Home → Guides → GPT-6 Sol vs GPT-6 Astra: what the 5x price gap actually buys
Two models, one family, five times apart on price. The interesting question is not which is better — it is which parts of a pipeline actually need the expensive one, and what happens to the bill when a prompt crosses 272,000 tokens.
From OpenAI's pricing page, per million tokens:
| Model | Input | Cached input | Cache write | Output |
|---|---|---|---|---|
gpt-6-sol | $2.00 | $0.20 | $2.50 | $10.00 |
gpt-6-astra | $10.00 | $1.00 | $12.50 | $50.00 |
Above 272,000 input tokens both are repriced for the whole call: Sol to $4.00 / $15.00, Astra to $20.00 / $75.00.
What the same models cost here, taken live from the current rates:
| Model | Input / 1M | Output / 1M |
|---|---|---|
gpt-6-sol | $0.400 | $2.00 |
gpt-6-astra | $2.00 | $10.00 |
Summarising a 100,000-token document into a 1,000-token answer:
gpt-6-astra: 100,000 × $10 + 1,000 × $50 per million —
$1.05gpt-6-sol: 100,000 × $2 + 1,000 × $10 per million —
$0.21Run that once a day and the difference is loose change. Run it on every document in a queue of ten thousand and it is $10,500 against $2,100. The decision only matters at volume, which is exactly where people tend to keep the flagship out of habit.
Crossing the threshold reprices the entire request, not the tokens past the line. On Sol, with a 2,000-token answer:
Eleven percent more input, 120% more cost. If a workload sits anywhere near 272k — long documents, a repository in context, an agent that keeps appending history — trimming to stay under the line saves more than any model swap will.
Cached input is a tenth of the input price on both models: $0.20 on Sol, $1.00 on Astra. A cache write costs more than fresh input — $2.50 and $12.50 respectively — so a loop that invalidates its cache every turn pays the premium repeatedly, and pays it five times over on Astra.
Keep the volatile part of the prompt at the end. A timestamp or a reshuffled tool list near the top invalidates everything after it.
The useful division is between deciding and doing. Classification, extraction, routing, reformatting, tagging and short summaries are high-volume and low-judgement — that is where the token count lives, and Sol handles that shape of work at a fifth of the price. Reserve Astra for the final reasoning step and anything a user reads directly.
Because routine calls dominate the count, a blended pipeline lands much closer to Sol's price than to Astra's. That is usually a larger saving than switching providers.
We are not publishing quality benchmarks here — run your own twenty prompts through both and read the outputs. Per-model rates in 14 languages are on the gpt-6-sol and gpt-6-astra pricing pages.
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Read the 30-second quickstartPrices quoted on this page are regenerated automatically from live billing data. Third-party terms are quoted from that party's own published documentation.