Claude Fable 5.1 or GPT-6 Astra? I Looked at the Numbers and Made My Call

Let me admit something upfront: I already use AI-assisted development tools (Claude Code included) in my daily work, so claiming I have no stake in either side wouldn't be honest. I wanted to say that because claiming to be "objective" in a post like this while pretending otherwise would be wrong — I'll just try to report the numbers as fairly as I can, and keep my own opinion clearly separate.

Let's start with the sources, because that's where the first odd thing shows up: OpenAI's and Anthropic's own published benchmarks disagree with independent evaluators. According to OpenAI's own numbers, Astra beats Fable 5.1 on Terminal-Bench 4.0 (57.7% vs 55.8%) and DeepSWE v1.1 (74.1% vs 67.4%). But an independent evaluator, Artificial Analysis, flips the table on its own "Coding Agent Index": Fable 5.1 leads at 70 points against Astra's 67. The same source has Fable ahead by a wide margin on public coding benchmarks too — 84.2 vs 74.6 — though the two models' confidence intervals overlap, so I wouldn't read that gap as a clean win.

So the short answer isn't "which one is better," it's "better at what." From what I've gathered, the picture settles roughly like this: Fable 5.1 leads on agentic coding and scientific-research tasks; Astra leads on math-heavy problems and cybersecurity. That means the decision should follow the shape of your workload — if you're building long-running coding agents and retrieval pipelines, the data leans toward Fable; if you're building security tooling or doing math-heavy research, it leans toward Astra.

There's an interesting coincidence on pricing: both land at exactly the same rate — $10 per million input tokens, $50 per million output. Anthropic also cut cache-read pricing significantly on top of that, which meaningfully lowers total cost on long, multi-step tasks (agentic workflows that read and rewrite the same context over and over). That detail mattered more to me than it might sound, because nobody notices a price difference on short, one-off prompts — the real difference shows up on hour-long agent tasks that reprocess the same context repeatedly.

Now, my own side of this. Writing this portfolio site, the accounting system that reconciles eight payment providers, the live-stream selling backend — I keep needing the same thing: consistency on tasks that run for hours and span an entire codebase, the kind where you step away and come back needing the model to still remember where things stood. Anthropic's own announcement leans on exactly this point — that Fable 5.1 shows better judgment on ambiguous work and gives fewer confidently wrong answers. If that claim holds up, it's worth more to me than a model that's fast but stubborn about being wrong — because in a long session, what actually wastes time is the model insisting on a bad assumption.

But I'm not presenting this as a universal rule. If you're building a security-focused product, or doing math-heavy research, the numbers above point toward Astra, and it would be wrong to ignore that. My own leaning comes from the fact that my workload (long-running, multi-file, agentic coding) sits on the side Fable 5.1 is strong on — someone with a different workload landing on a different conclusion is entirely reasonable.