GPT-6 Astra vs Claude Fable 5.1: Which Frontier Coding Agent Fits Your Workload?
Compare GPT-6 Astra and Claude Fable 5.1 on Coding Agent performance, Terminal-Bench, cache pricing, long-context economics and real agent workloads, with an interactive monthly cost calculator.
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At matched maximum settings, GPT-6 Astra and Claude Fable 5.1 tie at 53 on the Artificial Analysis Intelligence Index. They also tie at 62 on the Coding Agent Index, but Astra reaches that coding-agent score at roughly 60% of Fable's task cost. The largest pricing difference is cache reads: $1.00 / 1M for Astra versus $0.25 / 1M for Fable 5.1.
Short verdict: Astra for terminal-heavy execution; Fable for cache-heavy, long-running work
These models are closer than a simple “winner” headline suggests. GPT-6 Astra currently has the stronger Terminal-Bench v4.0 result in Artificial Analysis and is unusually token-efficient in coding-agent tests. Claude Fable 5.1 is designed for long-running, asynchronous work and has a much cheaper cache-read rate, which can matter when the same large working set is reused across many turns.
If a task spends most of its time executing commands, recovering from tool failures and moving through a terminal-heavy workflow, Astra has a strong case. If the workload repeatedly reuses a large codebase, specification, document set or system prompt over long sessions, Fable's cache economics become more important.
Sources: Artificial Analysis · Anthropic
Same base price, different cache economics
Both providers list the same headline input and output rates: $10 per million input tokens and $50 per million output tokens. The split appears when prompts are reused through caching.
| Price item | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Standard input / 1M | $10.00 | $10.00 |
| Output / 1M | $50.00 | $50.00 |
| Cache read / 1M | $1.00 | $0.25 |
| Context window | 1.05M | 1M in current AA comparison |
| Long-context pricing | Requests above 272K input: 2× input/cache and 1.5× output for the full request | No equivalent surcharge stated on the cited Fable 5.1 launch page |
That does not mean Fable is always cheaper. Artificial Analysis reports Astra using far fewer tokens per task at maximum effort, which is why Astra can still have a lower cost per completed benchmark task even though its cache-read price is four times higher.
Sources: OpenAI pricing · Anthropic pricing · Artificial Analysis comparison
What the current benchmark data actually says
The cleanest current comparison is not “Claude is better at coding” or “Astra dominates everything.” At maximum settings, Artificial Analysis reports a 53–53 tie on its Intelligence Index and a 62–62 tie on its Coding Agent Index.
The subtests reveal different strengths. In the model-level comparison, Astra scores 59% on Terminal-Bench v4.0 versus Fable 5.1 at 52%. Fable scores higher on SciCode (63% vs 56%), Humanity's Last Exam (59% vs 55%) and AA-LCR v1.1 (85% vs 81%).
On the Coding Agent Index, Astra reaches the same 62 score as Fable while Artificial Analysis says it costs about 40% less per task. That result is driven by token efficiency, not cheaper list pricing.
Terminal-Bench, Intelligence Index and Coding Agent Index are different measurements. Keep model version, reasoning effort and agent harness attached to every number before turning it into a recommendation.
Which model should you deploy?
| Workload | Better starting point | Why |
|---|---|---|
| Terminal-heavy coding, shell automation, repeated tool recovery | GPT-6 Astra | Stronger current Terminal-Bench v4.0 evidence and high token efficiency in agent tests |
| Long-running agent sessions that repeatedly reuse a large working set | Claude Fable 5.1 | $0.25 / 1M cache reads and explicit long-running agent positioning |
| Large document / codebase reasoning where prompts are reused heavily | Claude Fable 5.1 | Cache economics can outweigh identical base input/output prices |
| High-end coding where either model clears the quality bar | Benchmark both | The current Coding Agent Index is tied; task shape and token usage determine the winner |
For production, the best metric is still cost per accepted task: model bill + retries + tool failures + review time. A cheaper cache rate can be decisive in one workload while lower token use wins in another.
OpenAI API — GPT-6 Astra · Anthropic — Claude Fable 5.1 · Artificial Analysis — Benchmarking GPT-6 Astra · Artificial Analysis — GPT-6 Astra vs Claude Fable 5.1