GPT-6 Astra vs Claude Fable 5.1: Same Price, Not Same Cost

OpenAI shipped GPT-6 Astra on 3 September 2026. Anthropic shipped Claude Fable 5.1 two days earlier, on 1 September. Both landed at exactly $10 per million input tokens and $50 per million output tokens.

That identical sticker price makes this the cleanest frontier comparison available right now: when the headline number matches, the decision moves entirely to capability, access, and what the bill actually looks like once caching and long context enter the picture. Those last two matter far more than the list price suggests. Everything below is checked against OpenAI's and Anthropic's own developer documentation; where a circulating number could not be verified at a primary source, we say so rather than repeat it.

What are the actual specs of GPT-6 Astra and Claude Fable 5.1?

Both vendors publish full specifications. This is worth stating plainly because a lot of early commentary claimed OpenAI had withheld Astra's context window — it has not. The numbers are in the model reference.

The specs are close enough to be near-interchangeable. Astra's headline context window is 5% larger, but its usable input ceiling is 922,000 tokens — the remainder is budget for reasoning and output. In practice, treat both as million-token models with 128K output.

The one clear spec win is Anthropic's: Fable 5.1's reliable knowledge cutoff is June 2026 against Astra's 30 April 2026. Two months is not decisive, but for anything from May or June 2026 asked without search access, Fable 5.1 is likelier to know.

Do the benchmarks show a clear winner?

This is where most comparisons of these two models go wrong, so it is worth slowing down.

Anthropic published a benchmark table with Fable 5.1's launch. The comparison set in that table is Claude Fable 5, Claude Opus 5, Claude Mythos 5.1 and GPT-5.6 Sol — not GPT-6 Astra. That is not an omission on Anthropic's part; Astra did not exist when the table was published. The practical consequence is that no vendor has published a head-to-head Astra versus Fable 5.1 benchmark. Any table you see presenting one is stitching together numbers from two different launches.

Here are Anthropic's verified figures for Fable 5.1:

Look at the two OSWorld 2.0 rows. Same benchmark, same model, same evaluation run — and a 36-point spread depending on whether you score partially or strictly. Fable 5.1 is either a 77.9% model or a 41.7% model on computer use, and both numbers are honest.

This matters because a widely-shared comparison sets Astra's OSWorld result against Fable 5.1's 41.7% and reports a roughly 31-point gap in OpenAI's favour. That comparison does not survive scrutiny. It pits an OpenAI figure whose scoring convention is not documented in OpenAI's developer materials against Anthropic's strict-scored number, while Anthropic's partial-scored number for the same model on the same benchmark is 77.9% — which would reverse the direction of the gap. Anthropic also attaches an explicit footnote that its OSWorld 2.0 task set differs from earlier releases and is not directly comparable to previous OSWorld 2.0 results.

So: the choice of scoring convention moves the number more than the difference between any two frontier models does. We were unable to verify Astra's OSWorld scoring conditions from a primary OpenAI source, so we are not printing a head-to-head OSWorld figure. If you see one quoted anywhere, ask which scoring mode each side used before you act on it.

What you can take from the verified table is narrower but solid: Fable 5.1 is a large step up from Fable 5 on long-horizon agentic work — Terminal-Bench-Science more than doubles, AutomationBench nearly doubles — and it beats Opus 5 across every row. We covered that generational jump in detail in our Fable 5.1 versus Fable 5 comparison and our Fable 5.1 versus Opus 5 breakdown.

Which model is actually cheaper?

The list prices are identical, so the answer depends entirely on your workload shape. There are three regimes, and they give three different answers.

Regime 1: uncached, short context — a genuine tie

A one-shot request with 100,000 input tokens and 5,000 output tokens:

• Astra: (100,000 × $10 ÷ 1M) + (5,000 × $50 ÷ 1M) = $1.00 + $0.25 = $1.25
• Fable 5.1: (100,000 × $10 ÷ 1M) + (5,000 × $50 ÷ 1M) = $1.00 + $0.25 = $1.25

Exactly the same, to the cent. Batch pricing is also 50% on both sides, so batched work stays tied at $0.625. For stateless classification, summarisation or extraction over short documents, price is not a differentiator. Choose on quality.

Regime 2: cached agent loops — Fable 5.1 is about 33% cheaper

Agent loops re-read a large stable prefix on every turn. That is what prompt caching is for, and it is where the 4× cache-read difference bites. Take a typical coding-agent turn: 100,000 cached prefix tokens, 5,000 fresh input tokens, 3,000 output tokens.

Astra costs 33% more per turn on identical work, purely because of the cache read line. The heavier your cached prefix relative to fresh tokens, the wider that gap gets.

The mechanism is documented on both sides. Anthropic prices cache reads at a multiplier of base input, and Fable 5.1 is the exception in its own lineup: reads cost 0.025× base input where every other Claude model uses 0.1×. That is what produces $0.25 per million against a $10 base. Astra's cached input is listed flat at $1.00 per million — the conventional 10% of its $10 base. Two models, the same headline rate, a 4× difference on the line that dominates agent bills. Anthropic's own framing is consistent: roughly 25% lower cost on typical workloads versus Fable 5, rising to approximately 45% on highly agentic ones, entirely from this change.

Regime 3: long context — Fable 5.1 is roughly 2.3× cheaper

Astra prices prompts above 272,000 input tokens at 2× input and cache rates and 1.5× output. Fable 5.1 has no equivalent tier; Anthropic documents the full 1M window at standard per-token pricing, stating that a 900K-token request bills at the same per-token rate as a 9K-token request.

A long-context research turn — 350,000 cached tokens, 50,000 fresh input, 8,000 output:

Astra costs about 2.3× as much for the same request. Two models with identical published prices, and a bill that differs by more than a factor of two. This is the single most important practical finding in this comparison.

What happens when you cross 272K tokens on GPT-6 Astra?

One detail deserves isolating, because it is easy to miss and expensive to discover in production. OpenAI's pricing note states that prompts over 272K input tokens are priced at the higher rates for the full request — not just for the tokens above the threshold.

That makes it a cliff, not a ramp. Compare two uncached requests with 4,000 output tokens each:

Adding 2,000 tokens — 0.7% more input — increases the Astra bill by 98%. On Fable 5.1 the same change costs an extra two cents. If you build on Astra with variable-length context, you either keep prompts reliably under 272K or accept that a slightly larger document doubles that request's cost, which makes budget forecasting on a straddling workload genuinely hard.

Two caveats cut against both sides of this arithmetic. First, per-token prices are only directly comparable if the two tokenizers produce similar token counts for the same text, and neither vendor publishes a cross-vendor comparison. Anthropic does note that its current tokenizer, introduced with Claude Opus 4.7, produces roughly 30% more tokens for the same text than its own older models. Run your own token counts on representative documents before committing to a cost model.

Second, neither model lets you buy a cheap non-reasoning request. Astra's effort scale runs low, medium, high, xhigh, max — there is no "off". Fable 5.1's adaptive thinking is always on, and thinking: {"type": "disabled"} returns a 400. Both bill the reasoning they do, so neither offers the cheap shallow path that earlier model generations did. Budget accordingly.

Which is better for coding and agentic work?

On verified evidence, Fable 5.1 is the stronger documented agentic model — but the honest framing is that we have Anthropic's numbers and not OpenAI's, so this is an argument from available evidence rather than a settled result.

Fable 5.1 posts 55.8% on Terminal-Bench 4.0 and 73.4% on CursorBench 3.2.0, both ahead of Opus 5 and well ahead of GPT-5.6 Sol. Anthropic positions it for "demanding reasoning and long-horizon agentic work" while still recommending Opus 5 as the default for most workloads — a restrained recommendation for a flagship, and a useful signal that Fable 5.1 is a specialist rather than a general upgrade. OpenAI describes Astra as its most capable model, built for "complex reasoning, coding, computer use, research, and document creation," with five effort levels up to max.

Two behavioural details matter more than the benchmark rows for anyone actually building an agent:

• Fable 5.1 batches tool calls less reliably than Fable 5. Anthropic documents that it may issue one tool call per turn where its predecessor batched several. Answer quality is unaffected, but the extra round trips cost tokens and wall-clock time. A one-line batching instruction in the prompt is the documented fix.
• Fable 5.1 tends to rewrite whole files for small edits. Also documented by Anthropic. The result is usually correct but burns output tokens at $50 per million — the most expensive line on your bill.

Both are prompt-level fixes rather than blockers, but neither is discoverable from a benchmark table. For deeper background on the model itself, see our complete guide to Claude Fable 5.1, and for the previous generation's cross-vendor matchup, our GPT-5.6 versus Claude Fable 5 comparison.

Can you actually use both models today?

No — and this is probably the most decisive practical difference right now.

GPT-6 Astra is, per OpenAI's own model page, "rolling out today for enterprises in our Trusted Access Program, with access through API and our Plus, Pro, Business and Enterprise plans coming in the coming days." If you are not in that programme, you are waiting. As of 3 September 2026, Astra does not appear in the OpenRouter model catalogue, so the usual third-party route is not open either.

Claude Fable 5.1 shipped to everyone on day one as claude-fable-5-1, across the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS. It is live on OpenRouter today at $10/$50, with a batch endpoint at $5/$25.

A model you cannot call is not a model you can evaluate. For most teams in early September 2026 the real comparison is between Fable 5.1 and whatever you run today; Astra becomes an option once general access lands. See our complete guide to GPT-6 Astra and the full rate card in our GPT-6 Astra pricing and API cost breakdown.

Where is each model genuinely worse?

Both carry constraints that will break specific architectures.

GPT-6 Astra's weak points

• Cache reads cost 4× more. $1.00 versus $0.25 per million. This is the dominant cost line in any agent loop.
• The 272K cliff. Full-request repricing, not marginal — a near-doubling of cost for crossing the threshold by a single token.
• Restricted availability. Trusted Access Program only at launch.
• Older knowledge cutoff. 30 April 2026 versus June 2026.
• Usable input is 922K, not 1.05M. The headline context figure includes the reasoning and output budget.

Claude Fable 5.1's weak points

• Forced tool use is gone. tool_choice set to {"type": "any"} or {"type": "tool", "name": "..."} returns a 400 invalid_request_error. If your architecture depends on guaranteeing a tool call, this is a breaking change requiring a rewrite. Anthropic's guidance is to keep tool_choice: {"type": "auto"} with strict tool use, or move the schema to structured outputs.
• Thinking blocks are bound to the model and the conversation prefix. Editing an earlier turn, rebuilding the system prompt, or changing the tools array mid-conversation invalidates every later thinking block and returns a 400. Enforced for accounts created on or after 31 August 2026. Any code that builds its own messages array needs auditing before migration.
• No zero data retention. Fable 5.1 carries 30-day data retention and is not available under ZDR unless expressly authorised by Anthropic. For regulated workloads with a hard no-retention requirement, this alone can rule it out.
• Slowest in its family. Anthropic rates its comparative latency as "Slower," and unlike Opus 5 and Opus 4.8, Fable 5.1 has no fast mode.
• No prefill, no temperature control. Assistant prefill returns a 400, as do non-default temperature, top_p or top_k.

That data-retention constraint is worth flagging loudly. It is the one item on this page most likely to be a hard blocker rather than a cost or convenience issue.

Which one should you choose?

The decision rule is short, because the price tie removes most of the usual complexity:

If your workload caches a large prefix or regularly exceeds 272K tokens, choose Claude Fable 5.1 — it costs 33% to 130% less for identical work. If you need zero data retention, forced tool calls, or you are already inside OpenAI's Trusted Access Program, choose GPT-6 Astra. For short uncached prompts the cost is identical, so pick on your own evals.

When two frontier models arrive within 48 hours at the same list price, the sticker stops being informative. What separates them is cache economics, threshold behaviour, and whether you can get access at all — all knowable in advance. Run the arithmetic against your own token mix before committing, because the gap between these two on a cached long-context workload is wider than the gap between most models a full price tier apart.

If you are building agent infrastructure on either model, Codersera can extend your team with vetted remote developers.

FAQ

Is GPT-6 Astra better than Claude Fable 5.1?

There is no published head-to-head benchmark between them — Anthropic's launch table compares Fable 5.1 against GPT-5.6 Sol, because Astra launched two days later. On verified evidence Fable 5.1 is the stronger documented agentic model, leading Terminal-Bench 4.0 at 55.8% and CursorBench 3.2.0 at 73.4%. Astra may well match or beat it, but OpenAI has not published comparable figures.

Which is cheaper, GPT-6 Astra or Claude Fable 5.1?

Both list at $10 per million input and $50 per million output, and uncached short prompts cost exactly the same. Claude Fable 5.1 is cheaper in practice: cache reads are $0.25 versus Astra's $1.00, and it has no long-context surcharge. On a cached agent turn Fable 5.1 runs about 33% cheaper; on a 400K-token cached request, roughly 2.3× cheaper.

Which is better for coding, GPT-6 Astra or Claude Fable 5.1?

Fable 5.1 has the stronger published coding evidence: 55.8% on Terminal-Bench 4.0 and 73.4% on CursorBench 3.2.0, both ahead of Opus 5 and GPT-5.6 Sol. OpenAI describes Astra as its most capable model for coding but has not published comparable scores. Note that Fable 5.1 tends to rewrite whole files for small edits, which raises output-token cost.

What is GPT-6 Astra's context window?

1,050,000 tokens, with a maximum input of 922,000 tokens and up to 128,000 output tokens. The knowledge cutoff is 30 April 2026. Claude Fable 5.1 offers a 1,000,000-token window as both default and maximum, also with 128,000 max output, and a more recent June 2026 knowledge cutoff.

Which is better for agentic work?

Claude Fable 5.1, on both cost and verified capability. Its cache reads cost a quarter of Astra's, which dominates the bill in any loop that re-reads a stable prefix, and it has no 272K pricing cliff. It also leads every agentic benchmark Anthropic published. The caveat: it no longer supports forced tool use, so architectures depending on guaranteed tool calls need rewriting.

Should I switch from Claude to GPT-6 Astra?

Not on price — the list prices are identical and Fable 5.1 is cheaper once caching or long context is involved. Switch if you need zero data retention, if you depend on forced tool calls that Fable 5.1 now rejects with a 400 error, or if your own evaluations show Astra is materially better on your workload. Most teams cannot yet test that, since Astra is limited to OpenAI's Trusted Access Program.

Does Claude Fable 5.1 charge extra for long context?

No. Anthropic documents the full 1M-token context window at standard per-token pricing, noting that a 900K-token request bills at the same per-token rate as a 9K-token request. Prompt caching and batch discounts also apply at standard rates across the whole window. GPT-6 Astra, by contrast, prices prompts above 272K input tokens at 2× input and 1.5× output for the full request.

Can I use GPT-6 Astra on OpenRouter?

Not as of 3 September 2026 — Astra does not appear in the OpenRouter model catalogue. It is rolling out to enterprises in OpenAI's Trusted Access Program, with API and Plus, Pro, Business and Enterprise plan access described as coming in the following days. Claude Fable 5.1 is available on OpenRouter now at $10/$50, plus a batch endpoint at $5/$25.

Sources verified 3 September 2026: OpenAI GPT-6 Astra model reference, OpenAI API pricing, Anthropic pricing documentation, What's new in Claude Fable 5.1, and Anthropic's Claude Fable and Mythos 5.1 announcement.