GPT-5.6 Sol vs Terra vs Luna: Which Model Should You Use?

The biggest AI model is not automatically the best choice for every task. A routine transformation may need speed and low cost. A difficult professional problem may justify more model capability and review time.

OpenAI's current model catalog describes the GPT-5.6 family this way:

• GPT-5.6 Luna is optimized for cost-sensitive, high-volume workloads.
• GPT-5.6 Terra balances intelligence and cost.
• GPT-5.6 Sol is the flagship option for complex professional work, reasoning, and coding.

Check the official OpenAI model catalog before implementation because access, pricing, and model guidance can change.

The safest selection rule is simple: start with the least expensive model that can pass your real quality check, then move up only when the result fails.

Use Luna for routine, high-volume work

Start with Luna when the task is predictable, repeated often, and easy to verify.

Examples include:

• Reformatting structured text
• Classifying support messages into known categories
• Extracting fields from consistent documents
• Rewriting short text to match a fixed style
• Producing first-pass summaries for human review

Do not choose Luna only because the task sounds simple. Test it with messy, incomplete, and unusual examples from the real workload.

Use Terra for balanced everyday work

Terra is a practical starting point when the task needs more judgment than a routine transformation but still runs frequently enough for cost and speed to matter.

Examples include:

• Drafting from a detailed brief
• Comparing options with stated criteria
• Analyzing documents and identifying themes
• Planning a project from known requirements
• Creating a first version of a report or proposal

Terra is often the middle option for workflows where Luna misses nuance but Sol does not produce a measured improvement worth the added cost.

Use Sol for complex, high-value work

Use Sol when the task is difficult, ambiguous, multi-step, or expensive to get wrong.

Examples include:

• Complex professional analysis
• Difficult coding and architecture work
• Research that must connect evidence across sources
• High-value planning with many constraints
• Reviewing another model's output for subtle failure modes

Sol still needs clear instructions, reliable evidence, and human review. A more capable model does not remove the need to check important claims or decisions.

Build a task-fit test

Do not compare models with one impressive demo. Create a small evaluation set from representative tasks.

Include:

1. Five normal examples
2. Two messy or incomplete examples
3. Two edge cases
4. One example where a wrong answer would matter

Remove private information or use approved test data.

Score every result against the same rubric:

Criterion	Question
Accuracy	Are the claims and extracted details correct?
Completeness	Did the answer include every required field?
Instruction following	Did it obey the constraints and format?
Evidence	Are important claims supported or marked uncertain?
Review effort	How much human correction was required?
Speed	Was the response fast enough for the workflow?
Cost	Is the total cost acceptable at the expected volume?

Set a pass threshold before comparing results. For example, a customer-facing draft may require every factual field to be correct and no unsupported claims.

Copy-and-paste evaluation prompt

Evaluate the model output below against this task contract.

Goal: [What the task must accomplish]
Required context: [What information the answer must use]
Constraints: [Accuracy, safety, privacy, length, and tone rules]
Required format: [Fields or structure]
Pass conditions: [The minimum acceptable result]

Return a table with:
- Criterion
- Pass or fail
- Evidence from the output
- Required correction

Do not reward confident wording. Mark unsupported claims as failures.

Model output:
[Paste the output]

Use a human reviewer or a trusted reference answer for important evaluations. A model grading another model can help organize the review, but it should not be the only judge.

Route tasks instead of choosing one model forever

A useful production workflow can route tasks by difficulty:

1. Send routine requests to Luna.
2. Check the output against automatic or human quality rules.
3. Escalate failed or high-risk cases to Terra.
4. Use Sol for the hardest cases or final review where testing proves it helps.

Example routing brief:

Classify this task before answering.

Routine: predictable transformation with a clear answer and an automatic check.
Judgment: requires comparison, drafting, or interpretation using supplied context.
Complex: ambiguous, multi-step, high-risk, or requires connecting evidence across sources.

Return the classification, the reason, the evidence required, and the success criteria. Do not complete the task yet.

The routing label should inform your application or human operator. Do not let the model silently increase cost or take a high-impact action without the controls your workflow requires.

Common model-selection mistakes

• Choosing Sol for every request without measuring whether it improves the outcome
• Choosing Luna only by price and ignoring correction time
• Testing with one clean example instead of representative work
• Comparing outputs with different prompts or different success criteria
• Ignoring latency, review effort, and failure risk
• Assuming model access or pricing is the same in every account
• Treating a fluent answer as proof of accuracy

Practical decision checklist

Use this order:

1. Define the task and the cost of failure.
2. Write measurable pass conditions.
3. Build a small representative evaluation set.
4. Test Luna first for routine work.
5. Compare Terra when Luna fails quality or nuance.
6. Compare Sol for complex or high-value work.
7. Measure quality, speed, total cost, and human correction time.
8. Route only the tasks that benefit from the stronger model.
9. Re-test after a major model or prompt change.

The best model is the least expensive option that reliably meets the task's real quality bar. Use evidence from your workflow instead of choosing by model size alone.