GPT-6 Astra vs Claude Fable 5.1

GPT-6 Astra and Claude Fable 5.1 They cost exactly the same amount: ten dollars per million incoming tokens, fifty outgoing tokens. At the top of the market the price stopped being a competitive lever, and the two main suppliers lined up on the same spot.

With list prices out of the equation, the comparison is decided entirely by what they do better. Data verified on September 4, 2026.

Specifications in comparison

Here again the deadline plays in favor of the competing model: June 2026 for Fable 5.1 against late April for Astra. Two months, which do not change a decision but disprove the idea that the model announced later necessarily has the most recent knowledge.

Where Astra is ahead

The areas of strength are consistent with the rest of the ad: terminal work, process automation, CAD Generation and Advanced Mathematics.

On Terminal-Bench 4.0, however, the margin is only 2.1 points — much narrower than the communication suggests. And OpenAI declares that that result comes to about the 63% less estimated cost compared to Fable 5.1, which is the most relevant data in the line.

Where Fable 5.1 is ahead

The first two lines matter a lot. Humanity's Last Exam is a test of difficult knowledge and Fable 5.1 is ahead by almost eight points. The Artificial Analysis Intelligence Index is a third-party measure, not an internal test, and sees Fable 5.1 first by a margin of 4.5 points.

Notes that need to be read

Since these are tables published by one of the two contenders, footnotes count.

• Up BenchCAD Claude scores reflect three changes to the evaluation
• Up ScreenSpot-Pro and ExploitGym the Fable scores quoted come from a variant with fewer safeguards
• Fable 5 and 5.1 are excluded from LifeSciBench, GeneBench Pro and MedChemBench because they reject most questions
• Up OSWorld 2.0 Claude scores use official settings, not Fable 5.1 system card settings
• All scores are the maximum at any level of reasoning

Exclusion from the three scientific tests deserves clarification: It's not a defeat. It is a choice of behavior of the model, which rejects those questions. For a company it can be a value or a problem depending on the domain in which it works, but presenting it as a low score would be incorrect.

Practical examples

Laboratory automating terminal analysis. Terminal-Bench Science gives twelve points of detachment in favor of Astra, with lower estimated cost. It is the least questionable area.

Study answering complex specialist questions. Humanity's Last Exam suggests Fable 5.1, ahead of almost eight points. If your job is to answer difficult questions of domination, the verdict overturns.

Pharmaceutical or biomedical company. Be wary of behavior: The notes point out that Fable models reject most questions on three scientific assessments. It must be verified on your domain before choosing, because a waste in production is a flow that stops.

Mistakes to Avoid

• Treating OpenAI tables as neutral on Claude.
• Reading exclusion from a test as a defeat.
• Ignoring notes on modifications and variants used.
• Searching for a unique winner where there are two different profiles.
• Forgetting that the price is identical: only decides surrender.
• Not checking refusals on their own domain.

How to apply it in the company

With the identical price list, the decision boils down to two numbers that only you can measure.

The first is how many attempts are needed to close a task. At the same rate, one model that closes on the first shot where the other requires two costs half. It is the only economic lever left and is invisible to published comparisons.

The second is How They Mistaken. A model that declares when information is missing produces a manageable queue; one that invents a plausible value produces errors that nobody will check. This can only be observed by examining the difficult cases one by one.

Then prepare ten real cases with three difficult ones and one that lacks the required data, and perform everything on both with the same prompt. Add at least one case to your domain boundary, to see where the behavioral boundaries fall.

The stated cost, which is the real news

At the same price list, the most relevant statement of the ad is not a score but an estimate of expenditure. OpenAI declares that Astra achieves its results by consuming less.

The line on Terminal-Bench 4.0 is the most instructive: two points ahead in scoring, but the 63% less estimated cost. If it was confirmed on its own case, it would be more important than the score itself — because at the same price list the cost per task depends entirely on how many tokens it takes to get there.

It must be clearly stated that it is manufacturer estimates on individual benchmarks, not of independent measurements nor of a general rule. But they are verifiable: fifty real tasks on both models are enough, counting the tokens actually invoiced.

Speed: a factor that shifts the choice

There is a difference between the two patterns that score tables do not capture.

The Anthropic documentation places Fable 5.1 at the slow end of its family. OpenAI, for its part, offers for Astra a Fast mode which promises up to twice the speed at twice the price, and declares task completion 1.9 times faster with the updated Codex scaffolding.

In agent scenarios the latency is multiplied by the number of steps: a twelve-step stream pays the response time twelve times. If your agent works while someone is watching, this difference weighs as much as a benchmark point — the reasoning is in AI models latency by comparison, while the comparison with the other flagship Anthropic model is in GPT-6 Astra vs Claude Opus 5.

If, on the other hand, it works at night, the latency comes out of the decision and it is better to look only at quality and cost per task.

Two philosophies on the boundaries of behavior

One last item that in production counts more than you think.

The announcement notes signal that the Fable models reject most questions on three scientific evaluations, and that some of the cited scores come from a variant with less protection. On the other hand, Astra reaches the Critical threshold on cybersecurity and consequently rejects advanced cyber tasks, with controls that can also stop legitimate work — in API the task stops.

Both models therefore have boundaries, but they fall at different points. For a real project the question is not which is more permissive, but where the boundaries with respect to your domain fall.

That's why it's worth including at least a couple of cases on the test stand at the edge of your industry: not to force anything, but to find out at the evaluation stage —and not in production.where the model stops.

The case where choice doesn't matter

Let's close with the observation that makes half of this comparison unnecessary for most readers.

Both of these models cost fifty dollars per million tokens out. On a system that processes twenty thousand requests per month with responses from a thousand tokens, it is a thousand dollars per month of output alone — before the system prompt, reasoning tokens and failed attempts.

To classify messages, extract fields from invoices, or respond from a document base, Neither is necessary.: there is an intermediate band at one fifth of the price that closes the same tasks, and an economic one at one fiftieth for works of pure recognition.

The comparison between Astra and Fable 5.1 becomes relevant when the task is genuinely difficult: autonomous agents on long chains, analysis that cross many sources, specialized technical work where an error costs more than the call. Out of there, the correct choice is lower — and it occurs in an hour with ten real cases.

A final verification before deciding

If after the test the two models are equivalent on your cases — and it is a frequent outcome on this range — the decision shifts to operational factors that it is legitimate to consider: which ecosystem already knows your team, what tools exist around, where you know how to solve a problem when it occurs at six o'clock in the evening.

It's not a fallback choice. At the same measured result, the team's familiarity with the platform is worth more than a few decimal places on a benchmark, because it results in faster intervention times when something doesn't work.

Three readings to complete the picture. The complete picture of the model is in GPT-6 Astra: what it is and what changes. For the economic part, which on this band decides more than capacity, the reference is GPT-6 Astra: prices and how to pay. And to see if your task really justifies it, the verification is in GPT-6 Astra: all official benchmarks.

Conclusion

Astra and Fable 5.1 cost identical and offer two different profiles: Astra stronger in performing technical tasks, Fable 5.1 stronger on difficult knowledge and third-party aggregate measures.

With the price out of the equation, the choice is made as to how many attempts are needed and how the two go wrong —two things you don't find in any table and measure in half a day.

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