Choose GPT-6 Astra if...
- Your product is an agent operating tools, terminal commands, or multi-step workflows.
- AutomationBench and Terminal-Bench gains justify the same headline price.
- You need the largest context window: 1.05M tokens.
Model comparison / 2026
GPT-6 Astra
vs Fable 5.1
The short answer: GPT-6 Astra and Anthropic Fable 5.1 are priced identically - $10.00 per 1M input and $50.00 per 1M output tokens. The real differences are cache economics and where the capability lives. Choose based on workload, not price: the price is the same.
Last updated: September 4, 2026 · Reported data, not independent re-runs
Same price.
Different bet.
Neither model wins everywhere. The useful question is what kind of work you are paying to make reliable.
| Dimension | Winner | Notes |
|---|---|---|
| API price | Tie | Both list $10 input / $50 output |
| Cache read cost | Fable 5.1 | $0.25 vs $1.00 - 75% cheaper |
| Long-context pricing | Fable 5.1* | Astra bills 2x above 272K input tokens |
| Agentic / automation | Astra | AutomationBench: 41.4% vs 31.4% |
| Knowledge tests | Near tie | GPQA: 96.0% vs 93.7% |
| Context window | Astra | 1.05M vs 1M tokens |
* Long-context note: published Fable 5.1 pricing documents reviewed for this page do not show an equivalent surcharge. Confirm current provider terms before committing to a large-context workload.
READ THE FINE PRINTPrice comparison:
the tie has a catch.
Headline rates match. Repeated prefixes and very long inputs change the effective bill.
| Item | GPT-6 Astra | Fable 5.1 |
|---|---|---|
| Input / 1M tokens | $10.00 | $10.00 |
| Output / 1M tokens | $50.00 | $50.00 |
| Cache read / 1M | $1.00 | $0.25 |
| Cache write / 1M | $12.50 | Not confirmed |
Cache reads. Fable 5.1's reported $0.25 read rate is 75% below Astra's $1.00. For agents, support systems, and document workflows that reuse a long system prompt, the saving compounds across requests. Fable's cache-write rate has not been directly confirmed here; do not substitute the older Fable 5 figure.
Long context. Astra bills 2x the standard rate once a request exceeds 272K input tokens. The published Fable 5.1 documents reviewed here show no equivalent surcharge, but pricing terms can change. See the full token cost breakdown before launch.
Specs side
by side.
Only figures that were directly supported in the supplied source set are included.
| Specification | GPT-6 Astra | Fable 5.1 |
|---|---|---|
| Context window | 1.05M tokens | 1M tokens |
| Max output tokens | - | 128K tokens |
| Knowledge cutoff | - | June 2026 |
A dash means the figure was not reliably confirmed for this comparison - it is not a claim that the capability is unavailable.
Capability has
a shape.
Astra's reported lead is widest on agentic tasks and much thinner on knowledge tests.
| Benchmark | GPT-6 Astra | Fable 5.1 | Gap |
|---|---|---|---|
| Terminal-Bench Science 0.1 | 64.6% | 52.6% | +12.0 |
| AutomationBench | 41.4% | 31.4% | +10.0 |
| BenchCAD | 95.9% | 84.3% | +11.6 |
| FrontierMath Tier 4 (v2) | 97.6% | 87.8% | +9.8 |
| GPQA Diamond | 96.0% | 93.7% | +2.3 |
Astra leads on all five reported head-to-heads here, but the pattern matters more than the total count: double-digit leads on agentic and automation tasks, versus a roughly two-point lead on PhD-level knowledge. If you are building an agent, Astra's margin is the meaningful signal. If you are answering knowledge questions, the difference may sit within benchmark noise. See the full benchmark ledger for the wider comparison.
Need implementation guidance? See how to use GPT-6 Astra after reviewing the comparison.
Choose by
workload.
Use the model whose strengths match the expensive part of your product.
Choose Fable 5.1 if...
- Your app is cache-heavy and repeated prefixes drive most of the bill.
- You run long-context workloads and want to avoid Astra's 272K surcharge.
- Your task is knowledge-heavy, where the GPQA margin is thin.
Price-sensitive? Neither model is cheaper at headline rates. Fable 5.1 only wins in practice when cache reuse or long-context billing dominates your workload.
Speed:
don't guess.
Latency changes with provider, region, load, and output length.
No stable same-protocol comparison yet. Both models are newly released, and no reliable Astra-versus-Fable 5.1 speed figure was confirmed for this page. Check the live latency leaderboard on Artificial Analysis before committing.
FAQ
Short answers to the questions that matter when the rates match.
Is GPT-6 Astra cheaper than Fable 5.1?
No. Both are $10/$50 per 1M input/output tokens. Fable 5.1 can be cheaper in practice for cache-heavy workloads ($0.25 versus $1.00 per 1M cached reads) or long-context requests where Astra's 2x surcharge applies above 272K input tokens.
Which is better for agents, Astra or Fable 5.1?
On the reported benchmarks used here, Astra: AutomationBench is 41.4% versus 31.4%, and Terminal-Bench Science is 64.6% versus 52.6%.
Are GPT-6 Astra and Fable 5.1 the same price?
Yes. Their headline standard API rates are identical: $10.00 per 1M input tokens and $50.00 per 1M output tokens.
What is Fable 5.1's cache-write price?
It was not directly confirmed in the source set used for this page. The older Fable 5 figure should not be copied over to Fable 5.1.
Sources and limits
Pricing and specification figures were cross-checked against the supplied source set: official model and platform documentation, VentureBeat, Devin, MindStudio, and DataScienceDojo. Benchmark figures are reproduced from this site's benchmark ledger; the individual source URLs and methodology are listed in its Sources and methodology section.
Pricing and model specifications can change. Re-check official documentation before publishing a comparison, signing a contract, or estimating production spend.