GPT-6 Sol vs Luna vs Claude Opus 5.5: Cost and Fit
Compare GPT-6 Sol, Luna and Claude Opus 5.5 API costs. Use worked examples and an evaluation worksheet to choose a model for your actual tasks.
By Hardeep ·
Quick answer: For GPT-6 Sol vs Luna vs Claude Opus 5.5, choose by the cost of a correct, usable result. Luna has the lowest listed token prices in this comparison. Sol and Opus are worth evaluating when work needs more judgment. Run the same tasks on each before making one your default.
This guide is for developers, small software teams and people evaluating AI for recurring research or document work. It compares verified pricing with an original evaluation worksheet. It is not a hands-on benchmark, and it does not declare a universal winner.
What does the video actually compare?
Vaibhav Sisinty's AI update video discusses GPT-6 Sol and Luna around 04:08. Its later comparison, beginning around 15:45, uses GPT-5.6 Sol, not GPT-6 Sol, against Claude Opus 5.5. Do not transfer that comparison's conclusions to a different model generation.
The video is the starting point for this article. The pricing below comes from vendor documentation checked on September 29, 2026. The exercises and cost examples are proposed methods, not results obtained by AiThinkArticles.
How do their API prices compare?
These are USD prices per one million text tokens for standard processing, using OpenAI's short-context rates. They are API charges, not ChatGPT or Claude subscription prices.
| Model | Input | Cached input/read | Output |
|---|---|---|---|
| GPT-6 Luna | $0.10 | $0.01 | $0.50 |
| GPT-6 Sol | $2.00 | $0.20 | $10.00 |
| Claude Opus 5.5 | $4.00 | $0.20 | $20.00 |
Sources: GPT-6 Luna model documentation, GPT-6 Sol model documentation, and Anthropic's Opus 5.5 announcement. Cache creation, long-context processing, tools and other service options can change the bill. A repeated prompt does not automatically qualify for a cache discount.
OpenAI describes Luna as a model for focused, high-volume tasks. Its Sol and Luna announcement positions both as more affordable ways to use advances from Astra. Anthropic describes Opus 5.5 as a model for complex coding, computer use and knowledge work. These are vendor descriptions; they do not establish which model will succeed on your data.
What would a small request cost?
For an illustrative request with 10,000 uncached input tokens and 2,000 billable output tokens, the text-token calculation is:
cost = input tokens ÷ 1,000,000 × input rate + output tokens ÷ 1,000,000 × output rate
| Model | Input cost | Output cost | Total per request | 1,000 identical requests |
|---|---|---|---|---|
| GPT-6 Luna | $0.001 | $0.001 | $0.002 | $2 |
| GPT-6 Sol | $0.020 | $0.020 | $0.040 | $40 |
| Claude Opus 5.5 | $0.040 | $0.040 | $0.080 | $80 |
This is arithmetic, not a monthly quote. It excludes tool calls, cache writes, taxes, retries and human review. Use the provider's recorded billable usage, including any billed reasoning tokens, rather than counting only the words displayed in an answer.
The useful question comes next: how many of those requests produce something you can accept?
Measure cost per accepted result
A low request price can lose its advantage when an answer needs extensive repair. Track both the invoice cost and the time your team spends checking output.
For example, imagine 100 requests cost $4 and 80 pass your rubric without repair. The API cost per accepted result is $4 ÷ 80, or $0.05. If the complete batch also needs 30 minutes of review at an assumed $30 an hour, its combined cost per accepted result is $19 ÷ 80, or about $0.238. These are invented inputs, not measured model results.
Use one accounting rule across candidates. Count failed attempts and retries even if their output is discarded. Track the eventual outcome of rejected work separately so a model does not appear cheaper merely because someone else finishes its mistakes.
Build a comparison that answers your real question
Choose tasks from work you already do. Before running them, write down what an acceptable answer must contain and what would make it fail. A polished layout should not compensate for an incorrect calculation.
| Task | Give each candidate | Check before accepting |
|---|---|---|
| Invoice extraction | The same synthetic invoices, including missing fields | Exact amounts, correct currency, no invented values |
| Research brief | A fixed set of source documents | Every factual claim traceable to a source; uncertainties retained |
| Bug repair | The same small repository and failing test | The bug is fixed without breaking the existing checks |
| Spreadsheet report | A fixed dataset with known totals | Formulas recalculate correctly and totals reconcile |
| Support draft | A policy document and ambiguous customer request | Policy accuracy, useful clarification, no unsupported promise |
Keep task inputs, tools and output requirements consistent. Record the exact model, effort setting, date and token usage. Matching the word “medium” across providers does not prove equal compute, so report settings without claiming they are equivalent.
Run important cases more than once. Report the number of trials and variation, rather than treating one attractive answer as a reliable success rate. Include difficult cases from your own workflow, such as incomplete inputs or contradictory documents.
When should you route work between models?
A routing experiment can start with a cheaper candidate for a narrow task, then escalate failures to another model or a person. Only use this arrangement when you can recognize failure reliably.
For an invoice workflow, require the expected fields, valid numbers and a matching total before accepting output. Send missing or inconsistent records for review. A confident explanation from the model is not a substitute for those checks.
For an open-ended strategy memo, correctness is harder to automate. A stronger model may still need a reviewer who knows the business. Record that review cost instead of assuming the model can approve its own work.
Questions before switching
Is GPT-6 Luna always the best choice because it is cheaper?
No. Its listed token rates are lower, but the relevant cost includes failed work and review. Test it on the particular task and use it where it meets your acceptance rules.
Does the video's Opus comparison prove it beats GPT-6 Sol?
No. The later comparison identifies GPT-5.6 Sol. It does not establish a GPT-6 Sol result, and a creator's demonstration is not a benchmark of your workflow.
Can I compare a subscription price with these API numbers?
Not directly. Subscriptions and API billing have different limits and inclusions. Compare plans separately from usage-based API charges.
What should a small team test first?
Start with a frequent task whose output can be checked cheaply. Keep a manual fallback, measure accepted results and review time, and expand only when the evidence supports it.
Keep a decision record
Save the task, selected model, settings, acceptance rule, cost per accepted result and review date. Revisit the decision when pricing, models or your workload change. That record is more useful than a permanent “best AI” label.
Related reading: AI agent evaluation and our editorial standards.
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