GPT-5.6 Pricing Explained: Sol, Terra, and Luna Costs

CarolinaCodeBench:

The basic comparison is straightforward when measured per 1 million tokens. Sol costs $5 for input and $30 for output, Terra costs $2.50 for input and $15 for output, and Luna costs $1 for input and $6 for output. That makes Terra half the standard token price of Sol, while Luna is one-fifth of Sol's standard price. These numbers do not mean each completed task will preserve exactly the same ratio. A less capable model might need more instructions, retries, or review. Start by calculating input and output separately because generated output is considerably more expensive than input on all three models.

BudgetTokensBen:

Do not assume Terra is automatically the best choice just because it sits in the middle. Luna may be sufficient for classification, extraction, rewriting, routing, and other predictable high-volume tasks. Sol may be economical for a difficult task when it gets the result right in one attempt and avoids several failed runs. Terra is a sensible starting point for mixed workloads, but the decision should be based on a test set that resembles your actual documents. Compare accuracy, completion length, latency, retry frequency, and human review time rather than looking only at the rate card.

OhioWorkflowLab:

A simple API estimate uses two calculations: input tokens divided by 1 million and multiplied by the input rate, plus output tokens divided by 1 million and multiplied by the output rate. Suppose one monthly workload uses 20 million input tokens and 4 million output tokens. Before caching or other charges, Luna would cost about $44, Terra about $110, and Sol about $220. This example also shows why output control matters. Although the workload contains five times more input than output, output still represents more than half of the basic bill because output tokens have a higher rate.

CanyonCacheUser:

Prompt caching can lower costs when requests repeatedly include the same long instructions, examples, or reference material. Published cached-input rates are lower than normal input rates, but caching only helps the reusable portion that qualifies as a cache hit. It does not reduce newly generated output charges. Structure stable instructions consistently and place changing user content after the reusable prefix when the API supports that pattern. Also remember that writing content into a cache may follow different billing rules from reading cached content. Confirm the current caching requirements and rates on the official API pricing documentation before building a forecast.

GeorgiaDataRoute:

I would use routing instead of forcing every request through one model. Send routine extraction and formatting work to Luna, ambiguous business documents to Terra, and only the hardest reasoning cases to Sol. A routing rule can consider document length, task type, confidence checks, or whether a cheaper model failed validation. The important part is preventing endless automatic escalation. Set a maximum number of attempts and log why each request moved to a more expensive model. This approach can preserve Sol for tasks where its additional capability has measurable value.

RockiesResponseTrim:

Response length deserves more attention than it usually receives. Output is priced at $30 per million tokens for Sol, $15 for Terra, and $6 for Luna, so asking for long explanations when a compact JSON object will work can increase costs quickly. Give the model an explicit output format, request only required fields, set sensible output limits, and avoid asking it to repeat the source document. These controls may also make downstream parsing easier. Do not cut output so aggressively that important information is lost, but eliminate decorative summaries that your application never uses.

PrairieAIBudget:

Include more than text-token charges in your budget. Some workflows use search, computer interaction, file processing, or other tools that may have separate fees. Application hosting, vector storage, monitoring, failed jobs, data transfer, and human quality checks can also matter. Subscription access in a consumer or business chat product should not be treated as identical to API billing. The API is generally usage based, while a subscription may include plan limits and access rules. Keep those budgets separate so you do not assume a monthly chat subscription covers production API requests.

BostonEvalRunner:

Run a controlled evaluation before choosing a default. Use perhaps a few dozen representative tasks, record token usage from the API response, and score whether each result meets your acceptance criteria. Then calculate cost per accepted result. For example, a Luna request that costs one cent but succeeds half the time may be less attractive than a Terra request that costs two cents and usually passes. Human correction time should also be counted when mistakes are expensive. The goal is not the lowest cost per request. It is the lowest reliable cost per completed business outcome.

DesertUsageMonitor:

Whichever model you select, add cost controls from the beginning. Track tokens by customer, feature, model, and request type. Set alerts for unusual daily usage, limit repeated retries, and investigate requests with unexpectedly large contexts. Store the price assumptions used by your reporting system so they can be updated without changing application code. Model rates and platform policies can change, so verify current pricing through the official provider documentation instead of relying permanently on an older spreadsheet or article.