Claude Opus 5.5 vs GPT-6.1 Sol
The two models most teams should actually be choosing between at the high end. Opus 5.5 is the stronger all-round worker on professional knowledge work and long coding; GPT-6.1 Sol gets close to OpenAI’s flagship on agent tasks at a lower rate. If cost per task decides it, start with Sol; if quality on messy, judgement-heavy work decides it, start with Opus.
Choose Claude Opus 5.5 if
- Your work is reports, analysis and other professional tasks — Opus scored highest of Anthropic’s models on GDPval-AA.
- You need to send PDFs directly.
- You want Anthropic’s recommended default for long coding sessions and code review.
Choose GPT-6.1 Sol if
- Cost per task matters: OpenAI says Sol matches Astra on DeepSWE at about a fifth of the cost.
- Your agents operate software — Sol lands within about two points of Astra on OSWorld.
- You already build on OpenAI models.
Side by side
| Claude Opus 5.5 | GPT-6.1 Sol | |
|---|---|---|
| Made by | Anthropic | OpenAI |
| Released | September 2026 | September 2026 |
| Input price, per 1M tokens | $4.60 | $2.30 |
| Output price, per 1M tokens | $23.00 | $11.50 |
| Cached input, per 1M tokens | $0.23 | $0.115 |
| 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 | 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 2.0× 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. Trying the other one is a one-word change:
client.chat.completions.create(model="claude-opus-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.