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OpenAIFastHigh

GPT-5.6 Terra

The balanced tier of OpenAI's GPT-5.6 family (GA July 2026). Terra offers near-flagship quality at $2/$12 per million tokens (40% of Sol), with a 1M token context window, 128K max output tokens, and cached inputs at a 90% discount. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

3,716/22,296credits
input / output ยท per 1M tokens
Balanced tier of the GPT-5.6 family
1M token context window
128K max output tokens
Knowledge cutoff: February 2026
Cached input pricing (90% discount)
Adjustable reasoning effort
Function calling and native vision support

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Model Specifications

Context Window
1M
tokens
Max Output
128K
tokens
Training Cutoff
2026-02
Compatible SDK
OpenAI

Capabilities

Vision
Function Calling
Streaming
JSON Mode
System Prompt

Token Pricing (per 1M tokens)

Token TypeCreditsUSD Equivalent
Input Tokens3,716$2.48
Output Tokens22,296$14.86
Cached Tokens372$0.25

* 1,500 credits โ‰ˆ $1 (actual charges may vary based on usage)

Quick Start

curl -X POST "https://api.core.today/llm/openai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "gpt-5.6-terra",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful customer support assistant."
    },
    {
      "role": "user",
      "content": "My webhook deliveries started failing with 403 yesterday. What should I check?"
    }
  ],
  "max_completion_tokens": 2000
}'

Parameters

ParameterTypeRequiredDefaultDescription
messagesarrayYes-Array of message objects with role and content
modelstringYesgpt-5.6-terraModel identifier
max_completion_tokensintegerNo4096Maximum tokens in response (up to 128000). Note: use max_completion_tokens, not max_tokens
reasoning_effortstringNomediumReasoning effort level: none, low, medium, high, or xhigh
nonelowmediumhighxhigh
temperaturefloatNo1.0Sampling temperature (0-2)
streambooleanNofalseEnable Server-Sent Events streaming

Examples

Production Assistant

Balanced quality and cost for high-volume assistant traffic

curl -X POST "https://api.core.today/llm/openai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "gpt-5.6-terra",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful customer support assistant."
    },
    {
      "role": "user",
      "content": "My webhook deliveries started failing with 403 yesterday. What should I check?"
    }
  ],
  "max_completion_tokens": 2000
}'

Code Review

Review a diff with near-flagship reasoning at a fraction of the cost

curl -X POST "https://api.core.today/llm/openai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "gpt-5.6-terra",
  "messages": [
    {
      "role": "system",
      "content": "You are a strict code reviewer. Report bugs first."
    },
    {
      "role": "user",
      "content": "<unified diff>"
    }
  ],
  "reasoning_effort": "high",
  "max_completion_tokens": 4000
}'

Tips & Best Practices

1Best price-performance of the GPT-5.6 family for most production workloads
2Escalate to Sol only for the hardest reasoning tasks; drop to Luna for simple, high-volume calls
3Use cached inputs ($0.20/M, 90% off) for repeated system prompts and RAG context
4Keep prompts under 272K input tokens where possible โ€” above that the whole request bills at 2x input / 1.5x output
5reasoning_effort 'high' recovers much of Sol's quality on complex tasks
6Lower temperature (0.2-0.5) for coding and analytical tasks

Use Cases

Production chat and assistant workloads
Code generation and review at scale
Document analysis and summarization
Agentic workflows with cost constraints
Repeated context with cached inputs (RAG)