Claude Fable 5.1 vs GPT-6 Astra: Pricing, Benchmarks, and Which to Use for What

Quick Answer

Claude Fable 5.1 (Anthropic, September 1, 2026) and GPT-6 Astra (OpenAI, September 3, 2026) are the two current frontier flagships — at identical list prices: $10 / $50 per million tokens. The decision is made by everything around that headline number: Fable 5.1 is cheaper to run agentically (cache reads at $0.25/M, a 75% cut) and leads independent coding impressions; Astra leads computer use and OpenAI's ARC-AGI-3 table and has the larger raw context (~1.05M vs 1M). Pick by workload, not by leaderboard.

Side by Side

*Fable 5's cache write was $12.50; confirm current write pricing on Anthropic's announcement — the 75% cache-read cut is the officially confirmed change.

The Pricing Is the Same — Until You Look at Caching

Both flagships list at $10/$50, which makes this the first flagship-vs-flagship race where the sticker is a tie. The difference is the cache-read rate:

• Fable 5.1: $0.25/M cache reads — down 75% from Fable 5's $1.00. Anthropic estimates ~25% lower total cost than Fable 5 for typical workloads and up to ~45% less for heavily agentic work, and almost all of that comes from the cache discount.
• Astra: $1.00/M cache reads — 4x Fable 5.1's rate, on the same $10 fresh-input price.

What that means concretely: an agent session that re-reads a large stable system prompt and codebase context on every step (the Claude Code pattern) pays roughly 4x less for the cached portion on Fable 5.1. On long agent loops, cache reads can be the majority of input tokens — which is why the "same price" flagships are not the same cost in practice.

Capability Verdict by Workload

Choose Fable 5.1 for:

• Long agent sessions and coding agents — best independent coding impressions, 4x cheaper cache reads, 128K output for large diffs.
• Long-context document work where cost matters — 1M tokens without Astra's reported double-rate billing past ~272K input.
• Claude Code / MCP-based stacks — native fit, plus migration is mostly a model-ID change.

Choose GPT-6 Astra for:

• Computer use and desktop automation — the one category with an independently-corroborated, visually obvious lead (~72.6% OSWorld).
• Hardest reasoning / math — the 10 Lean 4-verified proofs and the ARC-AGI-3 numbers are the strongest published reasoning evidence of any model.
• Codex-centric workflows — Astra's Codex gains context preservation and retrieval when the window fills.

Genuinely close-call: general chat/summarization/analysis. Both are excellent; route on price mechanics (caching) and latency from your provider instead.

One Caveat on Astra's Benchmarks

Astra's widest margins (ARC-AGI-3 ~98.6%+ vs Fable 5.1's 87.8%) are vendor-reported and not yet independently replicated. Independent coding impressions are mixed-to-against-Astra — some evaluators put Fable 5.1, and even Claude Opus 5, ahead on real software work. Meanwhile Fable 5.1's numbers ship with fewer disputes, but also with less third-party evaluation overall (it launched two days earlier). Re-run both on your tasks before committing a fleet. Full score tables: GPT-6 Astra benchmarks.

The Practical Setup: Don't Choose — Route

Since both models speak their vendor's OpenAI- or Anthropic-compatible API and cost the same at list, the highest-leverage move is a task-based routing rule behind one gateway key:

from openai import OpenAI
client = OpenAI(api_key="sk-teamo-xxxxxx", base_url="https://api.teamorouter.com/v1")

# agentic coding / long session  -> "claude-fable-5-1"  (cheap cache reads)
# computer use / hardest reasoning -> "gpt-6-astra"      (category lead)
# bulk work                       -> cheaper fallbacks

TeamoRouter carries both flagships behind one key and base_url, so switching a task from one to the other is a model-string change — and you can A/B them on identical tasks before deciding where your budget goes.

FAQ

Q: Is Claude Fable 5.1 cheaper than GPT-6 Astra? At list price they're identical ($10/$50 per million tokens). Fable 5.1 is effectively cheaper for cache-heavy agentic workloads ($0.25/M cache reads vs $1.00), while Astra can get more expensive on very long contexts (reported double-rate billing past ~272K input tokens).

Q: Which is better for coding? Independent practitioner impressions currently favor Fable 5.1; OpenAI's table shows Astra ahead (74.1% DeepSWE v1.1, and ARC-AGI-3 by a wide margin). The gap is within the range where your own repo is the tiebreaker — and Fable 5.1's cache pricing makes long coding sessions cheaper either way.

Q: Which has the bigger context window? Nearly identical: Astra ~1.05M tokens, Fable 5.1 1M with 128K max output. The practical difference is Astra's tiered long-context pricing versus Fable 5.1's flat rates.

Q: Do both have cheaper "little sibling" models? Yes. Anthropic ships Fable 5.1 alongside the restricted-access Mythos 5.1 (same model, stricter guardrails); OpenAI keeps GPT-5.6 (Sol/Terra/Luna) as the cheaper tiers below Astra.

Q: Can I use both through one API key? Yes — an OpenAI-compatible gateway like TeamoRouter exposes both, so per-task routing and A/B testing are config changes, not new accounts.

Summary

Same $10/$50 sticker, different shapes: Fable 5.1 wins cost-per-agent-loop and independent coding trust; Astra wins computer use, raw reasoning scores, and Codex integration. Route by task instead of declaring a winner. Sign up for TeamoRouter to run both flagships on one key and let your own benchmarks decide.

Get Started

TeamoRouter — one key, one base_url, both flagships plus cheap fallbacks for everything else.