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OpenAIFastHigh

GPT-5.6 Luna

The fast, low-cost tier of OpenAI's GPT-5.6 family (GA July 2026). Luna is built for high-throughput workloads at $1/$6 per million tokens, with a 1M token context window, 128K max output tokens, and cached inputs at a 90% discount.

1,858/11,148credits
input / output ยท per 1M tokens
Fast, low-cost 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 Tokens1,858$1.24
Output Tokens11,148$7.43
Cached Tokens186$0.12

* 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-luna",
  "messages": [
    {
      "role": "system",
      "content": "Classify the ticket as: billing, bug, feature_request. Respond with only the label."
    },
    {
      "role": "user",
      "content": "I was charged twice for my subscription this month."
    }
  ],
  "max_completion_tokens": 20,
  "temperature": 0
}'

Parameters

ParameterTypeRequiredDefaultDescription
messagesarrayYes-Array of message objects with role and content
modelstringYesgpt-5.6-lunaModel 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

Bulk Classification

High-throughput labeling at the lowest GPT-5.6 price point

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-luna",
  "messages": [
    {
      "role": "system",
      "content": "Classify the ticket as: billing, bug, feature_request. Respond with only the label."
    },
    {
      "role": "user",
      "content": "I was charged twice for my subscription this month."
    }
  ],
  "max_completion_tokens": 20,
  "temperature": 0
}'

Real-time Chat

Low-latency streaming responses for interactive UIs

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-luna",
  "messages": [
    {
      "role": "user",
      "content": "Give me three taglines for a coffee subscription."
    }
  ],
  "stream": true,
  "max_completion_tokens": 500
}'

Tips & Best Practices

1Cheapest GPT-5.6 tier at $1/$6 per M tokens โ€” ideal default for high-volume calls
2Use cached inputs ($0.10/M, 90% off) for repeated system prompts
3reasoning_effort 'none' or 'low' maximizes throughput on simple tasks
4Escalate to Terra or Sol when answers need deeper reasoning
5temperature 0 for deterministic classification and extraction

Use Cases

High-volume classification and routing
Real-time chat applications
Bulk summarization and extraction
Lightweight agent steps and tool selection
Cost-sensitive RAG pipelines