why “both cost $10/$50” is not enough for a budget decision.
Caching creates another difference. Suppose a 400,000-token stable prefix is written once and successfully read nine more times. Ignore output, changing input, tools, and misses so the cache contract stays visible:
This is a pricing illustration, not a workload quote. A real coding agent adds new tokens each turn, may rewrite or miss a cache, uses separately billed tools, and can spend very different amounts of output. The providers also use different tokenizers, so the same repository text should not be assumed to create the same billable token count. Use the usage records from the exact routes you test.
OpenAI also lists Batch and Flex at 50% of Standard rates and Fast at 2× the applicable Astra rates. Anthropic lists a 50% Batch API discount for Fable 5.1. Those modes have different latency and execution contracts; do not compare their prices as though they were interchangeable interactive sessions.
For a deeper breakdown of Fable's cache economics, see the Claude Fable 5.1 pricing and coding-agent guide.
What the published benchmarks do—and do not—prove
OpenAI's GPT-6 Astra launch report publishes direct comparisons with Claude Fable 5.1. Two coding-relevant results are especially visible:
OpenAI also estimates lower API cost per task for Astra in the shown configurations: about 63% lower on Terminal-Bench 4.0 and 31% lower on Terminal-Bench Science 0.1. Those are useful data points, but they remain vendor-reported OpenAI results. OpenAI states that reported scores use the maximum at any effort and that its research environment or API can differ from production ChatGPT because system prompts and tools differ.
Anthropic's Fable 5.1 benchmark notes reveal why method details matter. Anthropic reports a standard error of roughly ±3.5–4.5 points per model for Terminal-Bench Science 0.1, distinguishes its setup from the public leaderboard's Claude Code harness, and explains when production safeguards changed scores. It also warns that different task releases can make similarly named benchmark results non-comparable.
Read a public benchmark through at least six fields:
- the exact task version and subset;
- the harness, tools, and network access;
- the system and developer instructions;
- the effort or inference setting chosen for each model;
- the retry count, stopping rule, and safety behavior; and
- whether cost means one request, one attempt, or one accepted task.
The detailed GPT-6 Astra benchmark evidence audit separates vendor, independent, and internal results instead of flattening them into one leaderboard.
Codex versus Claude Code is a harness decision
GPT-6 Astra and Claude Fable 5.1 are models. Codex and Claude Code are Agent harnesses: they decide how to search a repository, call tools, edit files, enforce permissions, preserve context, recover from failures, and present evidence. The distinction is explained in Agent Harness vs Model.
For Astra, use Codex CLI 0.153.0 or newer as the minimum compatibility line. The official Codex release history shows that 0.153.1 added API configuration for Astra without initially showing it in the picker; 0.153.3 added supported Amazon Bedrock catalog routes; and 0.153.4 fixed bundled picker visibility and made Astra the bundled default when no model is configured. Client support still does not grant account access.
For Fable 5.1, use a current Claude Code release rather than assuming an older binary will expose the model's full context or caching behavior. The Claude Code release history records Fable picker, 1M-context, prompt-cache, and effort fixes around v2.1.260–v2.1.261. Account entitlement, usage credits, provider route, and organization policy remain separate checks.
There are two valid comparisons, and they answer different questions:
- Model comparison: run both APIs through the same small, neutral harness with equivalent tools and acceptance checks. This reduces harness confounding but does not reproduce the best native product experience.
- Workflow comparison: run Astra in Codex and Fable 5.1 in Claude Code. This tests the product you would actually use, but the result belongs to the whole model-plus-harness route—not to the model alone.
Do not take a result from the second experiment and label it as pure model intelligence.
Choose which model to test by workload
Provider and harness compatibility should come before benchmark preference. A theoretical winner is not a usable route if the selected product cannot expose the model, tools, modalities, or accounting data you need.
A reproducible evaluation protocol
Use at least three tasks rather than one impressive demo:
- a bounded change with deterministic tests;
- a normal multi-file feature or review; and
- a failure-prone investigation that requires tool recovery and preserves constraints over time.
For the neutral model comparison, keep the starting commit, task prompt, tool schema, permissions, effort policy, time limit, and acceptance checks identical. Allow the same retry budget. Record input, cache-write, cache-read, output, tool fees, elapsed time, failed attempts, tests passed, review corrections, and final acceptance.
Then run the end-to-end workflow comparison in Codex and Claude Code. Preserve the business task and acceptance criteria, but let each harness use its native context and tool flow. Label that result by the full route, for example “Codex + GPT-6 Astra,” rather than by the model alone.
The primary metric should be cost per accepted result. A cheaper attempt that needs three repairs may cost more than a premium attempt that passes. A higher benchmark score that expands scope or ignores a protected constraint is not a successful task.
Define the decision before running the test: maximum acceptable cost, latency, reviewer minutes, failure rate, and safety boundary. Otherwise it is easy to select whichever result looks most fluent.
How Agent.Space fits
Agent.Space is independent from OpenAI and Anthropic. This article does not claim that GPT-6 Astra or Claude Fable 5.1 is currently available through a particular Agent.Space harness. Official API availability and native client support do not establish third-party compatibility.
Use the live Agent.Space model selector as the source of truth. Choose the harness first, confirm the model and price shown for your account, and run one bounded task with an acceptance check before moving a real workload.
Start an Agent.Space session and compare an available route on work you can actually verify.
FAQ
Is GPT-6 Astra better than Claude Fable 5.1 for coding?
Not for every task. OpenAI reports higher results for Astra on Terminal-Bench 4.0 and Terminal-Bench Science 0.1 in the configurations it published. Those are useful vendor results, not a guarantee for your repository, tools, or harness. Run a controlled model test and an end-to-end workflow test before setting a default.
Which model is cheaper?
Their base uncached API rates are both $10 per million input tokens and $50 per million output tokens. Fable 5.1 has cheaper cache reads and no separately listed long-context multiplier. Astra applies higher rates to the full request above 272K input tokens, but it may still cost less per accepted task if it completes the work in fewer turns.
Which model has the larger context window?
GPT-6 Astra lists 1,050,000 tokens; Claude Fable 5.1 lists 1,000,000. The 50,000-token capacity difference is usually less important than retrieval quality, cache reuse, the actual token count, and Astra's 272K pricing boundary.
Should I compare Codex with Claude Code or Astra with Fable 5.1?
Do both, but label them correctly. A neutral API harness helps compare the models. Codex plus Astra versus Claude Code plus Fable compares complete workflows. The second result includes differences in tools, prompts, permissions, and context management.
Can I use both models in Agent.Space?
This article does not establish support for either route. Check the live model selector for the Agent and model combinations available to your account, then test one small task before relying on that combination.