Built for Repeated Tasks
Use Luna for focused work that runs many times: classify requests, extract fields, summarize selected material or draft short answers. Measure accuracy on your own examples before increasing volume.
curl --request POST "https://gptproto.com/v1/chat/completions" \
--header "Authorization: Bearer $GPTPROTO_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "gpt-6-luna",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'Chat, coding agents & document work. Priced per 1M tokens — input, cached input and output are billed separately. GPTProto is 20% below official rates.
OpenAI · ≈ 148M tokens/mo (48M cached)
OpenRouter costs include its ~5.5% credit purchase fee. GPTProto applies a per-model discount (10–30% off) and your bonus credits are also spent at discounted rates — savings compound. Estimates assume a 60% cache hit rate.
Build repeatable application workflows with Luna, from labeling support messages to extracting product details and handling small agent steps. Test accuracy on real inputs, then compare latency, retries and total cost before moving a high-volume workload to this tier.
Use Luna for focused work that runs many times: classify requests, extract fields, summarize selected material or draft short answers. Measure accuracy on your own examples before increasing volume.
The model accepts up to 1,050,000 tokens of context and supports up to 128,000 output tokens. Retrieve only useful material for each request; a large window is a capacity limit, not a recommended prompt length.
Choose none, low, medium (default), high, xhigh or max upstream. Start with a small task set and compare completed results, latency and token use at different effort levels.
OpenAI documents structured outputs, streaming and function calling for Luna. Use these capabilities for extraction or application workflows where supported by the GPTProto route; validate the response before writing to your database.
GPT-6 Luna is OpenAI's efficiency-focused model for high-volume, repeatable work. Its published API model ID is gpt-6-luna. The model accepts text and images as input and produces text; it is a hosted model, not an open-source download. OpenAI lists streaming, structured outputs and function calling among its upstream capabilities.
Luna is a first candidate for bounded requests: ticket labels, answers grounded in retrieved text, or product attributes. For ambiguous plans, cross-file edits or longer agent runs, test GPT-6 Sol too. Select by completed-task cost, not token rate alone.
| Published specification | GPT-6 Luna |
|---|---|
| OpenAI model ID | gpt-6-luna |
| Context window | 1,050,000 tokens |
| Maximum output | 128,000 tokens |
| Input / output | Text and image / text |
| Reasoning effort | none, low, medium (default), high, xhigh, max |
| Structured outputs / streaming | Supported upstream |
| Fine-tuning | Not supported |
The long context window does not guarantee success on a long-horizon task. Break work into checkable stages, save state outside the prompt and count retries when comparing costs.
High-volume chat and classification. Route narrowly defined messages to Luna. Provide category definitions or a short knowledge excerpt, then test accuracy and response time on real inputs.
Structured extraction. Extract fields from product descriptions or customer messages. Request a schema, validate fields in your application and review uncertain cases.
Bounded coding and agent steps. Explain a function, draft a targeted test or summarize a tool result. Your application runs tools and tests; escalate difficult changes or repeated failures to Sol.
The table shows OpenAI's standard upstream short-context text rates, not GPTProto prices. Consult this page's live pricing panel for the rate billed by GPTProto. All three models publish a 1,050,000-token context window and a 128,000-token maximum output.
| Model | OpenAI input / output per 1M tokens | Good first test |
|---|---|---|
| GPT-5.6 Luna | $0.20 / $1.20 | Existing workload baseline; retain it until migration tests pass. |
| GPT-6 Luna | $0.10 / $0.50 | Repeatable tasks where cost and response time matter. |
| GPT-6 Sol | $2.00 / $10.00 | Tasks needing stronger reasoning or longer agent loops. |
OpenAI's GPT-6 Luna list rates are 50% lower for input and about 58% lower for output than GPT-5.6 Luna's current rates. This compares token prices, not quality. Above 272,000 input tokens, higher upstream long-context rates apply to the entire request; check GPTProto's billing rules for large jobs.
Run a representative task sample on GPT-5.6 Luna, GPT-6 Luna and Sol. Compare accepted answers, retries, latency and total charge. Set a rule for escalation. For reasoning-enabled tool calls, check the endpoint: OpenAI recommends Responses, while Chat Completions permits function calling only with reasoning_effort: "none". Verify GPTProto's route in the Quick Start.
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