GPT-6 Astra vs Claude Fable 5.1: Same Price, Different Strengths — Which Frontier Model to Route First

GPT-6 Astra and Claude Fable 5.1 launched two days apart in early September 2026, both priced at $10 per million input tokens and $50 per million output tokens. The headline rate is identical, but the models are not. Astra dominates mathematics, computer use, and terminal-based agent workflows. Fable 5.1 leads independent composite intelligence scores and long-horizon knowledge work, and its 75% cache-read discount changes the cost equation for any workload with repeated context.

This post compares the two head-to-head across benchmarks, pricing, API surface, and routing implications. If you run production traffic through either model, you will likely end up routing to both. The question is which one goes first for which task.

OpenAI-compatible means a provider exposes a chat-completions endpoint whose request and response shape matches the OpenAI API contract closely enough that an unmodified OpenAI SDK call works against it after swapping three values: API key, base URL, and model name. The minimum surface in practice is POST /v1/chat/completions with messages, model, and an OpenAI-shaped streaming response.

TL;DR: GPT-6 Astra vs Claude Fable 5.1

Sources: Artificial Analysis (retrieved 2026-09-11), Coursiv (retrieved 2026-09-11), MindStudio (retrieved 2026-09-11).

Benchmark Breakdown: Where Each Model Wins

General intelligence

Artificial Analysis's Intelligence Index, a composite score covering reasoning, knowledge, and analytical quality, gives Fable 5.1 a score of 66 versus Astra's 61. That five-point gap makes Fable 5.1 the stronger pick for broadly difficult questions, research synthesis, and reasoning-heavy tasks without a clear executable workflow.

On Humanity's Last Exam with tools, Fable 5.1 leads by nearly eight points (65.0% vs 57.2%). Fable 5.1 also has a more recent knowledge cutoff (June 2026 vs April 30, 2026), which can matter for tasks involving very recent events.

Mathematics and science

The benchmarks reverse here. Astra leads FrontierMath Tier 4 at 97.6% versus 87.8%, GPQA Diamond at 96.0% versus 93.7%, and Terminal-Bench Science at 64.6% versus 52.6%. If your workload involves mathematical problem-solving, scientific reasoning, or formal verification, Astra has a material advantage.

Coding

This is closer than either vendor's marketing suggests. Astra wins on individual coding benchmarks like DeepSWE (74.1% vs 67.4%) and Terminal-Bench 4.0 (57.9% vs 55.8%). But on Artificial Analysis's Coding Agent Index, which measures end-to-end coding-agent workflows including repository navigation, multi-file editing, and test recovery, Fable 5.1 leads 70 to 67.

The implication for routing: Astra is the stronger pick for isolated coding problems and terminal-heavy workflows. Fable 5.1 is better for sustained code-agent sessions that need to understand a codebase holistically and produce mergeable pull requests.

Computer use and automation

Astra has the clearest advantage in computer use. It leads AutomationBench at 41.4% versus 31.4%, scores 59% on Terminal-Bench v4.0, and has strong results on OSWorld 2.0 and ScreenSpot Pro. If you're building an agent that operates a browser, navigates a desktop, or runs multi-step automation workflows, Astra is the first model to route to.

Token efficiency

Astra uses roughly one-third the output tokens of Fable 5.1 for the same Intelligence Index score at max reasoning effort (27K tokens vs 78K tokens per task according to Artificial Analysis). This means that despite identical per-token prices, Astra's cost per task is substantially lower when both models are run at maximum effort.

Pricing Deep Dive: Cache Economics Change Everything

The headline rate is identical: $10 input, $50 output, $12.50 cache writes. The difference is cache reads.

For one-shot requests with no caching, the models cost the same. But for any workload that reuses system prompts, few-shot examples, or long documents across multiple requests, Fable 5.1's cache reads cost 75% less than Astra's.

Practical example: A coding agent that loads a 200K-token repository context for every request. Over 100 requests:

• Astra cache reads: 200K × 100 × $1.00/M = $20.00
• Fable 5.1 cache reads: 200K × 100 × $0.25/M = $5.00

That $15 difference scales linearly with request volume. For high-throughput production workloads with significant context reuse, Fable 5.1's cache pricing gives it a meaningful cost edge that the per-token sticker price does not show.

Cost per task (Artificial Analysis data): At maximum reasoning effort, Astra costs $3.26 per task versus Fable 5.1's $7.63 on the Intelligence Index benchmark. Astra achieves the same score at roughly 40% of the cost, driven by its much lower token consumption. The cache advantage only kicks in for workloads with repeated context.

API Surface Comparison: Responses API vs Messages API

The models use different API formats, but both support the standard features production workloads need.

Both models are accessible through OpenAI-compatible endpoints via routers like TheRouter, OpenRouter, and LiteLLM. If you already use the OpenAI SDK, you can route to Fable 5.1 through an OpenAI-compatible gateway without changing your client code.

OpenAI SDK routing example (both models through TheRouter)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.therouter.ai/v1",
    api_key="your-therouter-key",
)

# Route to GPT-6 Astra
astra_response = client.chat.completions.create(
    model="openai/gpt-6-astra",
    messages=[{"role": "user", "content": "Solve this integral..."}],
)

# Route to Claude Fable 5.1
fable_response = client.chat.completions.create(
    model="anthropic/claude-fable-5-1",
    messages=[{"role": "user", "content": "Review this codebase and suggest improvements..."}],
)

One SDK, two frontier models, one API key.

Routing Decision Matrix: When to Pick Which

Fallback Chains: Using Both Models Together

The strongest routing setup uses both. A practical pattern:

Chain 1 — Math/science workloads: GPT-6 Astra (primary) → Claude Fable 5.1 (fallback on rate limit or timeout)

Chain 2 — Coding and general reasoning: Claude Fable 5.1 (primary) → GPT-6 Astra (fallback)

Chain 3 — Cost-optimized general tasks: GPT-6 Astra at low/medium effort (cheap per task) → Claude Fable 5.1 at max effort (when the task needs more reasoning power)

With a router like TheRouter, you configure these chains once and let the routing layer handle provider selection, retry, and fallback automatically. Both models speak OpenAI-compatible formats through the gateway, so your application code stays the same regardless of which model handles the request.

Frequently Asked Questions

Is GPT-6 Astra smarter than Claude Fable 5.1?

Not on aggregate. Artificial Analysis's Intelligence Index gives Fable 5.1 a score of 66 versus Astra's 61. Astra wins on math, science, and computer use, but Fable 5.1 leads on general reasoning, knowledge work, and expert-level Q&A.

Which model is cheaper?

They charge the same per-token rates ($10/$50), but the effective cost depends on your workload. Astra uses fewer tokens per task, making it cheaper for one-shot requests at max effort. Fable 5.1's cache reads are 75% cheaper ($0.25/M vs $1.00/M), making it cheaper for high-volume workloads with repeated context.

Can I use both through a single API?

Yes. Routers like TheRouter provide OpenAI-compatible endpoints for both models. You can switch between them by changing the model parameter, or set up automatic fallback chains.

Which is better for coding?

It depends on the task. Astra wins on isolated coding benchmarks (DeepSWE 74.1% vs 67.4%). Fable 5.1 wins on the end-to-end Coding Agent Index (70 vs 67). For terminal-heavy work, Astra has the edge. For sustained code-agent sessions that need holistic codebase understanding, Fable 5.1 is stronger.

What about hallucination rates?

Astra halved its hallucination rate compared to GPT-5.6 Sol (from 92% to 51% on AA-Omniscience at max effort, according to Artificial Analysis). Direct hallucination comparisons between Astra and Fable 5.1 on the same benchmark are not yet published by independent evaluators.