GPT-6 Astra vs Sol vs Luna: Pricing and When to Use Each (2026)
GPT-6 Astra costs $10/M input and $50/M output. Sol costs one-fifth as much; Luna costs one-hundredth. Start revenue workflows on Luna or Sol. Route only high-risk steps to Astra.
GPT-6 Astra Pricing: Don't Pay 100x by Default
| Model | Input | Cached input | Output | Best use |
|---|---|---|---|---|
| GPT-6 Astra | $10.00/M | $1.00/M | $50.00/M | Hard reasoning and risky actions |
| GPT-6 Sol | $2.00/M | $0.20/M | $10.00/M | General sales, marketing, and ops agents |
| GPT-6 Luna | $0.10/M | $0.01/M | $0.50/M | Extraction, routing, and high-volume tasks |
All three models support a reported 1.05 million-token context window. Each can return up to 128,000 output tokens.
Don't send one million tokens just because the context window allows it.
Requests above 272,000 input tokens cost more. Input prices double, and output prices rise by 50% for the full request.
1. GPT-6 Astra: Best for expensive mistakes
Pricing
GPT-6 Astra costs $10 per million input tokens. Cached input costs $1 per million tokens. Output costs $50 per million tokens.
That's five times GPT-6 Sol's price. It's also 100 times Luna's price.
Astra isn-is)'t expensive compared with human labor. It gets expensive when you run it 50,000 times without a good reason.
Strengths
OpenAI calls Astra its most capable broadly released model. It's built for hard reasoning, software work, computer use, and long tool chains.
Astra also reached OpenAI's Critical cybersecurity level. That matters when an agent can touch code, browsers, files, or internal systems.
The Astra system card describes extra monitoring for tool use. It can block certain unsafe actions before they run.
Limitations
Astra's biggest weakness is its price.
It also has stricter safety controls. Some Codex jobs can pause when monitoring flags a request.
That may be the right call. It can still break an unattended workflow.
Astra supports fewer low-effort settings than Sol and Luna. You can't set reasoning to `none` and use it as a cheap classifier.
Best For
Use Astra when a wrong answer creates real financial or security risk.
Examples include contract analysis, complex account planning, code changes, and actions across several systems. Astra also makes sense when Sol fails a written test set.
Don't use Astra for lead scoring, CRM cleanup, routing, or field extraction.
That's like renting a Ferrari to deliver office mail.
2. GPT-6 Sol: Best for most revenue agents
Pricing
GPT-6 Sol costs $2 per million input tokens. Cached input costs $0.20 per million tokens. Output costs $10 per million tokens.
Sol costs 20% of Astra's price across all three rates.
OpenAI released Sol on September 22, 2026. That was 19 days after Astra's September 3 release.
Strengths
Sol is the default choice for most working agents.
Microsoft recommends Sol for agents, coding, long-context analysis, and tool-based workflows. GitHub calls it a balanced choice for multi-step validation.
That fits revenue work.
Sol can research accounts, read CRM history, write outreach, and update records. It can also review its answer before taking action.
StoryPros builds agents around that full loop. The model needs to think, act, check its work, and recover from errors.
Limitations
Sol doesn't match Astra on peak capability.
One analysis of OpenAI's release charts found Astra ahead on all six comparisons. The gaps ranged from 2.9 to 9.4 points across five scored tests.
Sol scored 60.5% on OSWorld 2.0. Its GPT-5.6 predecessor reportedly scored 65.7%.
Lower cost doesn't always mean better results.
Sol still needs validation rules. A fluent answer doesn't prove that a CRM field is correct.
Best For
Start with Sol for AI BDRs, campaign agents, and revenue operations work.
It's strong enough for customer-facing writing. It's cheap enough for repeated tool calls.
Use Sol when the workflow needs judgment but follows a known process.
If Sol passes your tests, paying five times more for Astra is tool worship.
3. GPT-6 Luna: Best for high-volume grunt work
Pricing
GPT-6 Luna costs $0.10 per million input tokens. Cached input costs $0.01 per million tokens. Output costs $0.50 per million tokens.
Luna costs 1% of Astra's standard token rates.
That price makes more automation worth trying.
Strengths
Luna is built for extraction, summaries, request routing, and short answers.
Revenue teams do these tasks every day.
Use it for lead classification, job-title cleanup, call tagging, and campaign routing. Luna can also prepare data before Sol reviews it.
Aderant offers a useful example. Its Amazon Nova Lite ticket system reviewed 109 tickets with 96% routing accuracy.
The full system cost less than $30 per month. Model inference cost less than $1.
Luna isn't Nova Lite. But routine classification rarely needs the most expensive model.
Limitations
Luna falls behind Astra and Sol on hard reasoning tasks.
It's a poor default for complex negotiation emails. It's also risky for messy account research without a second check.
Cheap failures still cost money.
A Luna error can create duplicate records, bad personalization, or incorrect routing. Use schemas, confidence thresholds, and field-level checks.
Best For
Use Luna when the expected answer has a clear format.
Good outputs include labels, scores, fields, summaries, and routing decisions.
Let Luna handle volume. Use Sol for judgment.
Use Astra for the few steps where failure gets expensive.
4. What AI automation costs per workflow
Token pricing isn't your total AI automation cost per workflow.
You also pay for workflow runs, databases, logs, and outside data. Clay, Apollo, HubSpot, and email tools can cost more than the model.
These examples use four planning assumptions:
- Cached instructions are reused across every run.
- Ops costs cover n8n, storage, and basic logs.
- Data-provider and CRM fees aren't included.
- Every workflow completes in one model call.
| Workflow | Token assumption | Astra | Sol | Luna |
|---|---|---|---|---|
| Sales outreach | 2K fresh, 6K cached, 800 output | $0.076 | $0.023 | $0.011 |
| Lead enrichment | 3K fresh, 2K cached, 300 output | $0.052 | $0.014 | $0.005 |
| Deal-risk review | 20K fresh, 15K cached, 2K output | $0.335 | $0.083 | $0.023 |
| Weekly pipeline report | 100K fresh, 50K cached, 5K output | $1.400 | $0.360 | $0.113 |
Those totals include assumed ops costs of $0.005 to $0.10 per run.
Caching matters.
The outreach example uses 8,000 total input tokens. Caching 6,000 of those tokens cuts the model fee by 45%.
At 10,000 outreach runs, Astra costs about $660 before ops. Sol costs $132, while Luna costs $6.60.
Routing matters more than finding one "best" model.
Cloud buyers learned this years ago. AWS never told customers to run every job on its fastest machine.
AI buyers still make that mistake.
Use this decision tree
1. Does the task return a label, field, or short summary? Start with Luna.
2. Does it require research, writing, or several tool calls? Start with Sol.
3. Could failure create legal, security, or major financial damage? Test Astra against Sol.
4. Does Astra reduce retries enough to cover a 5x price jump? Use Astra only if the numbers say yes.
5. Does the prompt exceed 272,000 input tokens? Split the job or retrieve fewer records.
6. Can the output be checked with rules or another model? Add that check before granting tool access.
The best setup often uses all three.
Luna extracts CRM data. Sol builds the account plan. Astra reviews only high-risk deals.
That approach is called model routing.
How to access the GPT-6 models
OpenAI released Sol and Luna through its API, Codex, and ChatGPT Work. Paid Plus, Pro, Business, and Edu users received Codex access.
Free and Go users can try Luna through the desktop app.
GitHub added Sol and Luna to Copilot on September 22. Sol starts with Pro+, while Luna starts with Pro.
Microsoft Foundry offers Astra, Sol, and Luna through Standard service across 28 global regions. US and EU data zones are also listed.
Access can still depend on your account, region, and administrator settings.
FAQ
How much does Astra AI cost?
GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens. Cached input costs $1 per million tokens under standard short-context pricing.
Requests above 272,000 input tokens cost more.
Is GPT-6 Astra out?
Yes. OpenAI published the GPT-6 Astra system card on September 3, 2026.
Sol and Luna followed on September 22, 2026.
How do I use GPT-6 Astra?
GPT-6 Astra is available through supported OpenAI accounts and Microsoft Foundry. Access may require a paid account, administrator approval, or availability in your region.
For revenue workflows, connect it through an agent builder such as n8n. Add tool permissions, logs, and validation before allowing it to take action.
Which is better: Astra vs Sol vs Luna?
Astra is strongest for difficult reasoning and high-risk actions. Sol is the best default for most revenue agents. Luna works best for high-volume extraction and routing.
Choose based on measured task success, not benchmark headlines.
When should a revenue team avoid Astra?
Avoid Astra for lead cleanup, classification, summaries, routing, and routine outreach. Sol or Luna can handle those jobs at 20% or 1% of Astra's token price.
Use Astra only when tests show it prevents costly failures.
Related Reading
How much does GPT-6 Astra cost?
GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens. Cached input drops to $1 per million tokens. Requests above 272,000 input tokens trigger higher rates across the full request.
What is the price difference between GPT-6 Astra, Sol, and Luna?
Sol costs 20% of Astra's price: $2 per million input tokens and $10 per million output tokens. Luna costs 1% of Astra's price: $0.10 per million input tokens and $0.50 per million output tokens. At 10,000 outreach runs, Astra costs roughly $660 versus $132 for Sol and $6.60 for Luna.
When should a revenue team use GPT-6 Luna instead of Astra?
Luna fits lead classification, CRM field cleanup, call tagging, and routing decisions. It costs $0.10 per million input tokens, compared to Astra's $10. Use Luna when the expected output is a label, score, or short summary with a clear format.