VidgoAI

GPT 5.6 Terra API

openai/gpt-5.6-terra
1M tokens · 112 input credits / 1M tokens

GPT 5.6 Terra converts prompts and conversation context into structured text for coding, analysis, extraction, and general generation. Its 1M-token context supports large instructions and documents when you need a balanced GPT 5.6 option for recurring production workloads.

GPT 5.6 Terra

OpenAI · responses

Chat with GPT 5.6 Terra

Each model has its own conversation. Switching never sends another model's history, and switching back resumes where you left off. Requests are billed from actual token usage.

Ctrl / ⌘ + Enter to send0 / 32,000

Continue with

GPT 5.6 SolGPT 5.6 LunaKimi K3

GPT 5.6 Terra

GPT 5.6 Terra accepts a text string or ordered message array and returns generated text plus usage in synchronous JSON or SSE. Use clear output rules and relevant long-form context to support repeatable coding, analysis, and data-extraction workflows.

Why Choose This?

  • Balanced production tierRoute recurring coding, analysis, extraction, and generation work to one GPT 5.6 model with mid-family token rates.

  • Large-context analysisProvide long instructions or document sets within the 1M-token context when evidence spans distant sections.

  • Structured output workDefine fields, labels, and formatting rules to turn unstructured input into application-ready text.

Parameters

ParameterRequirementDescription
inputRequired

A text string or ordered message array containing the task, supporting context, and conversation history.

max_output_tokensOptional

Sets the maximum number of tokens the model may return for this response.

streamOptional

Set false for synchronous JSON or true for an ordered SSE event stream.

Defaultfalsetrue

How to Use

  1. Define the production taskState the coding, analysis, extraction, or generation outcome and the conditions it must satisfy.

  2. Add relevant contextInclude the source code, documents, examples, and business rules needed for this request.

  3. Specify the output contractDefine required sections, fields, labels, or formatting so the response can enter the next workflow step.

  4. Validate and reuseCheck the response against representative inputs before applying the same prompt pattern in production.

Pricing

Vidgo AI meters input, output, cache-read, and cache-write tokens separately, then settles their sum in credits. Rates below are per 1M tokens.

UsageRateDetails
Input tokens112 credits / 1M tokensInput usage that does not hit prompt cache.
Output tokens672 credits / 1M tokensTokens generated by the model.
Cache read11.2 credits / 1M tokensInput tokens read from prompt cache.
Cache write140 credits / 1M tokensInput tokens written to prompt cache.

Best Use Cases

  • Feature implementation briefTurn requirements, existing code, and architecture rules into an implementation plan with acceptance checks.

  • Long-document extractionConvert reports, policies, or specifications into named fields, decisions, and unresolved risks.

  • Structured operations assistantTransform support notes and internal procedures into categorized answers or workflow-ready records.

Pro Tips

  • Define the output schema and include one representative example when downstream code expects stable fields or labels.
  • Separate extraction from synthesis when long documents require both traceable facts and a final recommendation.
  • Keep recurring system instructions and reference material unchanged to improve prompt-cache reuse.

Notes

  • The context window is 1M tokens, and the maximum output is 128K tokens.
  • GPT 5.6 Terra uses /v1/responses on Vidgo AI.
  • The minimum positive charge for a successful request is 0.001 credit; requests without valid output are not charged.
  • If a provider returns valid content without usage, the backend estimates token usage for settlement.

Related Models

GPT 5.6 SolOpenAI · 1M tokensGPT 5.6 Sol text generation API from 280 credits per 1M input tokens.
GPT 5.6 LunaOpenAI · 1M tokensGPT 5.6 Luna text generation API from 11.2 credits per 1M input tokens.
Kimi K3Moonshot AI · 1,048,576 tokensKimi K3 text generation API from 456 credits per 1M input tokens.
Claude Opus 5Anthropic · 1M tokensClaude Opus 5 text generation API from 400 credits per 1M input tokens.
Claude Sonnet 5Anthropic · 1M tokensClaude Sonnet 5 text generation API from 170 credits per 1M input tokens.

GPT 5.6 Terra API — Frequently asked questions

What is the GPT 5.6 Terra API?

GPT 5.6 Terra is developed by OpenAI. It accepts text or conversation messages and returns generated text plus usage through Vidgo AI.

How do I call the GPT 5.6 Terra API?

Send a Bearer-authenticated POST request to /v1/responses with model gpt-5-6-terra and valid input. Use stream: false for JSON or stream: true for SSE.

How much does GPT 5.6 Terra cost?

Per 1M tokens, Vidgo charges 112 credits for input, 672 for output, 11.2 for cache reads, and 140 for cache writes. The minimum positive charge for a successful request is 0.001 credit.

What inputs does GPT 5.6 Terra accept?

The Responses endpoint accepts input as a text string or message array. You can also set max_output_tokens and choose synchronous JSON or SSE with stream.

Which GPT 5.6 tier should I choose?

Choose Terra for recurring coding, analysis, extraction, and generation where production cost matters. Choose Sol for the hardest multi-step work or Luna for routine, high-volume text processing at lower Vidgo token rates.