Summarize an implementation plan
ProductivitySummarize this implementation plan in one paragraph for a non-technical stakeholder, then list the three main operational risks. <paste plan here>
OpenAI GPT-5.6 Terra: mid GPT-5.6 tier balancing quality and cost.
from$0.504/ 1M input tokens$0.5610% off
Model IDgpt-5.6-terra
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 | $3.024 / 1M output tokens 303 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.
21 prompts
Summarize this implementation plan in one paragraph for a non-technical stakeholder, then list the three main operational risks. <paste plan here>
Draft a friendly reply to a customer whose export keeps timing out. Ask for the file size and browser, and offer a workaround.
Using only the policy document below, answer: can contractors expense home internet? Quote the relevant section. <paste document here>
Summarize this implementation plan in one paragraph.
What is Objective C vs C?
Review this diff and list the blocking issues with file and line: <diff>
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-5.6-terra",
"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-5.6-terra" |
| provider | string | No | Pin a provider; omit to auto-route to the cheapest. One of: replicate | 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-5.6-terra",
"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-5.6-terra |
| 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-5.6-terra"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-5.6 Terra | GPT-6 Astra | GPT-6 Sol | GPT-6 Luna | |
|---|---|---|---|---|
| Vendor | OpenAI | OpenAI | OpenAI | OpenAI |
| Starting price | $0.504 / 1M input tokens | $2.52 / 1M input tokens | $0.54 / 1M input tokens | $0.027 / 1M input tokens |
| Input | Text | Text | Text | Text |
| Provider | 8 providers | 7 providers | 7 providers | 8 providers |
Terra sits between Sol and Luna and covers most production work at a moderate price: app features, internal tools, support automation, routine agent steps, code review and document Q&A.
Log the prompts where Terra falls short, re-run them on GPT-5.6 Sol, and route only that class of request to Sol.
The same as Sol: about 1.05M tokens of context, up to 128K output tokens, text and image input, tools and structured output.
Yes. Call it through our /chat/completions endpoint with the OpenAI SDK by changing the base URL and model name.
Per 1M input and output tokens, settled from actual usage. See the pricing table for current rates.
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-6 Luna: the fastest, lowest-cost GPT-6 tier for high-volume tasks. 1.05M context.
OpenAI GPT-5.6 Sol: flagship GPT-5.6 tier for complex reasoning and coding.
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.