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Greetings from above,
Two labs. Two flagship releases. Two sets of benchmark charts, both claiming the crown. So let's actually answer the question everyone's asking instead of dancing around it. Who's better, GPT-6 Astra or Claude Fable 5.1?
Today we're covering:
Where Astra clearly wins, and by how much
The one benchmark where Fable 5.1 actually comes out ahead
How the two compare on cost, not just raw score
The alignment numbers worth knowing
A straight answer, based on what you actually need it for
Let's get into it.
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Where Astra Wins, Clearly
Start with the categories where the gap isn't close.
On Terminal-Bench-Science, testing agentic scientific research, Astra scored 64.6% against Fable 5.1's 52.6%, at roughly 31% lower estimated cost. On FrontierMath Tier 4, one of the hardest math benchmarks that exists, Astra hit 97.6% against Fable 5.1's 87.8%. On AutomationBench, a business workflow test, Astra scored 41.4% against 31.4%.
The cybersecurity gap is even bigger. Astra scored a perfect 100% on ExploitBench, a benchmark testing exploit development, and reached a 42.4% success rate on ExploitGym, using far fewer tokens to get there. On SRE-Bench, testing reverse engineering software without source code access, Astra solved 88% of tasks on the first attempt. Astra meets the Critical threshold for cybersecurity under OpenAI's own risk framework, a jump significant enough that OpenAI is deliberately restricting the most advanced version of it.
On BenchCAD, a 3D reconstruction test, Astra hit 95.9% against Fable 5.1's 84.3%, at roughly 86% lower estimated cost. That's not a small win, that's a wide gap at a fraction of the price.
The One Place Fable 5.1 Actually Wins
Here's the exception worth knowing, because it says something real. On Humanity's Last Exam with tools, one of the broadest, most demanding multi-discipline reasoning benchmarks around, Fable 5.1 scored 65.0% against Astra's 57.2%. That's the clearest win Anthropic's model has on a shared benchmark.
It's a meaningful data point specifically because of what that benchmark tests, wide-ranging, cross-domain reasoning that doesn't fit neatly into one specialty. Astra dominates the narrow, technical, well-defined benchmarks. Fable 5.1 holds its own, and wins, on the one built to resist exactly that kind of narrow specialization.
The Coding Comparison Is Actually Close
This is the category where you shouldn't assume one model just wins outright.
On Terminal-Bench 4.0, Astra scored 57.9% against Fable 5.1's 55.8%, a real but small gap, at about 63% lower estimated cost. On DeepSWE, Astra reached 74.1% against Fable 5.1's 67.4%. On FrontierCode Extended and Main, the two sit within a few points of each other, 64.5% versus 63.6%, and 53.3% versus 50.9%.
For everyday coding, both models genuinely hold up. The bigger difference tends to show up in cost, not capability.
What Cost Actually Tells You
This is the part worth paying closer attention to than the headline scores. Astra doesn't just win several benchmarks, it wins many of them while using less compute. On Terminal-Bench 4.0, that's a 63% cost advantage. On BenchCAD, it's 86%.
But Anthropic made a specific bet with Fable 5.1's pricing that changes this picture depending on your workflow. Cache-read costs, the price of reusing context a model has already processed, dropped by 75%. That specifically benefits long, tool-heavy, context-reusing agent sessions, exactly the kind of work that racks up cost fastest. For highly agentic workloads, Anthropic estimates savings up to 45% compared to Fable 5.
So the real cost comparison depends on your task shape. Short, one-off requests tend to favor Astra's per-task pricing. Long, context-heavy agent sessions may favor Fable 5.1's cache pricing more than the raw benchmark cost numbers suggest.
The Alignment Numbers Worth Knowing
Capability isn't the only thing that matters if you're planning to give either model real autonomy.
On a computer-use safety stress test built to elicit misbehavior, Astra produced misaligned outcomes just 2.4% of the time, against 9.5% for Fable 5.1. Astra also never attempted to circumvent a deliberately evadable auto-review denial in internal testing, even when the assigned task was otherwise impossible to complete without doing so. On an evaluation modeled directly on the Hugging Face breach incident, Astra went beyond its authorized target in 0% of cases, compared to 48% for GPT-5.6 Sol without safeguards.
Anthropic's own alignment work on Fable 5.1 showed real improvement too, it's significantly less likely than its predecessor to try accessing resources outside its test environment on impossible tasks, and less likely to reason its way into believing a real situation is just a simulation. Fable 5.1 improved. Astra currently tests lower on the specific benchmarks both companies ran.
So, Who's Actually Better?
Here's the honest answer, not a dodge. On raw benchmark performance across math, science, cybersecurity, and cost efficiency, Astra currently leads, often by a wide margin. That's a real, measurable result, not marketing spin.
But "better" isn't the same question as "better for you." If your work is narrow and technical, single-domain coding, structured research, cybersecurity-adjacent tasks, Astra's lead is the more relevant number. If your work is broad and multi-disciplinary, spanning domains rather than staying inside one, Fable 5.1's win on Humanity's Last Exam suggests it may handle that kind of ambiguity better. If your workflow is long, context-heavy, and agent-driven, Fable 5.1's pricing changes might matter more to your actual bill than either model's leaderboard position.
What This Means For You
Don't pick a model off a single headline benchmark, the gap that matters is the one on the specific type of task you actually run
If cost matters, test both on your real workflow, not the published benchmark cost, since cache-heavy agent work and short one-off tasks land very differently on each model's pricing
If you're weighing autonomy or unsupervised use, Astra's current alignment numbers are stronger on the shared tests both companies ran
Treat the one benchmark Fable 5.1 wins as a real signal, not a footnote, broad reasoning is exactly the kind of task most real work actually looks like
Wrap Up
What you learned today:
Astra leads on most shared benchmarks, math, science, cybersecurity, and cost efficiency, often by a meaningful margin
Fable 5.1's one clear win, on the broadest multi-domain reasoning benchmark tested, is a real and telling exception
Coding performance is close enough that cost and workflow shape should decide it, not raw score
Alignment testing currently favors Astra on the benchmarks both labs ran, though Anthropic showed real improvement over its own prior model
If you came here for a one-word answer, it's Astra, on paper, for now. But paper isn't your actual Tuesday. Run your own task through both before you let either company's benchmark chart make the decision for you.
Thanks for being part of this community. Keep learning,
🔑 Robert from God of Prompt
P.S. Every prompt in the free library is tested on both of these models and carries the date it was last checked. If you want to run the same task through Astra and Fable 5.1 tonight, that is the fastest place to start.
