Claude Opus 5.5 vs GPT-6 Sol: How to Compare Cost for Business Automation
Compare Claude Opus 5.5 and GPT-6 Sol on the cost of a finished task, not the price per million tokens. Check both providers' current published prices, then run the same sample of your own work through each at the effort settings you would use, and count cost, accuracy and retries. Simple high-volume work often suits the cheaper option; reasoning-heavy or customer-facing work may justify a stronger model. Many teams use more than one model.
In short
- Take token prices from each provider's current pricing page.
- Compare the cost of a finished, correct task on your own sample.
- Effort settings, long prompts and retries change the real cost.
- Check data location and retention for regulated data.
- Many teams route simple and complex steps to different models.
Start with the published prices
Both providers publish input and output token prices, and may charge differently for long prompts, batch processing or faster tiers. Prices change, so take them from Anthropic's and OpenAI's own pricing pages on the day you decide.
Then measure cost per task
The number that matters is what one finished task costs: an invoice read, a ticket answered, a document checked. That depends on how long your inputs are, how much the model writes, the effort or reasoning setting, and how often a task has to be retried or corrected by a person.
- Pick 50 to 100 real examples of the task.
- Run them through each model at the settings you would use.
- Record tokens used, cost, time taken and how many results needed fixing.
- Divide total cost by the number of correct results.
What to compare
| Question | Why it matters |
|---|---|
| Price per input and output token | The starting point; check current pricing pages |
| Long-prompt pricing | Some providers charge more above a context size |
| Effort or reasoning settings | Higher settings cost more and take longer, but may be more accurate |
| Accuracy on your sample | Rework costs more than tokens |
| Data location and retention | Matters for regulated or personal data |
| Batch and cloud options | Batch processing and cloud marketplaces can change price and data location |
Using both
A common pattern is to send simple, high-volume steps to a cheaper model and harder steps to a stronger one. Routing is usually a small code change because both offer similar APIs, but test each step separately.
Self-hosted open models
Open models on your own hardware can make sense at very high volumes with in-house engineering capacity. Include hardware, staff time and accuracy in the comparison, not just the absence of token fees.
How AiStaffo would automate this
AiStaffo builds back-office automation such as invoice processing, ticket sorting and reconciliation on whichever model suits each step, and measures cost and accuracy on your own documents before it goes live. Book a free automation audit to see which model, or combination, fits your workload.
Questions people ask
Which model is cheaper?
Do I need to rewrite integrations to switch?
What about very long documents?
Which is better for customer-facing work?
Does data residency matter?
Book a free automation audit
Thirty minutes. We look at one process you run every week and tell you exactly what an AI worker would take off your desk, and what it would not.
