Claude Sonnet 5.5 vs GPT-6.1 Sol
The same tier from two labs, released a day apart. Sonnet 5.5 is the faster everyday worker and posted the best Terminal-Bench 4.0 score in Anthropic’s lineup; GPT-6.1 Sol is closer to a flagship on agents that operate software. Both are brand new, so test them on your own prompts.
Choose Claude Sonnet 5.5 if
- Everyday coding, bug fixing and command-line agents — Sonnet scored 70.6% on Terminal-Bench 4.0.
- Documents, slides and spreadsheets.
- Thinking you can dial back for speed.
Choose GPT-6.1 Sol if
- Agents that click through web pages and desktop apps.
- Science and terminal work, which OpenAI benchmarks at a low cost per task.
- The closest thing to Astra at a mid-tier rate.
Side by side
| Claude Sonnet 5.5 | GPT-6.1 Sol | |
|---|---|---|
| Made by | Anthropic | OpenAI |
| Released | September 2026 | September 2026 |
| Input price, per 1M tokens | A$3.03 | A$3.03 |
| Output price, per 1M tokens | A$15.14 | A$15.14 |
| Cached input, per 1M tokens | A$0.3028 | A$0.1514 |
| Batch (about half price) | Yes | Yes |
| Context window | 1M tokens | 1.05M tokens |
| Longest answer | 128K tokens | 128K tokens |
| Reads | Text, Images, PDFs | Text, Images |
| Thinks before answering | Yes, adjustable | Always on |
| Open weights | No | No |
Prices are Roar AI’s published rates, live from our price list. Specs are each lab’s own published figures. Prices are our published US dollar rates converted at A$1.47 to the dollar, our rate for October — the same rate your card is charged.
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.
The two cost about the same for this job. 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, paid in your local currency. Trying the other one is a one-word change:
client.chat.completions.create(model="claude-sonnet-5-5", …) client.chat.completions.create(model="gpt-6.1-sol", …)
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.
Try both
on one key
Send the same prompts to each and compare the answers on your own work.