GPT-6 Sol vs Claude Opus 5: Which AI Model Should You Use in 2026?
Compare verified GPT-6 Luna, Sol, Astra and Claude Opus 5 API prices with a worked cost example and a five-task decision test. Corrected September 26, 2026.
Quick answer
For API users paying per token, GPT-6 Sol is the practical starting point for coding and multi-step work, GPT-6 Luna is the low-cost option for simple, repeatable tasks, and Claude Opus 5 is worth testing on demanding jobs where it produces a better finished result. GPT-6 Astra is the premium OpenAI option. These are recommendations to test against your own tasks, not results of NOWSCOPE benchmark testing.
Correction, checked September 26, 2026: an earlier version of this article referred to "Claude Opus 5.5" and a $4/$20 API rate. We could not verify that model or rate in Anthropic's official materials. Anthropic identifies Claude Opus 5 at $5 per million input tokens and $25 per million output tokens. The title and comparison below have been corrected; this article's existing URL is retained.
The verified API prices
The table uses standard processing, short-context prices in USD per one million tokens. Cached input, long context, batch, fast mode and regional processing can have different rates. Checked September 26, 2026.
| Model | Input | Output | Practical starting use |
|---|---|---|---|
| GPT-6 Luna | $0.10 | $0.50 | Simple extraction or classification |
| GPT-6 Sol | $2 | $10 | Everyday coding and multi-step tasks |
| GPT-6 Astra | $10 | $50 | Hard tasks that justify a premium |
| Claude Opus 5 | $5 | $25 | Complex Claude workflows to benchmark |
Sources: OpenAI API pricing and Anthropic's Claude Opus 5 announcement. These are API usage rates, not monthly ChatGPT or Claude subscription fees. Check the pricing pages before budgeting a production workload.
What would a real workload cost?
Suppose a task uses 2 million input tokens and 500,000 output tokens in one month, with standard short-context processing and no caching. Multiply each model's input rate by 2 and its output rate by 0.5:
| Model | Input cost | Output cost | Total |
|---|---|---|---|
| Luna | $0.20 | $0.25 | $0.45 |
| Sol | $4 | $5 | $9 |
| Astra | $20 | $25 | $45 |
| Opus 5 | $10 | $12.50 | $22.50 |
Those are arithmetic examples, not a claim that each model will finish the same work with the same number of tokens. A model that requires repeated attempts or more reasoning tokens may cost more per successful task. Tool calls and other billable features can change your actual invoice.
Which should you choose for coding?
Start by defining a task you can judge: a bug fix with passing tests, a code review with known defects, or a small feature with clear acceptance criteria. Run the same instructions and tool access on Sol and Opus 5. Compare the final result, total tokens, elapsed time and the human time needed to repair errors. Use the cheaper model if both pass; pay more only if the stronger result saves more time or reduces meaningful risk.
OpenAI lists GPT-6 Sol among its lower-priced models. Anthropic describes Opus 5 as designed for long-running and coding work, but its published performance figures are vendor benchmarks, not a head-to-head NOWSCOPE test. Neither vendor's marketing proves which model will be best on your repository.
For straightforward text transformations, use Luna as a first pass and validate a sample of outputs. For difficult architectural or research work, compare Astra with the other candidates using the same acceptance test. Do not send sensitive business or customer data to any provider before checking your own privacy and data-processing requirements.
A simple routing rule for teams
- Put repeatable, low-risk tasks on Luna only after a sample meets your accuracy standard.
- Route coding and multi-step work to Sol by default if it passes your tests.
- Escalate failures and unusually hard tasks to Opus 5 or Astra; record which actually succeeds.
- Recalculate cost per accepted result each month, including retries and human review time.
The break-even question is concrete: if Opus 5 costs $13.50 more than Sol in the example above, does it save more than $13.50 of work or avoid an error worth more than that? If not, Sol is the economical choice for that workload. If you are choosing a consumer subscription instead of an API, compare each product's current plan price and usage limits separately; the token table does not answer that decision.
What should you do now?
If you already pay for an AI service, do not switch from a headline alone. Select five representative tasks, keep your success criteria identical, run them across the models you can access, and record successful outcomes and total cost. Use the model that reliably completes your work for the lowest overall cost. Recheck rates before deployment because model prices and plan access can change.
Bottom line
Luna wins on listed token price, Sol is the sensible OpenAI default for substantial daily work, and Astra and Opus 5 deserve a paid trial only where their actual results earn the premium. We have not independently benchmarked these models; your own accepted-output rate is the deciding number.
