GPT-6 Astra vs Claude Fable 5.1: who actually wins?

Both labs shipped a flagship within weeks of each other, and both launch pages say roughly the same thing: most capable model we've ever built. Put the two side by side on the numbers that are actually public, and the picture is more specific than either headline.

This comparison gets asked constantly for a reason that has nothing to do with brand loyalty: teams building agents have to pick one to build on top of, and switching later means re-testing every prompt, every tool call, and every guardrail against a different model's quirks. Getting it right the first time is worth more than the marginal benchmark points either company is advertising this quarter.

Where they actually differ

Category	GPT-6 Astra	Claude Fable 5.1	Edge
Reasoning (FrontierMath T4, ARC-AGI-3)	97.6% / 99.9%	Behind on both	Astra
Agentic coding (Terminal-Bench 4.0)	57.9%	55.8%	Astra, barely
Long context (needle test, 512K-1M)	96.3%	Not independently published	Astra, on paper
Pricing (API, per M tokens)	$10 in / $50 out	Priced separately by tier	Depends on tier used
Ecosystem and tooling	ChatGPT, Codex, Azure, Bedrock	Claude Code, skills marketplace, MCP	Fable, for builders
Scope discipline in testing	Bypassed a deliberately bypassable auto-review 0% of the time	Anthropic's own agentic safety testing, not directly comparable	Too close to call

What these benchmarks actually test

It's worth being specific about what each row measures, because "reasoning" and "agentic coding" get used interchangeably when they're testing different skills. FrontierMath Tier 4 and ARC-AGI-3 are closed-form reasoning problems with a single correct answer, the kind of test a model can be very good at without ever touching a file system or a terminal. Terminal-Bench 4.0 is the opposite: it scores a model on real terminal use, software engineering, and system configuration, the actual shape of most agentic coding work. That's why Astra's wide lead on the reasoning benchmarks doesn't carry over cleanly to Terminal-Bench, where the gap shrinks to two points. OpenAI also reports Astra at 72.6% on OSWorld 2.0, a computer-use benchmark, in roughly 40 minutes per task, against 65.7% in about 75 minutes for its own predecessor, GPT-5.6 Sol. That's a same-lab, generation-over-generation comparison, not a claim about Fable 5.1, but it says something real about the direction Astra is optimized in: less time per task at a similar or better accuracy, which matters more for a bill and a deadline than another point on a reasoning leaderboard.

The gap that matters is smaller than the headline

Terminal-Bench 4.0 tests exactly the kind of work most people mean when they say "AI agent": real terminal use, real software engineering, real system configuration. Astra scores 57.9%. Fable 5.1 scores 55.8%. Two points, on a benchmark that's genuinely hard, is not the gap the marketing copy implies. Where OpenAI does pull ahead by a wide margin is reasoning-only tests like FrontierMath and ARC-AGI-3, and on long-context retrieval, where Astra holds 96.3% accuracy across up to a million tokens. If your job is closer to "solve a hard, self-contained problem" than "operate inside a messy real system," that gap is the one to weigh.

The part the benchmark table can't show you

A benchmark score is a snapshot from a controlled test. It doesn't tell you what happens three hours into an unattended agent session, when the task has drifted from what you originally asked and the model has to decide whether to keep going, ask, or quietly expand its own scope. That's where the ecosystem around each model starts to matter as much as the model itself. Anthropic has spent more of its product effort on exactly that problem: Claude Code as the default harness, plus agents built specifically to add guardrails a raw model doesn't have on its own. Terraform IaC Reviewer, for instance, refuses to touch infrastructure without checking the blast radius first, and Doublecheck Agent reviews AI-generated claims for hallucinations instead of trusting them by default. Address Comments Agent adds a smaller but telling piece of the same discipline: its instructions have it push back on a review comment that doesn't make sense instead of implementing it silently, the opposite of the failure mode everyone worries about with unattended agents. That kind of discipline never shows up in a Terminal-Bench score, but it shows up the first time you leave an agent running without babysitting it.

Common mistakes people make comparing the two

The most common one is picking a model off a single leaderboard screenshot instead of testing it on the actual task it'll run in production. A two-point Terminal-Bench gap can flip either direction on your specific codebase, your specific prompts, and your specific tools, and the only way to know is to run both on the same real task and compare. The second mistake is comparing sticker prices instead of total cost per completed task: Astra's Fast mode runs at 2x the speed for 2x the price, which can be cheaper overall if it means fewer retries, or more expensive if the task never needed the speed. The third is assuming ecosystem maturity transfers across labs, that a harness pattern that works well in Claude Code will behave identically in Codex, or the reverse. The tools rhyme, but the guardrails and defaults underneath them don't transfer automatically, and finding that out in production is the expensive way to learn it.

How to actually test this yourself

Pick one recurring task you already do today, not a synthetic benchmark, and run it on both models with the same prompt and the same tools available. Track cost per successful completion, not cost per token, since a cheaper model that needs three retries can cost more than an expensive one that gets it right the first time. Note every point where a human had to step in and redirect the agent, because that's the failure mode no benchmark table captures, and it's usually the one that decides which model you actually keep using after the first week.

The verdict, if you need one

For a single hard problem you can check by hand, Astra is the better raw model, and it's not close on reasoning benchmarks. For an agent you have to trust to run for an hour without wandering off task, Fable 5.1 is still the safer default, not because it's smarter, but because the harness around it was built by a lab that treats "does it stay on task" as the actual product, not a footnote in a launch post. Test both on the job you actually have, because that two-point gap on Terminal-Bench means the real answer depends more on your task than on either company's leaderboard slide.