OpenAIFastHigh

GPT-6 Luna

The smallest, lowest-cost tier of OpenAI's GPT-6 generation, at $0.10/$0.50 per million tokens — built for high-volume, latency-sensitive work such as classification, extraction, routing and short-form generation. Cached inputs at a 90% discount. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

186/929credits

input / output · per 1M tokens

Lowest-cost tier of the GPT-6 generation

1M token context window

128K max output tokens

Knowledge cutoff: May 18, 2026

Cached input pricing (90% discount)

Adjustable reasoning effort (low to max)

Function calling and native vision support

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Test this model instantly in the Console Playground — no code required

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Model Specifications

Context Window

tokens

Max Output

128K

tokens

Training Cutoff

2026-05

Compatible SDK

OpenAI

Capabilities

Vision

Function Calling

Streaming

JSON Mode

System Prompt

Token Pricing (per 1M tokens)

Token Type	Credits	USD Equivalent
Input Tokens	186	$0.12
Output Tokens	929	$0.62
Cached Tokens	18.58	$0.01

* 1,500 credits ≈ $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-6-luna",
  "messages": [
    {
      "role": "system",
      "content": "Classify the support ticket into one of: billing, bug, feature_request, account. Reply with the label only."
    },
    {
      "role": "user",
      "content": "I was charged twice for my subscription this month."
    }
  ],
  "reasoning_effort": "low",
  "max_completion_tokens": 200
}'

Parameters

Parameter	Type	Required	Default	Description
`messages`	array	Yes	-	Array of message objects with role and content
`model`	string	Yes	gpt-6-luna	Model identifier
`max_completion_tokens`	integer	No	4096	Maximum tokens in response (up to 128000). Note: use max_completion_tokens, not max_tokens
`reasoning_effort`	string	No	medium	Reasoning effort level: low, medium, high, xhigh, or max lowmediumhighxhighmax
`temperature`	float	No	1.0	Sampling temperature (0-2)
`stream`	boolean	No	false	Enable Server-Sent Events streaming

Examples

High-volume Classification

Low-cost ticket triage with GPT-6 Luna

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-6-luna",
  "messages": [
    {
      "role": "system",
      "content": "Classify the support ticket into one of: billing, bug, feature_request, account. Reply with the label only."
    },
    {
      "role": "user",
      "content": "I was charged twice for my subscription this month."
    }
  ],
  "reasoning_effort": "low",
  "max_completion_tokens": 200
}'

Cached Repeated Context

Reuse a large system prompt with 90% cached input discount

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-6-luna",
  "messages": [
    {
      "role": "system",
      "content": "<large repeated system prompt or codebase context>"
    },
    {
      "role": "user",
      "content": "Summarize the open TODOs and rank them by risk."
    }
  ],
  "temperature": 0.3,
  "max_completion_tokens": 4000
}'

Tips & Best Practices

1Luna ($0.10/$0.50 per M) is the cheapest GPT-6 tier — escalate to GPT-6 Sol ($2/$10) or Astra ($10/$50) when answers need deeper reasoning

2Use cached inputs ($0.01/M, 90% off) for repeated system prompts and RAG context

3Keep prompts under 272K input tokens where possible — above that the whole request bills at 2x input / 1.5x output

4Keep reasoning_effort low for classification and extraction to minimize latency and output tokens

5128K output enables single-shot long-form generation

6Lower temperature (0.2-0.5) for coding and analytical tasks

7Cache writes bill at 1.25x the input rate — implicit caching writes by default, so most of a large first prompt is billed as a cache write, then reads are 90% off

8Fast mode (service_tier: fast or priority) bills token charges at 2x — it is settled on the tier the response reports, so a request downgraded to standard is billed at the standard rate