The useful question about GPT-5.6 is not “Which model is best?” It is “Which part of this workflow actually needs Sol?”
OpenAI now positions the family in three tiers:
| Model | API ID | Input / 1M tokens | Output / 1M tokens | Sensible default |
|---|---|---|---|---|
| Sol | gpt-5.6-sol |
$4.00 | $20.00 | Ambiguous, high-consequence reasoning |
| Terra | gpt-5.6-terra |
$2.00 | $12.00 | Everyday production work |
| Luna | gpt-5.6-luna |
$0.20 | $1.20 | High-volume, well-specified jobs |
These are the API prices shown in OpenAI's model documentation on August 28, 2026. They can change, so keep them in configuration rather than burying them in application code.
The price gap is large enough to change architecture
Consider a job that consumes 8,000 input tokens and produces 1,500 output tokens.
- Sol: about $0.062 per task
- Terra: about $0.034 per task
- Luna: about $0.0034 per task
At 100,000 tasks, that becomes roughly $6,200, $3,400, or $340.
The arithmetic is simple. The harder part is deciding which requests deserve the expensive path.
For this workflow, CometAPI can serve as the shared API layer for switching between model tiers. That does not remove the need to evaluate each model on real tasks; it keeps routing, usage tracking, and fallback logic around one client instead of several provider-specific integrations. The CometAPI documentation lists the current model catalog and request formats.
I would not route by prompt length alone. A short request can hide a hard decision, while a long document may only need extraction. Route by the kind of uncertainty the model must resolve.
A small routing rule that is easy to audit
from dataclasses import dataclass
@dataclass
class Task:
kind: str
ambiguity: str = "low"
consequence: str = "low"
needs_final_review: bool = False
def choose_model(task: Task) -> str:
if task.consequence == "high" or task.needs_final_review:
return "gpt-5.6-sol"
if task.ambiguity == "high" or task.kind in {
"debugging",
"planning",
"multi_step_analysis",
}:
return "gpt-5.6-terra"
return "gpt-5.6-luna"
This is intentionally boring. The rule is visible, testable, and easy to replace once evaluation data arrives.
The application call stays ordinary:
from openai import OpenAI
client = OpenAI()
task = Task(kind="classification")
response = client.responses.create(
model=choose_model(task),
input="Classify this support request as billing, technical, or account access.",
)
print(response.output_text)
Where each model fits in a real pipeline
Luna: repeated work with a clear acceptance test
Good candidates include classification, extraction, normalization, short summaries, formatting, routing, and first-pass transformations.
The common feature is not that these tasks are “easy.” It is that success can be checked cheaply. If an extraction must match a schema, validation code can catch failures.
Terra: the default when context and judgment both matter
Terra makes sense for ordinary coding assistance, document analysis, planning with known constraints, support responses that require interpretation, and multi-step work where Luna's failure rate becomes expensive.
If I had to choose one model before running an evaluation, Terra would be the least surprising starting point.
Sol: use it where a bad decision creates follow-up work
Sol is easier to justify for ambiguous debugging, architecture decisions, difficult research synthesis, final review, and tasks where one overlooked constraint can invalidate the result.
The important phrase is “easier to justify,” not “always better.” A stronger model can still waste money on a task that a validator and Luna could finish reliably.
A better pattern than one model per application
For longer workflows, I prefer stage-level routing:
- Luna classifies the request and extracts structured facts.
- Terra creates the plan or draft.
- Sol handles unresolved ambiguity or reviews a high-consequence result.
That design also makes evaluation cleaner. Instead of asking whether one model is globally better, you can measure acceptance rate, retries, latency, and cost at each stage.
The metric worth tracking is not cost per token. It is:
cost per accepted task = total model cost / outputs that pass review
Cheap tokens are not cheap when they create three retries. Expensive tokens are not expensive when they prevent an hour of rework.
What I would measure before changing traffic
Start with 30 to 50 representative tasks. For each model, record:
- acceptance without editing;
- number of retries;
- input and output tokens;
- latency;
- validation failures;
- human review time.
Then route the stable majority to the least expensive model that passes, keeping an escalation path for the awkward cases.
That is less exciting than declaring a universal winner. It is also much closer to how production systems behave.
Sources
Disclosure: I work with CometAPI content. The prices above come from OpenAI's public documentation, and this article does not use promotional gateway pricing.
Top comments (1)
Routing by consequence is the right default. I would add one more bucket for reversible mistakes. A Luna answer that is cheap to rerun and easy to diff can stay on the cheap path even when the prompt looks messy. The expensive model should buy uncertainty reduction rather than tidier prose.