GPT-6 Astra or GPT-6 Sol: price and index differences, and what to pick

The Zukko catalogue has two GPT-6 family models that are often compared: Astra and Sol. The question of GPT-6 Astra vs GPT-6 Sol usually comes down to one thing: is it worth paying more for Astra when Sol is much cheaper. Below we compare them by input and output price, cache, context and Intelligence Index as of October 18, 2026, price one task on both models and suggest a selection rule.

A caveat up front: we do not claim that one model "writes better" than the other. What we have is price, context and the public Artificial Analysis Intelligence Index. Everything else has to be checked on your own tasks.

Parameter comparison

Zukko prices, $ per 1M tokens:

For reference, the neighbouring gpt-6.1-sol: input 0.27, output 1.32, cache read 0.013, index 52. More on it in GPT-6 Sol or GPT-6.1 Sol↗.

What the table shows:

• Context and image support are the same for Astra and Sol; price and index differ.
• The index gap is 4 points (53 versus 49). The index does not tell what that means in practice: it averages different tests.
• The price gap is large: Astra's input is 4–5 times higher, and so is the output. Exact arithmetic is below.

For GPT, Zukko accounts only for cache reads (the prompt_tokens_details.cached_tokens field). Cache writes are not billed separately: the first request goes at the input price. That is why there is no "cache write" line in the calculations below.

Pricing the same task

Take a typical request: a knowledge-base assistant. Input is 20,000 tokens (instruction, documents and the question) and output is 3,000 tokens.

Formula: fresh input × input price + cache read × read price + output × output price.

Without cache

Astra:

• Input: 20,000 × 0.62 / 1,000,000 = $0.0124
• Output: 3,000 × 3.05 / 1,000,000 = $0.00915
• Total: $0.02155

Sol:

• Input: 20,000 × 0.13 / 1,000,000 = $0.0026
• Output: 3,000 × 0.63 / 1,000,000 = $0.00189
• Total: $0.00449

The difference per request is about $0.017. Over 10,000 requests it is $215.50 for Astra against $44.90 for Sol.

With cache

Suppose 15,000 of the 20,000 input tokens come from cache (shared instruction and documents), leaving 5,000 fresh.

Astra:

• Fresh input: 5,000 × 0.62 / 1,000,000 = $0.0031
• Cache read: 15,000 × 0.063 / 1,000,000 = $0.000945
• Output: $0.00915
• Total: $0.013195

Sol:

• Fresh input: 5,000 × 0.13 / 1,000,000 = $0.00065
• Cache read: 15,000 × 0.013 / 1,000,000 = $0.000195
• Output: $0.00189
• Total: $0.002735

Both results dropped, but the order stayed the same. Output does not get cheaper with cache, so on long answers the gap between the models is determined mostly by the output price (3.05 versus 0.63).

What this is at official prices

At official prices ($10 / $50 for Astra and $2 / $10 for Sol) the same request without cache: Astra is 20,000 × 10 / 1,000,000 + 3,000 × 50 / 1,000,000 = $0.20 + $0.15 = $0.35; Sol is $0.04 + $0.03 = $0.07. Provider prices change; see current values in GET https://api.zukko.pro/v1/models or on the "Models" page in the dashboard.

A selection rule

There is no universal winner, so we suggest a four-step procedure:

1. Collect a task set. 30–50 real requests from your product and a "correct" criterion for each (a test, comparison with a reference, manual review).
2. Run it on Sol. This is the model to start with: it is cheaper.
3. Look at the errors. If Sol handles a share of tasks that is acceptable for you, stop there. If it fails the hard cases specifically, run those on Astra.
4. Price the mistake. Suppose Astra solves 10% more tasks. Is that worth the $0.017 difference per request? For paid customer support it may be, for bulk classification usually not.

Often the best option is a cascade: the cheap model first, and only the requests where a check failed are passed to Astra. The scheme is described in model cascade and escalation↗.

How to switch between models in code

The model name is the only thing that changes:

import os
from openai import OpenAI

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

MODEL = os.environ.get("MODEL", "gpt-6-sol")  # or gpt-6-astra

response = client.chat.completions.create(
    model=MODEL,
    messages=[{"role": "user", "content": "State the conclusion of the text: ..."}],
)

u = response.usage
cached = u.prompt_tokens_details.cached_tokens if u.prompt_tokens_details else 0
print(response.choices[0].message.content)
print("input:", u.prompt_tokens, "cached:", cached, "output:", u.completion_tokens)

Running MODEL=gpt-6-astra python run.py and MODEL=gpt-6-sol python run.py on the same task set gives a ready comparison of tokens and cost. The model list and parameters are described in the models documentation↗. Connecting Astra in detail is covered in GPT-6 Astra via API↗.

How to pay and get started

Create a key in the dashboard↗ and top up from $1. Access from Russia goes through the gateway; payment is by SBP (the payment page adds an 8.5% fee on top), a MasterCard card or cryptocurrency.

FAQ

Is Astra stronger than Sol?

By the Artificial Analysis Intelligence Index at October 18, 2026, Astra has 53 and Sol has 49. This is a general averaged figure. How the models perform in your case can only be learned by testing.

How many times more expensive is Astra?

Calculate using the formula for your profile. For the example above (20,000 input, 3,000 output, no cache) it is $0.02155 against $0.00449 per request.

Does the choice of model affect the request format?

No. Both models are called in the OpenAI format at https://api.zukko.pro/v1, and any model in the catalogue can also be called in the Anthropic format.

What should I pick for a first test?

Start with Sol and move up to Astra only where Sol does not pass your checks.