Astra or Fable 5.1 — if you’re trying to decide between OpenAI’s and Anthropic’s newest flagship models, you’ve probably already found a dozen articles listing benchmark scores. What’s missing from most of them is a straight answer: which one should you actually use for your specific work? Both models launched within 48 hours of each other in September 2026, cost the same on paper, and neither company benchmarked directly against the other’s newest release. So, instead of repeating those tables, this guide sorts the decision by what you’re actually trying to do.
Independent testing from Artificial Analysis puts GPT-6 Astra and Claude Fable 5.1 in a dead heat on raw intelligence. From there, the real differences show up in speed, cache pricing, and which specialized tasks each model was actually trained hardest for.
What the Independent Numbers Actually Show
Artificial Analysis, a neutral third-party evaluator, tested both models on the same suite of tasks rather than relying on either company’s own benchmarks. As of this week, both models score exactly 53 on its Intelligence Index — a genuine tie. Beyond that headline number, though, real gaps appear:
| Metric | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Intelligence Index (independent) | 53 | 53 (tied) |
| Output speed | 56.5 tokens/sec | 69.4 tokens/sec |
| Cache-read price (per million tokens) | $1.00 | $0.25 |
| Blended cost per task | $7.70 | $7.175 |
| Context window | 1M tokens | 1M tokens (tied) |
Where Each Model Pulls Ahead on Specialized Tasks
Away from the general intelligence tie, OpenAI’s own published benchmarks show real separation on specific domains where Astra was purpose-trained hard. It’s worth remembering these numbers come from OpenAI’s own testing, so treat them as a starting point rather than the final word:
- Advanced math: Astra scored 97.6% on FrontierMath Tier 4, well ahead of Fable 5.1’s 87.8%.
- Cybersecurity: Astra reached OpenAI’s “Critical” capability threshold, hitting 100% on ExploitBench — a category Fable 5.1 wasn’t scored on at all, since Anthropic’s models decline the majority of those test questions by design.
- Computer use: Astra leads decisively here, since it’s specifically built to navigate real software autonomously, a capability Fable 5.1 doesn’t compete on directly.
- Abstract reasoning: Astra hit 99.9% on ARC-AGI-3, dramatically ahead of Fable 5.1’s 30.2%.
The Decision Guide
You’re automating tasks inside real software
Choose GPT-6 Astra. Computer use is its headline feature, and it’s the only one of the two purpose-built to click through browsers, fill out forms, and operate desktop applications autonomously.
You’re running cache-heavy, repeated-context workloads
Choose Claude Fable 5.1. Its cache reads cost a quarter of Astra’s, and on Artificial Analysis’s blended cost-per-task measure, it comes out slightly cheaper overall despite an identical headline rate card.
You need the fastest possible response times
Choose Claude Fable 5.1. It generates roughly 23% more tokens per second and has a shorter time-to-first-token in independent testing.
You’re doing advanced math, science, or authorized security research
Choose GPT-6 Astra. These are the categories where OpenAI specifically pushed capability the hardest, and the gap over Fable 5.1 is large enough to matter for genuinely research-heavy work.
You’re already committed to one ecosystem
Stay put, at least for now. If your team already builds around Claude Code or OpenAI’s Codex harness, part of any benchmark gap you’d see comes from the surrounding tooling, not the model alone — so a clean switch is a bigger project than swapping an API key.
Neither company has published a benchmark suite testing directly against the other’s newest model — Anthropic tested Fable 5.1 against OpenAI’s older GPT-5.6 Sol, and OpenAI tested Astra mostly against its own predecessor. Every head-to-head number circulating right now, including this one, comes from stitching together two separate self-reported test runs plus independent third-party testing.
Frequently Asked Questions
Is one of these models just better overall?
No. Independent testing puts them in a tie on general intelligence. The real decision comes down to your specific workload, not a single winner.
Do they actually cost the same?
Only at the headline level — both charge $10 per million input tokens and $50 per million output tokens. Cache pricing diverges sharply, with Fable 5.1 four times cheaper on cache reads, which matters a lot for cache-heavy applications.
Can I switch between them easily?
Through routing services like OpenRouter, yes — switching is often just a model-slug change. Migrating a production system built specifically around one provider’s tooling, like Claude Code or Codex, takes considerably more work.
ChatGPT vs Gemini vs Claude: Which One Should You Use in 2026 — for the broader picture beyond just these two newest models.