GPT-6 Sol vs Luna vs Astra: choose a model for your task
Compare GPT-6 model roles and direct API costs, then use an acceptance checklist to choose Luna, Sol or Astra without assuming a universal winner.
Ofox 
Start with the least expensive GPT-6 model that reliably meets your task’s acceptance criteria. Luna is the natural candidate for focused, repeatable work; Sol for more demanding coding and agent workflows; Astra when the task justifies evaluating the family’s most capable option. Those are starting hypotheses, not a performance ranking for your own workload.
This guide uses OpenAI’s family announcement, model guidance and pricing documentation, checked September 23, 2026. It does not report a new head-to-head model test. Where we recommend an evaluation order, that is an editorial decision based on task risk and the published product positioning.
Match the model to the decision you need
| Your workload | Candidate to evaluate first | Acceptance check |
|---|---|---|
| Repeated extraction or classification with clear rules | Luna | Schema validity plus correctness against the source |
| A bounded code change requiring repository context | Sol | Reproduced issue fixed, relevant checks passed, diff within scope |
| A difficult multi-step problem where failure is expensive | Astra | Independent review of the result and its supporting evidence |
| A mixed queue | Start with separate task categories | Measure each category before designing escalation rules |
A small model that needs repeated correction can cost more in total than its rate suggests. A larger model that answers a trivial task at a premium is not automatically a better operating choice. Include human repair time in the decision, even if you record it separately from API spend.
Compare rates under one set of conditions
These are direct OpenAI Standard rates for input at or below 272K tokens, in USD per million tokens. They do not describe ChatGPT subscription allowances or Ofox provider quotes.
| Model | Ordinary input | Cache read | Cache write | Output |
|---|---|---|---|---|
| GPT-6 Luna | $0.10 | $0.01 | $0.125 | $0.50 |
| GPT-6 Sol | $2 | $0.20 | $2.50 | $10 |
| GPT-6 Astra | $10 | $1 | $12.50 | $50 |
At these rates, Sol’s input and output prices are 20 times Luna’s; Astra’s are five times Sol’s. That is a comparison of specific price fields under specified conditions. It is not a prediction of a 20-fold or five-fold difference in completed-task cost.
Use the Sol pricing guide and Luna pricing guide for cache accounting, processing modes and long inputs. Do not carry a short-context rate into a long-context request or add cache-write charges on top of the same ordinary-input tokens.
A worked cost example, not a benchmark
Assume each model consumes 20,000 ordinary input tokens and 5,000 billed output tokens, with no cache or tool charges. Luna costs $0.0045, Sol $0.09 and Astra $0.45. The downloadable worksheet records this fixed-token example.
Now suppose an inexpensive candidate fails a check and you run a second model to repair the result. The full workflow includes both attempts, any expanded prompt, extra tool work and your review. The initial model’s low price does not erase the first attempt’s cost.
Conversely, do not assume a failure means you must always jump to the highest tier. The task might need clearer input, an appropriate tool, a better acceptance test or a supported request format. Diagnose the cause before turning one example into a permanent routing policy.
Reasoning effort is another variable
Sol and Luna support none, low, medium, high, xhigh and max in the current documentation. Astra has its own supported range; do not copy unsupported settings between models. Identical effort labels across models do not certify equal compute or equal output budgets.
For tools and reasoning, follow the Responses compatibility guide. A protocol mismatch can look like poor model behavior if the tool never runs. Keep the model, endpoint, effort and tool permissions in the evaluation record.
Change one variable at a time when the goal is to understand a difference. If you switch model, provider, effort and repository instructions together, a better result cannot be attributed to the model alone.
Use an acceptance sheet before comparing answers
Download the blank evaluation sheet. For a coding task, record the starting commit, allowed files, expected behavior and the verification command before running either candidate. For extraction, retain the source record and expected fields.
Inspect correctness, omissions, scope and manual repair. Keep unsuccessful runs in the ledger. A polished explanation is not a substitute for working code; valid JSON is not proof that the values are right. Publish or share results with the settings and limitations attached.
The access guide helps locate the correct product entry point. If the real choice is between vendors, use the Sol versus Opus 5.5 comparison and apply the same acceptance criteria. Historical GPT-5.6 tier comparisons describe the older generation and should not be silently relabeled as GPT-6 evidence.
Frequently Asked Questions
- Is Sol always better value than Astra?
- No. Its lower token rates are useful, but value depends on accepted results, retries, tools and review work for the task.
- Can Luna replace Sol for every task?
- The documentation does not establish that. Evaluate focused tasks against explicit criteria and retain a route for work that fails those checks.
- Are the example costs measured runs?
- No. They hold input and output token counts fixed to demonstrate rate differences and exclude cache, tools and retries.
