GPT-6 Astra vs Claude Fable 5.1: Same $10/$50, 61 vs 66

GPT-6 Astra and Claude Fable 5.1 cost exactly the same per token: $10.00 in, $50.00 out. On the Artificial Analysis Intelligence Index, Fable 5.1 scores 66 and Astra scores 61. OpenAI shipped Astra on 3 September 2026 with a set of strong agentic benchmark numbers. The third-party composite tells a different story, and both are worth reading before a migration.

TL;DR

• Identical sticker price. $10 / $50 per million on both. There is no per-token saving in either direction.
• Cache reads are not identical. Fable 5.1 reads cache at $0.25 per million on Ofox; OpenAI lists Astra cached input at $1.00. Four times the rate on the part of an agent loop that repeats most.
• Fable 5.1 leads the composite by five points, 66 to 61 at max effort. Astra ranks #8 of 202 on that index.
• Astra leads the benchmarks OpenAI chose, which are agentic: computer use, exploit discovery, frontier math.
• Astra’s max tier costs about double its high tier for one point. 61 against 60, $3,013 against $1,429 to run the same index.

The Prices Are the Same. Only One of Them Is Cheap to Cache.

Astra’s standard and Fast-mode rates are OpenAI’s published launch pricing; the 1.1M context, 128K output ceiling and April 2026 knowledge cutoff are from its model specification. Fable 5.1’s rates are the live Ofox catalog, where anthropic/claude-fable-5.1 reads cached input at $0.25 per million; Astra’s Ofox rates match OpenAI’s list, with cached input at $1.00. Both re-read 5 September 2026.

The cache row is the one that moves a real bill. Agent loops resend the same system prompt, tool definitions and conversation history on every turn, so cached input is usually the largest single line on a long-running task. At $1.00 against $0.25, the same cached context costs four times as much on Astra. That is invisible in a headline-rate comparison and very visible in a monthly invoice.

What the Third-Party Index Says

Artificial Analysis scores GPT-6 Astra at 61 and Claude Fable 5.1 at 66. AA’s Intelligence Index is a composite of nine evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity’s Last Exam, GPQA Diamond, CritPt, AA-Omniscience and AA-LCR. Here is where Astra lands:

Read from Artificial Analysis on 4 September 2026. Astra is #8 of 202 models, which is a strong placement in absolute terms and a specific one in context: every model above it is a Claude or Muse Spark variant.

Two comparisons in that table matter more than the ranking.

At the same score, Astra is not the cheap option. Three configurations sit at 61: Astra max at $1.67 per task, Claude Opus 5 high at $1.23, and Muse Spark 1.3 xhigh at $0.55. Astra costs three times the cheapest way to reach the same measured score.

At the same price, Astra is not the strong option. Fable 5.1 charges the same $10 / $50 and scores five points higher.

What OpenAI Published, and Why It Is Not the Same Claim

OpenAI’s launch numbers are real and mostly agentic:

These are vendor-reported, on benchmarks the vendor selected, some with a qualifier attached: ARC-AGI-3 at 99.9% is “adapter harness”, and Humanity’s Last Exam at 57.2% is “with tools”. Neither qualifier is a flaw, but both change what the number means, and neither is directly comparable to a third-party run of the same evaluation.

The ARC-AGI-3 qualifier turns out to matter more than most: ARC Prize’s own standard harness put the same model at 62.7%, a 37-point spread on one benchmark. Our review walks through why both numbers are legitimate and what the gap actually measures.

The honest reading is that these measure different things. AA’s index weights general reasoning, long-context retrieval and knowledge. OpenAI’s selection weights computer use, terminal work, security tooling and math. A model can lead one and trail the other without either being wrong, and which one predicts your results depends on what you actually run.

Astra’s Own Tiers Disagree With Each Other

One point of index score costs roughly double.

Max writes 2.6x the output tokens of high and costs 2.1x as much across the index, for a single point. That is the same pattern Gemini 3.8 Flash showed against 3.7 Flash, where the newer model spent 30% more output tokens at the same rate: the effort setting is a price multiplier, and the top setting is rarely the value setting.

Before defaulting a production workload to max, run both on your own tasks and check whether the point exists there. On AA’s mix it is one point in 61; on a narrower workload it may be zero.

When to Pick GPT-6 Astra

• Computer use and terminal automation. OSWorld 2.0 at 72.6% and Terminal-Bench 4.0 at 57.9% are the numbers OpenAI led with, and this is the workload class the model was positioned for.
• Security and exploit work. ExploitBench at 100% and SRE-Bench at 88% on one attempt are specific, and there is no comparable third-party composite that covers this.
• You need Fast mode. At $20 / $100 for up to 2.5x standard speed, it is a latency lever Fable 5.1 does not offer at any price.
• You are already on Bedrock. Astra is supported there from launch.

When to Stay on Claude Fable 5.1

• General reasoning at the same price. Five index points for the same $10 / $50 is the clearest argument in this comparison.
• Cache-heavy agent loops. $0.25 against $1.00 per million cached input tokens, on the line item that grows fastest in a long-running agent.
• Cache-heavy loops. anthropic/claude-fable-5.1 and openai/gpt-6-astra are both in the Ofox catalog as of 5 September 2026, so this is now a one-string A/B rather than a wait. At $0.25 against $1.00 on cache reads, Fable is the cheaper of the two to replay a long prefix against.

If Budget Is the Constraint, Neither Is the Answer

Both of these are $50-per-million-output models. If the task does not need a 61 or a 66, the same index has cheaper ways to get close: Claude Opus 5 at $5 / $25 scores 63 at max, which is two points above Astra at half the per-token price. Opus 5 against GPT-5.6 Sol covers that tier, and the GPT-5.6 tier guide covers OpenAI’s previous generation, which is still $5 / $30 rather than $10 / $50.

That is worth stating plainly: GPT-6 Astra doubled OpenAI’s own flagship input price and raised output by two thirds, from GPT-5.6 Sol’s $5 / $30. The upgrade decision from inside the OpenAI lineup is a different question from this one, and it has its own answer. The full Astra rate card covers Fast mode, the cache tier and what a task costs at each effort level.

Calling Fable 5.1 Today

from openai import OpenAI

client = OpenAI(base_url="https://api.ofox.io/v1", api_key="YOUR_OFOX_API_KEY")

r = client.chat.completions.create(
    model="anthropic/claude-fable-5.1",
    messages=[{"role": "user", "content": "Refactor this function to remove the nested loop."}],
)
print(r.usage)

When GPT-6 Astra is listed, the comparison is a one-string change on the same endpoint. Until then the live catalog at GET https://api.ofox.io/v1/models is the authority on what is callable.

Sources

• https://artificialanalysis.ai/models/gpt-6-astra
• https://artificialanalysis.ai/models/gpt-6-astra-high
• https://llm-stats.com/models/gpt-6-astra
• https://ofox.io/models/anthropic/claude-fable-5.1

Artificial Analysis index scores, per-task costs and index run costs were read on 4 September 2026. GPT-6 Astra’s benchmark table is OpenAI’s launch figures as reported on 3 September 2026. Ofox rates for both models were read from the live /v1/models endpoint on 5 September 2026, the day GPT-6 Astra was listed.