GPT-6 Astra vs Claude Fable 5.1 API Comparison (2026): Pricing, Context, and Routing

Reuters reported on September 3, 2026 that OpenAI launched GPT-6 Astra, its most capable model, amid growing scrutiny over how far AI agents can go on their own. If you build with APIs, that launch quietly set up one of the cleanest head-to-heads of the year: GPT-6 Astra against Claude Fable 5.1, Anthropic's top reasoning model. Both list at $10 per million input tokens and $50 per million output tokens. Same sticker. Very different economics once you look past the base rate.

This is a practical comparison for developers deciding where to send hard, long-horizon work. We'll cover pricing, cache behavior, context limits, and real routing code. No hype, just the numbers that hit your bill.

Pricing: The Base Rate Is a Tie

Here's the thing that surprises people. On paper these two models cost exactly the same to run. The interesting decisions start when you factor in caching, long-context surcharges, and batch discounts.

Two footnotes that matter in production. GPT-6 Astra bills cache writes at $12.50 per million (1.25x the uncached input rate), and prompts over 272,000 input tokens jump to 2x input and 1.5x output for the whole request. Claude Fable 5.1's cache reads are cheaper per token, but Anthropic charges a separate cache write fee, so the win depends on your read-to-write ratio.

Where They Actually Differ

Same price, so the choice comes down to behavior and fit. Here's the comparison that drives real routing decisions.

My read: if your workload is cache-heavy with a big stable prefix and lots of reads, Claude Fable 5.1's cheaper cache reads add up. If you want explicit control over how hard the model thinks per request, GPT-6 Astra's reasoning.effort dial (all the way up to max) gives you a lever Fable doesn't expose the same way.

Calling GPT-6 Astra

GPT-6 Astra runs on both Chat Completions and the Responses API. Here's a minimal curl call with reasoning effort turned up for a hard task:

curl https://api.openai.com/v1/responses \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "gpt-6-astra",
    "reasoning": {"effort": "high"},
    "input": "Refactor this module and explain the risky changes first..."
  }'

Python, same idea, but keep the stable instructions separate so they cache well:

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

resp = client.responses.create(
    model="gpt-6-astra",
    reasoning={"effort": "high"},
    input=[
        {"role": "system", "content": "You are a senior code reviewer. Group issues by severity."},
        {"role": "user", "content": "Review this diff and flag security issues first..."}
    ],
)

print(resp.output_text)

Calling Claude Fable 5.1

Claude Fable 5.1 runs on the Messages API. Mark your reusable system block cacheable to get those 2.5% read rates:

import os
from anthropic import Anthropic

client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

resp = client.messages.create(
    model="claude-fable-5-1",
    max_tokens=1200,
    system=[{
        "type": "text",
        "text": "You are a senior code reviewer. Group issues by severity with exact line references.",
        "cache_control": {"type": "ephemeral"}
    }],
    messages=[{
        "role": "user",
        "content": "Review this diff and flag security issues first..."
    }]
)

print(resp.content[0].text)

Notice the two SDKs and two auth headers. That's the friction most teams hit: you want to A/B these models, but each one means a different client, different request shape, and a different billing dashboard.

Testing Both Without Two Integrations

The cleanest way to compare frontier models is to put them behind one OpenAI-compatible endpoint, then flip the model string. This is exactly what a gateway like KissAPI is for: keep one SDK, one key, one usage view, and route gpt-6-astra or claude-fable-5-1 per request.

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["KISSAPI_KEY"],
    base_url="https://api.kissapi.ai/v1",
)

def ask(model, prompt):
    return client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    ).choices[0].message.content

# Same code path, swap the model to benchmark
astra = ask("gpt-6-astra", "Plan a migration from a monolith to services.")
fable = ask("claude-fable-5-1", "Plan a migration from a monolith to services.")

Run the same prompts through both, compare quality and latency on your actual tasks, and let the numbers decide. When two models cost the same, the only honest tiebreaker is your own eval set.

Which Should You Pick?

• Pick GPT-6 Astra when you want explicit reasoning-effort control, computer use, and end-to-end agentic tasks, and your prompts usually stay under 272K tokens to dodge the long-context surcharge.
• Pick Claude Fable 5.1 when you have a large, stable cached prefix read many times, or when your evals on lighter models fall short and you need the deepest reasoning.
• Drop to Claude Opus 5 ($5 / $25) when the task is hard but not frontier-hard. Half the price of both, same 1M context. Most teams over-route to the top tier.

Frequently Asked Questions

How much do GPT-6 Astra and Claude Fable 5.1 cost per million tokens?

GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens. Claude Fable 5.1 also costs $10 per million input tokens and $50 per million output tokens. The headline prices match; the difference is in cache economics and context handling.

Which model has cheaper cached input, GPT-6 Astra or Claude Fable 5.1?

Claude Fable 5.1 has cheaper cached reads. GPT-6 Astra charges $1 per million cached input tokens, which is 10% of its base input rate. Claude Fable 5.1 charges prompt cache reads at 2.5% of base input, which works out to roughly $0.25 per million tokens.

What context window do GPT-6 Astra and Claude Fable 5.1 support?

GPT-6 Astra supports a 1,050,000-token context window with up to 922,000 input tokens and 128,000 output tokens. Claude Fable 5.1 supports a 1,000,000-token context window with up to 128,000 output tokens.