GPT-6.1 Sol vs GPT-6 Astra
OpenAI’s own numbers make the case for Sol: it matches Astra on DeepSWE and lands within about two points on OSWorld, at a fraction of the cost per task. Astra is for the hardest science and maths, where it still leads.
Choose GPT-6.1 Sol if
- Production agents where cost per task matters.
- Coding and computer use.
- Faster answers.
Choose GPT-6 Astra if
- The hardest science problems — Astra still leads Terminal-Bench Science.
- Advanced maths.
- Cases where a cheaper model’s answer is not good enough.
Side by side
| GPT-6.1 Sol | GPT-6 Astra | |
|---|---|---|
| Made by | OpenAI | OpenAI |
| Released | September 2026 | September 2026 |
| Input price, per 1M tokens | $2.30 | $11.50 |
| Output price, per 1M tokens | $11.50 | $57.50 |
| Cached input, per 1M tokens | $0.115 | $1.15 |
| Batch (about half price) | Yes | Yes |
| Context window | 1.05M tokens | 1.05M tokens |
| Longest answer | 128K tokens | 128K tokens |
| Reads | Text, Images | Text, Images |
| Thinks before answering | Always on | Always on |
| Open weights | No | No |
| Runs in Sri Lanka | No | No |
Prices are Roar AI’s published rates, live from our price list. Specs are each lab’s own published figures.
What a real job costs
Take 1,000 requests, each about 3,000 tokens in and 1,000 out — a long prompt and a page of answer.
GPT-6.1 Sol does the same job for 5.0× less. Models that think before answering spend extra output tokens doing it, so on hard prompts the real gap can be wider than the rates suggest.
Use both, switch any time
On Roar AI both are on the same key and the same monthly invoice, billed in rupees. Trying the other one is a one-word change:
client.chat.completions.create(model="gpt-6.1-sol", …) client.chat.completions.create(model="gpt-6-astra", …)
Send the same prompts to both for a day and compare the answers on your own work — that settles it faster than any benchmark. Read the docs.