Skip to content
Finitizer
Compare these two on your own workload

Published rates rank differently once prompt caching, batch discounts and your own input-to-output ratio are applied. Run both against your numbers.

GPT-6 Astra vs GPT-6.1 Sol

OpenAI pitches Sol as near-Astra performance at a fifth of the price.

Prices verified 12 Oct 2026
Input / 1M$10.00
Output / 1M$50.00
Cached input / 1M$1.00
Batch50% off
Context1.1M tokens
Blended (3:1)$20.00
Input / 1M$2.00
Output / 1M$10.00
Cached input / 1M$0.100
Batch50% off
Context1.1M tokens
Blended (3:1)$4.00

Monthly cost by workload shape

The same volume costs very different amounts depending on the shape of the traffic. No caching or batch applied.

WorkloadGPT-6 AstraGPT-6.1 SolDifference
Customer support chatbot
50K/day · 800 in / 300 out
$35.0K$7,001.20GPT-6.1 Solsaves $28.0KModel it →
RAG / search answers
20K/day · 3000 in / 400 out
$30.4K$6,088.00GPT-6.1 Solsaves $24.4KModel it →
Agent / tool-use loop
8.0K/day · 2500 in / 600 out
$13.4K$2,678.72GPT-6.1 Solsaves $10.7KModel it →
Batch summarization
100K/day · 5000 in / 500 out
$228.3K$45.7KGPT-6.1 Solsaves $182.6KModel it →
Code assistant
15K/day · 2000 in / 800 out
$27.4K$5,479.20GPT-6.1 Solsaves $21.9KModel it →

Frequently asked questions

Which is cheaper, GPT-6 Astra or GPT-6.1 Sol?

At a typical 3:1 input-to-output ratio, GPT-6.1 Sol is cheaper: $4.00 blended per 1M tokens against $20.00. That holds at every input:output ratio, one model is cheaper on both rates.

What is the price difference between GPT-6 Astra and GPT-6.1 Sol?

GPT-6 Astra: $10.00 input, $50.00 output per 1M tokens. GPT-6.1 Sol: $2.00 input, $10.00 output. That is 5.0x on input and 5.0x on output.

Does prompt caching change the answer?

GPT-6 Astra caches input at $1.00/1M and GPT-6.1 Sol caches at $0.100/1M. For input-heavy workloads with repeated context, caching can matter more than the base rate difference, model both in the calculator rather than comparing rate cards.

This page compares published prices only. It makes no claim about which model performs better on any task; that depends entirely on your evaluations.

Rate cards decide nothing on their own.

Most teams run several models at once. Finitizer Tokenomics shows what each one actually costs you, read from your own bills and exports, which workloads could test a smaller model, what they would cost on an open-weight model, and, for models you serve yourself, the cost per token beside the API price. A routing decision starts from your spend rather than a price page.

See how Finitizer works