tokevenGPT · Fast / Budget · 1,050K context window
GPT 5.6 Luna pricing for json extraction
$0.001560 per call at 3,000 input / 800 output tokens.
Per call
$0.001560
10K calls/mo
$15.6
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 json extraction spend. Prices verified daily against provider pricing pages.
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=800,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.001560 per call at standard rates
# Input: $0.20/MTok
# Output: $1.20/MTokHow GPT 5.6 Luna compares on cost
Per-call cost for all models scoring 50+ on json extraction benchmarks. Hover for details.
Cheapest viable option: Gemini 3.5 Flash-Lite at $0.002900/call (-86% less, 83/100 quality).
What drives json extraction cost
Structured JSON output is compact and predictable, so cost is set almost entirely by how much source text you send. The schema itself is identical on every call and belongs in the cached prefix. This is also a task where a smaller model often matches a larger one: the output space is constrained by the schema, which limits how wrong a cheaper model can go.
For GPT 5.6 Luna specifically, that means output tokens cost 6.0x more than input, at $1.20 vs $0.20 per MTok. At this use case’s 3,000/800 token split, output accounts for 62% of the $0.001560 per-call cost.
Complete GPT 5.6 Luna pricing breakdown
Standard token rates
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $0.20 | 3,000 |
| Output | $1.20 | 800 |
Prompt caching: 90% off input tokens
Caching drops input cost from $0.20 to $0.02/MTok. For repeated system prompts, that saves $0.000540 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 | $0.001560 | $15.6 | $10.2 | $5.4 |
| 50,000 calls | $0.001560 | $78 | $51 | $27 |
| 100,000 calls | $0.001560 | $156 | $102 | $54 |
| 500,000 calls | $0.001560 | $780 | $510 | $270 |
| 1,000,000 calls | $0.001560 | $1,560 | $1,020 | $540 |
GPT 5.6 Luna quality benchmarks for json extraction
GPT 5.6 Luna is a solid pick for json extraction
Scores 89/100 (rank #2 of 20). Good balance of quality and cost for json extraction.
Top-ranked: GPT 5.6 Terra (90/100) at $0.0156/call
Performance across all task types
GPT 5.6 Luna scores by task type. JSON Extraction highlighted.
| Task type | Confidence score | Rank out of 20 |
|---|---|---|
| Classification | 91/100 | #1 |
| Extraction(current page) | 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 | 58/100 | #7 |
Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
Alternative models for json extraction
13 models score 80+ on json extraction benchmarks, sorted by input rate. Cheapest: Gemini 3.5 Flash-Lite (-86% less, 83/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs GPT |
|---|---|---|---|---|---|---|
| Gemini 3.5 Flash-Lite | Gemini | $0.30 | $2.50 | 83/100 | $0.002900 | 86% more |
| Gemini 3 Flash | Gemini | $0.50 | $3.00 | 86/100 | $0.003900 | 150% more |
| Claude Haiku 4.5 | Claude | $1.00 | $5.00 | 88/100 | $0.007000 | 349% more |
| Gemini 3.5 Flash | Gemini | $1.50 | $9.00 | 90/100 | $0.0117 | 650% more |
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 88/100 | $0.0140 | 797% more |
| GPT 5.6 Terra | GPT | $2.00 | $12.00 | 90/100 | $0.0156 | 900% more |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 85/100 | $0.0156 | 900% more |
| GPT 5.4 | GPT | $2.50 | $15.00 | 89/100 | $0.0195 | 1150% more |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 86/100 | $0.0210 | 1246% more |
| GPT 5.6 Sol | GPT | $4.00 | $20.00 | 88/100 | $0.0280 | 1695% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 82/100 | $0.0350 | 2144% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 87/100 | $0.0390 | 2400% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 85/100 | $0.0700 | 4387% more |
About GPT 5.6 Luna
Provider
GPT
Tier
Fast / Budget
Context
1,050K tokens
Released
Jul 2026
Other models from GPT
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| GPT 5.5 | Flagship | $5.00 | $30.00 | 1,050K |
| GPT 5.4 | Mid-tier | $2.50 | $15.00 | 1,050K |
| GPT 5.6 Sol | Flagship | $4.00 | $20.00 | 1,050K |
| GPT 5.6 Terra | Mid-tier | $2.00 | $12.00 | 1,050K |
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
$0.005200/call
Unit Test Generation
$0.006600/call
SQL Generation
$0.001500/call
API Generation
$0.007800/call
Code Refactoring
$0.005600/call
Code Review
$0.003400/call
PR Review
$0.005200/call
Security Review
$0.004200/call
Bug Detection
$0.003400/call
Document Summarization
$0.003200/call
PDF Summarization
$0.004800/call
Meeting Notes
$0.004000/call
Email Summarization
$0.001200/call
Chatbot
$0.001600/call
RAG Pipeline
$0.003000/call
Customer Support Bot
$0.001800/call
FAQ Bot
$0.001260/call
Data Extraction
$0.002000/call
Invoice Parsing
$0.001120/call
Resume Parsing
$0.001460/call
Chain of Thought
$0.006600/call
Legal Analysis
$0.006800/call
Math Reasoning
$0.003900/call
Financial Analysis
$0.006400/call
Content Moderation
$0.000220/call
Sentiment Analysis
$0.000220/call
Categorization
$0.000340/call
Intent Detection
$0.000180/call
Copywriting
$0.002500/call
Blog Writing
$0.009800/call
Marketing Copy
$0.003760/call
Product Descriptions
$0.001900/call
Related pages
Frequently asked questions
How much does GPT 5.6 Luna cost for json extraction per API call?
A typical json extraction call using GPT 5.6 Luna costs about $0.001560 per call. This estimate uses 3,000 input tokens and 800 output tokens at standard rates. Standard rates are $0.20 per million input tokens and $1.20 per million output tokens.
Is GPT 5.6 Luna the best model for json extraction?
GPT 5.6 Luna scores 89 out of 100 on json extraction benchmarks overall. It ranks #2 across all 20 models in our cross-provider registry. The highest-ranked model for this task is GPT 5.6 Terra from GPT. That model scores 90/100 on this task type. Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
What is the cheapest model for json extraction with good quality?
The most affordable model for json extraction above 80/100 quality is Gemini 3.5 Flash-Lite. It comes from Gemini at $0.30 per million input tokens. Output costs $2.50 per million tokens with a quality score of 83/100. Switching from GPT 5.6 Luna to Gemini 3.5 Flash-Lite saves about -86% per API call.
How can I reduce my GPT 5.6 Luna costs for json extraction?
Three strategies lower GPT 5.6 Luna costs for this task type effectively. Prompt caching gives a 90% discount on input tokens when reusing system prompts. The batch API gives 50% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.001560 to about $0.000078 per call.
How does GPT 5.6 Luna pricing compare to other GPT models?
GPT has 4 other models alongside GPT 5.6 Luna today: GPT 5.5 at $5.00/$30.00 per million tokens (flagship), GPT 5.4 at $2.50/$15.00 per million tokens (mid-tier), GPT 5.6 Sol at $4.00/$20.00 per million tokens (flagship), GPT 5.6 Terra at $2.00/$12.00 per million tokens (mid-tier). The right choice depends on whether your json extraction workload prioritizes quality, cost, or speed.
What context window does GPT 5.6 Luna support?
GPT 5.6 Luna supports a context window of 1,050K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For json extraction, typical calls use 3,000 input and 800 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.
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