GPT-6 Astra or Claude Fable 5.1: Two Top Models by Price and Index

If you are choosing between GPT-6 Astra and Claude Fable 5.1, both are in the top price tier and have the same intelligence index. Below is a GPT 6 vs Fable 5.1 comparison on what can be verified: price, cache, context and the cost of an agent task. A table cannot tell which model will handle your work better, so the end describes a way to check on your own data.

Specs side by side

Prices as of October 18, 2026, $ per 1M tokens. "Index" is the Intelligence Index (Artificial Analysis).

Context, image support and index match; the price differs: at Zukko Astra is considerably cheaper than Fable 5.1. Their official list prices are the same, so the difference comes from the gateway rate, not from model properties. Connecting Astra is covered in GPT-6 Astra→.

A single request without cache

Take a request with 10,000 input and 1000 output tokens. Formula: input / 1M × input price + output / 1M × output price.

• Astra: 10,000 × 0.62 / 1M + 1000 × 3.05 / 1M = 0.0062 + 0.00305 = $0.00925.
• Fable 5.1: 10,000 × 7 / 1M + 1000 × 35 / 1M = 0.07 + 0.035 = $0.105.

For one request the difference is small in absolute numbers (about ten cents), but across thousands of calls a day it becomes a noticeable sum.

An agent task with cache

An agent repeatedly sends the same long history in a loop, so cache decides things. Take a hypothetical session: 1,360,000 input tokens in total, of which 1,200,000 are read from cache, 60,000 are written to cache, and the remaining 100,000 are fresh input; output is 20,000 tokens.

Accounting rules: for Claude in the Anthropic format, cache read and write are counted separately. For GPT only the read is counted and the write is not billed separately: the first pass goes at the input price.

Astra (the 60,000 write tokens go as ordinary input, so fresh input is 160,000):

• fresh input: 160,000 × 0.62 / 1M = $0.0992;
• cache read: 1,200,000 × 0.063 / 1M = $0.0756;
• output: 20,000 × 3.05 / 1M = $0.061;
• total about $0.236.

Fable 5.1 via /v1/messages:

• fresh input: 100,000 × 7 / 1M = $0.70;
• cache read: 1,200,000 × 0.7 / 1M = $0.84;
• cache write: 60,000 × 8.75 / 1M = $0.525;
• output: 20,000 × 35 / 1M = $0.70;
• total $2.765, about $2.77.

If you call Fable 5.1 via /v1/chat/completions, all input goes at the input price: 1,360,000 × 7 / 1M = $9.52 plus output $0.70, total $10.22. That is why Claude in agent tasks needs the Anthropic format. The reasons are covered in the article on Claude via messages or chat completions→.

This is an illustration with assumed proportions, not a measurement: the real ratio of cache read to write depends on your agent. Plug in your own numbers from the usage field in responses.

Do not forget Opus 5.5

The question is often phrased as "Fable or Opus". In the Zukko table on October 18, claude-opus-5-5 costs 0.8 / 4, cache 0.08 / 1, index 58. The same agent session: 100,000 × 0.8 / 1M = $0.08; 1,200,000 × 0.08 / 1M = $0.096; 60,000 × 1 / 1M = $0.06; 20,000 × 4 / 1M = $0.08; total $0.316. So in this table Opus 5.5 is both cheaper than Fable 5.1 and higher on the index. That does not mean it is better for your task, but it is worth testing. That pair is compared in the article Fable 5.1 or Opus 5.5→.

How to decide: test on your data

The intelligence index averages many tests. Your tasks may fall where the models diverge. A simple plan:

1. Collect 30-50 real examples from your work: user requests, code snippets, documents.
2. Run them through both models with the same prompt.
3. Rate the results blind against criteria written in advance: correct or not, format respected, whether a human must fix it.
4. Compute the cost of the run from the usage field and divide by the number of accepted answers. What matters to you is the price of a correct answer, not the price of a call.

A scaffold for such a run is in the article on testing prompts→. A comparison example in code, where both models are called by one client:

import os
from openai import OpenAI

client = OpenAI(base_url="https://api.zukko.pro/v1", api_key=os.environ["ZUKKO_API_KEY"])

PRICES = {  # $ per 1M tokens, input / output, as of October 18, 2026
    "gpt-6-astra": (0.62, 3.05),
    "claude-fable-5-1": (7, 35),
}

def run(model: str, prompt: str):
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],

This code goes through /v1/chat/completions, so for Claude it shows the price without a cache discount. To evaluate agent tasks on Claude, use the Anthropic SDK with the address without /v1.

A selection rule

• You need to pay as little as possible for the top class in an agent loop: start with Astra and compare on your data.
• You need the Anthropic format specifically, tools and familiar Claude Code behavior: look at Fable 5.1 and at Opus 5.5.
• There is no quality difference on your data: choose by the cost of a correct answer.

Always check current prices through GET https://api.zukko.pro/v1/models or the "Models" page in the dashboard.

FAQ

Which of the two models is smarter?

By the intelligence index they are equal (53). The table says no more than that: compare on your own tasks.

Why is cache write so expensive for Fable 5.1?

For Claude, cache write is priced above input (8.75 versus 7), and the read is much cheaper (0.7). The benefit appears when the same prefix is read many times.

Can I call both models with one key?

Yes. The fetch_... key and balance are shared; only the model field changes. Any model can be called in the OpenAI format or in the Anthropic format.

Are they available from Russia?

Yes, through the gateway. Payment: SBP, MasterCard or crypto, top-ups from $1.

To run your own test, create a key in the Zukko dashboard.