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For a Python router that balances answer quality, latency, and API spend, use the least costly model that meets your measured requirements—not a fixed assumption that one tier is best for every request. OpenAI positions GPT-5.6 Luna for cost-sensitive, high-volume work, Terra for a balance of intelligence and cost, and Sol for complex professional work. Those descriptions are useful starting points, but they do not establish which model will meet your application’s quality or speed targets.
How do Sol, Terra, and Luna differ?
The three models have distinct intended-use descriptions and different token rates. OpenAI’s model pages list these standard text prices in US dollars per 1 million tokens:
| Model | OpenAI’s positioning | Model ID | Input | Cached input | Output |
|---|---|---|---|---|---|
| GPT-5.6 Sol | Flagship for complex professional work | gpt-5.6-sol |
$4 | $0.40 | $20 |
| GPT-5.6 Terra | Balances intelligence and cost | gpt-5.6-terra |
$2 | $0.20 | $12 |
| GPT-5.6 Luna | Cost-sensitive, high-volume workloads | gpt-5.6-luna |
$0.20 | $0.02 | $1.20 |
These are the rates listed on the Sol, Terra, and Luna model pages as of October 7, 2026. OpenAI announced Terra and Luna API pricing effective July 30, 2026; the model pages are the source for the current listed rates. Prices can change, so check the live pages before estimating a deployment budget.
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Input and output rates both matter. Sol’s output rate is $20 per million tokens, for example, while Luna’s is $1.20; a workload that generates long answers can therefore have a different cost profile from one that mostly sends large prompts. Cached input has its own listed rate and should be accounted for separately when it applies.
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How should you decide which model handles a request?
Start with a workload sample rather than assigning tiers based on labels alone. OpenAI’s model-selection guidance recommends testing representative inputs and comparing the quality-and-cost tradeoff. It does not prescribe routing thresholds for these three models, and the published rates do not predict comparative quality or latency on your tasks.
- Collect representative tasks. Include routine requests, difficult cases, long-context inputs, and any failure-prone edge cases your application actually sees.
- Run the same inputs through each candidate. Keep prompts and evaluation conditions consistent so the comparison is meaningful.
- Define acceptance criteria first. Specify what counts as correct or useful for each task class, and set a latency target if response time matters.
- Record usage and outcomes. Track input and output token usage, whether cached input applies, the selected model, quality result, and latency under your application’s traffic conditions.
- Choose the least costly model that passes. If a cheaper tier misses the quality or latency bar, route that task class to a tier that meets it. Re-evaluate when traffic, prompts, or model availability changes.
For a low-cost initial policy, you might try Luna for simple, well-scoped tasks, Terra for tasks needing a middle ground, and Sol for tasks whose measured quality requirements justify its higher rates. This is an implementation hypothesis based on the published positioning and prices—not an official OpenAI routing rule or a guarantee that the assignment will work for a particular application.
How can you implement model selection in Python?
The Responses API exposes a model parameter in responses.create, which is the selection point a router can control. This minimal example maps an explicit task class to a documented model ID:
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from openai import OpenAI
client = OpenAI()
MODEL_BY_TASK = {
"routine": "gpt-5.6-luna",
"balanced": "gpt-5.6-terra",
"complex": "gpt-5.6-sol",
}
def respond(task_class: str, prompt: str):
model = MODEL_BY_TASK[task_class]
return client.responses.create(model=model, input=prompt)The mapping is only an example policy. It expects the caller to supply a valid task class; it does not classify prompts, evaluate correctness, retry failed requests, or guarantee savings. In a production implementation, validate the task class and handle API errors according to your application’s needs. Check the current model documentation and API reference for supported behavior and availability before deployment.
What should the router measure besides token price?
- Quality on your tasks: Apply the same acceptance criteria to each model. A general positioning statement is not a substitute for task-level evaluation.
- Input and output usage: Estimate or log both. Calculate standard and cached input separately where applicable, using the current rate table.
- Latency and throughput: Measure in your own request path and traffic conditions. The model descriptions provide no comparative latency benchmark for these tiers.
- Request frequency: Small per-call differences can add up in recurring automation, so include call volume when assessing cost.
- Context, output, and API needs: The three model pages list the same headline 1,050,000-token context window and 128,000-token maximum output, as well as reasoning effort choices of none, low, medium, high, xhigh, and max. Confirm live availability, supported tools, and request behavior for your account and API surface rather than assuming every capability is interchangeable.
Is service tier a substitute for choosing a model?
No. Model selection and request service tier are separate choices. The Responses API reference documents service_tier values of fast and priority for Fast-mode requests, and says the response reports the tier actually used. Treat that setting as a separate latency-related option, and verify current eligibility and pricing before relying on it. A faster service tier does not establish that a particular model will satisfy your quality target.
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