GPT-6 Sol and GPT-6 Luna belong to the same GPT-6 family, but their jobs differ. Sol is the higher model for complex work. Luna is built to handle high volumes at far lower cost.

The trouble usually starts here:

We have a good model. Why use the cheap one?

The reverse question matters more:

Should easy work really burn the expensive model?

The price gap is bigger than you think

Using OpenAI API prices published on September 22, 2026, standard Sol and Luna prices differ a lot.

Model Input / 1M tokens Output / 1M tokens
GPT-6 Sol $2.00 $10.00
GPT-6 Luna $0.10 $0.50

On token price alone, that is about a 20x gap.

Artificial Analysis also measured cost per Intelligence Index task at max effort: about $1.06 for Sol and $0.07 for Luna.

But "then just use Luna" is also too fast a conclusion.

The quality gap is real

On Artificial Analysis Intelligence Index v4.3.2 at max effort:

GPT-6 Sol   48
GPT-6 Luna  37

Sol often has the edge on complex terminal work like Terminal-Bench and long agentic tasks. Luna wins on far lower cost and higher throughput.

So they are less direct rivals. They are models you place at different stages.

The simplest setup: Luna by default, Sol when hard

Instead of sending every request to Sol, add simple routing.

if task_is_simple:
    model = "gpt-6-luna"
else:
    model = "gpt-6-sol"

In real work, the hard part is judging task_is_simple.

You do not need a fancy classifier at first.

Start with something like this:

Luna
- Summaries
- Classification
- Short drafts
- Simple code explanations
- Structured rewrites

Sol
- Multi-file edits
- Long document analysis
- Hard debugging
- Tasks with many tool calls
- Tasks where failure is costly

A better pattern: escalate on failure

You cannot predict difficulty perfectly up front.

So this pattern is practical:

1st: run Luna
  ↓
Meet success criteria?
  ├─ Yes → done
  └─ No  → retry with Sol

Here the key is not "model choice." It is your success criteria.

For code generation, for example, you can judge like this:

success = (
    tests_passed
    and lint_passed
    and no_unexpected_files_changed
)

CodeBridge Mini Lab: find your break-even with 30 small tasks

If you want to feel model routing yourself, collect 30 small repeat requests from your day.

Example:

10: sentence summaries
10: code explanations
10: small code fixes

Run each request on both Luna and Sol, and record:

Task ID
Success
Time
Cost
Retry count

Then do not look at simple average scores. Ask:

Where is Luna success 95% or higher?
Where does only Sol succeed reliably?
What is total cost with Luna fail → Sol escalation?

That shows not "Luna is cheap" but how far you can safely use Luna.

Max effort is not always best either

Within the same model, you can tune reasoning effort.

For GPT-6 Sol in Artificial Analysis, moving from high to max raises the total score, but tokens and time per task grow a lot too.

So real routing is wider than two models:

Luna medium
→ Luna high
→ Sol medium
→ Sol high
→ Sol max

Move up one step only when you need it.

Think of this structure as model escalation.

The bottom line: find the cheapest success path, not the cheapest model

You do not need to pick Sol or Luna alone.

Many real services land here:

Luna handles easy work, and only hard work moves up to Sol.

So cost optimization is not "use the cheap model." It is set success criteria and call a stronger model only when needed.

Further reading

References

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