tokevenGPT · Fast / Budget · 1,000K context window
GPT 5.6 Luna pricing for marketing copy
$0.0188 per call at 800 input / 3,000 output tokens.
Per call
$0.0188
10K calls/mo
$188
Cache savings
90% off input
Batch savings
50% off all
Calculate your GPT 5.6 Luna costs
Adjust token counts, volume, and discount toggles to model your marketing copy spend. Pricing updated weekly.
Configure your usage
Estimated cost
Quick start
Call GPT 5.6 Luna with the OpenAI Python SDK:
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-5.6-luna",
max_tokens=3000,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0188 per call at standard rates
# Input: $1.00/MTok
# Output: $6.00/MTokHow GPT 5.6 Luna compares on cost
Per-call cost for all models scoring 50+ on marketing copy benchmarks. Hover for details.
Cheapest viable option: Claude Sonnet 5 at $0.0316/call (-68% less, 87/100 quality).
Complete GPT 5.6 Luna pricing breakdown
Standard token rates
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $1.00 | 800 |
| Output | $6.00 | 3,000 |
Prompt caching: 90% off input tokens
Caching drops input cost from $1.00 to $0.10/MTok. For repeated system prompts, that saves $0.000720 per call.
Batch API: 50% off all tokens
Submit requests asynchronously and receive 50% off all token costs. This works best for offline pipelines where latency requirements are flexible.
Monthly cost projections
| Monthly volume | Cost per call | Standard monthly | Cached monthly | You save with caching |
|---|---|---|---|---|
| 10,000 calls | $0.0188 | $188 | $180.8 | $7.2 |
| 50,000 calls | $0.0188 | $940 | $904 | $36 |
| 100,000 calls | $0.0188 | $1,880 | $1,808 | $72 |
| 500,000 calls | $0.0188 | $9,400 | $9,040 | $360 |
| 1,000,000 calls | $0.0188 | $18,800 | $18,080 | $720 |
GPT 5.6 Luna quality benchmarks for marketing copy
Consider alternatives for marketing copy
Scores 58/100 (rank #7 of 14). Better options exist at similar price points for marketing copy.
Top-ranked: Claude Fable 5 (95/100) at $0.1580/call
Performance across all task types
GPT 5.6 Luna scores by task type. Marketing Copy highlighted.
| Task type | Confidence score | Rank out of 14 |
|---|---|---|
| Classification | 91/100 | #1 |
| Extraction | 89/100 | #2 |
| Q&A | 85/100 | #4 |
| Summarization | 84/100 | #4 |
| Code Generation | 70/100 | #6 |
| Code Review | 68/100 | #6 |
| Reasoning | 63/100 | #6 |
| Creative Writing(current page) | 58/100 | #7 |
Alternative models for marketing copy
8 models score 80+ on marketing copy benchmarks, sorted by input rate. Cheapest: Claude Sonnet 5 (-68% less, 87/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs GPT |
|---|---|---|---|---|---|---|
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 87/100 | $0.0316 | 68% more |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 84/100 | $0.0376 | 100% more |
| GPT 5.6 Terra | GPT | $2.50 | $15.00 | 80/100 | $0.0470 | 150% more |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 85/100 | $0.0474 | 152% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 93/100 | $0.0790 | 320% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 91/100 | $0.0940 | 400% more |
| GPT 5.6 Sol | GPT | $5.00 | $30.00 | 92/100 | $0.0940 | 400% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 95/100 | $0.1580 | 740% more |
About GPT 5.6 Luna
Provider
GPT
Tier
Fast / Budget
Context
1,000K tokens
Released
Jul 2026
Other models from GPT
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| GPT 5.5 | Flagship | $5.00 | $30.00 | 128K |
| GPT 5.4 | Mid-tier | $2.50 | $15.00 | 128K |
| GPT 5.6 Sol | Flagship | $5.00 | $30.00 | 1,000K |
| GPT 5.6 Terra | Mid-tier | $2.50 | $15.00 | 1,000K |
You can compare GPT 5.6 Luna head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.
GPT 5.6 Luna pricing for other use cases
See how GPT 5.6 Luna costs change across different workload types.
Code Generation
$0.0260/call
Unit Test Generation
$0.0330/call
SQL Generation
$0.007500/call
API Generation
$0.0390/call
Code Refactoring
$0.0280/call
Code Review
$0.0170/call
PR Review
$0.0260/call
Security Review
$0.0210/call
Bug Detection
$0.0170/call
Document Summarization
$0.0160/call
PDF Summarization
$0.0240/call
Meeting Notes
$0.0200/call
Email Summarization
$0.006000/call
Chatbot
$0.008000/call
RAG Pipeline
$0.0150/call
Customer Support Bot
$0.009000/call
FAQ Bot
$0.006300/call
Data Extraction
$0.0100/call
JSON Extraction
$0.007800/call
Invoice Parsing
$0.005600/call
Resume Parsing
$0.007300/call
Chain of Thought
$0.0330/call
Legal Analysis
$0.0340/call
Math Reasoning
$0.0195/call
Financial Analysis
$0.0320/call
Content Moderation
$0.001100/call
Sentiment Analysis
$0.001100/call
Categorization
$0.001700/call
Intent Detection
$0.000900/call
Copywriting
$0.0125/call
Blog Writing
$0.0490/call
Product Descriptions
$0.009500/call
Related pages
Frequently asked questions
How much does GPT 5.6 Luna cost for marketing copy per API call?
$0.0188 per call at 800 input / 3,000 output tokens.
Is GPT 5.6 Luna the best model for marketing copy?
Scores 58/100, rank #7 of 14 models. Top-ranked: Claude Fable 5 at 95/100.
What is the cheapest model for marketing copy with good quality?
Claude Sonnet 5 scores 87/100 quality at $2.00/$10.00 per MTok. That is -68% less than GPT 5.6 Luna per call.
How can I reduce my GPT 5.6 Luna costs for marketing copy?
Three strategies: prompt caching (90% off input), batch API (50% off all tokens), prompt optimization. Combined: $0.0188 drops to about $0.000940 per call.
How does GPT 5.6 Luna pricing compare to other GPT models?
GPT also offers: GPT 5.5 at $5.00/$30.00, GPT 5.4 at $2.50/$15.00, GPT 5.6 Sol at $5.00/$30.00, GPT 5.6 Terra at $2.50/$15.00.
What context window does GPT 5.6 Luna support?
1,000K tokens total. Typical marketing copy calls use 800 plus 3,000 tokens, well within limits.
Track what you spend on AI
This calculator estimates costs based on published rates only. Connect your API usage to see where your money goes and which models drive your spend.