Ticket classification
ClassificationI was charged twice for my October invoice and need one refunded.
OpenAI GPT-6 Luna: the fastest, lowest-cost GPT-6 tier for high-volume tasks. 1.05M context.
from$0.027/ 1M input tokens$0.0310% off
Model IDgpt-6-luna
Input
Text
Output
Chat
Provider
8 providers
Last 7 days · not enough recent requests for stats yet.
Prices are per unit; requests without a provider are routed to the cheapest provider that supports your parameters.
| Option | Price (USD) |
|---|---|
| All options | $0.45 / 1M output tokens 45 Credits |
| Prompt ≥ 272,001 tokens | $0.675 / 1M output tokens 68 Credits |
Test output quality, compare provider prices and estimate cost before writing any code.
Credits for max_tokens are held first, then settled to the actual usage.
Start a conversation. Replies stream in and are billed by actual token usage.
Ready-to-use prompts. Load one into the playground and tweak it.
19 prompts
I was charged twice for my October invoice and need one refunded.
Extract the sender's company, order number, requested delivery date and any product SKUs from this email as JSON. <paste email here>
Summarize each of these 10 customer reviews in one line and tag the sentiment as positive, neutral or negative. <paste reviews here>
My invoice shows two charges for the same month.
Summarize the content without using lists, Markdown, or exceeding 80 words.
Fix the invalid JSON structure without changing the original meaning.
Endpoints, request fields and runnable examples for this model.
Base URL
https://01router.com/api/v1Set API_KEY to your project key, then run a sample from your server. Replace example media URLs with your own public URLs.
Auto-routing chooses a provider that supports the request. Select a provider to see its input constraints and pinned request examples.
curl https://01router.com/api/v1/chat/completions \
-H "Authorization: Bearer $API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-6-luna",
"messages": [{ "role": "user", "content": "Hello!" }],
"stream": false
}'Only endpoints relevant to this model are listed. Paths below include /api/v1. Click an endpoint to read its usage.
Send your API key as a Bearer token. Requests are billed to the key owner's credits.
Authorization: Bearer YOUR_API_KEY
Content-Type: application/jsonSend messages directly in the request body. Chat requests do not use an input wrapper.
| Name | Type | Required | Description |
|---|---|---|---|
| model | string | Yes | "gpt-6-luna" |
| provider | string | No | Pin a provider; omit to auto-route to the cheapest. One of: 01router | wavespeed | kie | apimart | evolink | atlascloud | vercel | openrouter |
| messages | object[] | Yes | OpenAI-format messages, each with role and content. |
| stream | boolean | No | true returns an SSE stream. |
| max_tokens | integer | No | Output token cap; credits are pre-held for it (default 4096). |
This example uses stream: false. Read the reply from choices[0].message.content. With stream: true, read SSE chunks from choices[].delta until [DONE].
{
"id": "chatcmpl-example",
"object": "chat.completion",
"model": "gpt-6-luna",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I help?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 8,
"total_tokens": 17
}
}| Name | Type | Required | Description |
|---|---|---|---|
| id | string | Yes | Chat completion ID |
| object | string | Yes | chat.completion |
| model | string | Yes | gpt-6-luna |
| choices | object[] | Yes | Reply choices; message.content contains the non-streaming response. |
| usage | object | No | Token usage: prompt_tokens, completion_tokens and total_tokens. Availability depends on the provider. |
This public endpoint returns the model parameters and each provider’s supported constraints and prices.
curl "https://01router.com/api/v1/models/gpt-6-luna"Check the HTTP status and error.message. Fix the request before retrying validation or authentication errors. A failed generation is reported in status and error on the task itself.
{
"error": {
"message": "Invalid or missing API key",
"code": "unauthorized"
}
}400Invalid body or parameter value
401Missing or invalid API key
402Not enough credits
404Unknown model or generation
429Too many requests — retry after the given seconds
502Every provider rejected the task (credits refunded)
Give your coding assistant this model reference: endpoints, input constraints, output fields and request examples in plain text.
Specs and starting prices side by side. Test with the same prompt before you choose.
| GPT-6 Luna | GPT-6 Astra | GPT-6 Sol | GPT-5.6 Sol | |
|---|---|---|---|---|
| Vendor | OpenAI | OpenAI | OpenAI | OpenAI |
| Starting price | $0.027 / 1M input tokens | $2.52 / 1M input tokens | $0.54 / 1M input tokens | $1.26 / 1M input tokens |
| Input | Text | Text | Text | Text |
| Provider | 8 providers | 7 providers | 7 providers | 8 providers |
Luna is the most efficient GPT-6 tier, built for focused, high-volume work where cost and latency matter most: classification, routing, extraction to JSON, quick summaries and cheap sub-steps under Sol or Astra.
It handles small, well-scoped edits, but open-ended coding and long agent runs are better on GPT-6 Sol or Astra.
About 1.05M tokens of context and up to 128K output tokens, with text and image input and structured JSON output.
Use reasoning effort none or low, keep max_tokens small and enable a JSON response format for classification tasks.
Per 1M input and output tokens at the lowest GPT-6 rates, settled from actual usage. See the pricing table for each provider.
Other models you can call with the same API key.
OpenAI GPT-6 Astra: the most capable GPT-6 tier for hard reasoning, coding and agents. 1.05M context.
OpenAI GPT-6 Sol: balanced GPT-6 tier for everyday reasoning, coding and tool use. 1.05M context.
OpenAI GPT-5.6 Sol: flagship GPT-5.6 tier for complex reasoning and coding.
OpenAI GPT-5.6 Terra: mid GPT-5.6 tier balancing quality and cost.
OpenAI GPT-5.6 Luna: small, fast GPT-5.6 tier.
Anthropic Claude Opus 5.5: frontier model for agentic coding, long-horizon tasks and deep analysis. 1M context.