tokevenGPT · Fast / Budget · 1,050K context window
GPT 5.6 Luna pricing for pdf summarization
$0.004800 per call at 15,000 input / 1,500 output tokens.
Per call
$0.004800
10K calls/mo
$48
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 pdf summarization 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=1500,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.004800 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 pdf summarization benchmarks. Hover for details.
Cheapest viable option: Gemini 3 Flash at $0.0120/call (-150% less, 83/100 quality).
What drives pdf summarization cost
PDFs are the worst case for input cost: extracted text carries page furniture, headers, and footnotes that inflate token counts well past what the content warrants. Cleaning the extraction before sending it often cuts more cost than changing models. Long PDFs also routinely cross long-context pricing thresholds, so chunking is both a quality and a cost decision.
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 15,000/1,500 token split, output accounts for 38% of the $0.004800 per-call cost.
Complete GPT 5.6 Luna pricing breakdown
Standard token rates
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $0.20 | 15,000 |
| Output | $1.20 | 1,500 |
Prompt caching: 90% off input tokens
Caching drops input cost from $0.20 to $0.02/MTok. For repeated system prompts, that saves $0.002700 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 | $0.004800 | $48 | $21 | $27 |
| 50,000 calls | $0.004800 | $240 | $105 | $135 |
| 100,000 calls | $0.004800 | $480 | $210 | $270 |
| 500,000 calls | $0.004800 | $2,400 | $1,050 | $1,350 |
| 1,000,000 calls | $0.004800 | $4,800 | $2,100 | $2,700 |
GPT 5.6 Luna quality benchmarks for pdf summarization
GPT 5.6 Luna is a solid pick for pdf summarization
Scores 84/100 (rank #4 of 20). Good balance of quality and cost for pdf summarization.
Top-ranked: GPT 5.6 Terra (92/100) at $0.0480/call
Performance across all task types
GPT 5.6 Luna scores by task type. PDF Summarization highlighted.
| Task type | Confidence score | Rank out of 20 |
|---|---|---|
| Classification | 91/100 | #1 |
| Extraction | 89/100 | #2 |
| Q&A | 85/100 | #4 |
| Summarization(current page) | 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 pdf summarization
12 models score 80+ on pdf summarization benchmarks, sorted by input rate. Cheapest: Gemini 3 Flash (-150% less, 83/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs GPT |
|---|---|---|---|---|---|---|
| Gemini 3 Flash | Gemini | $0.50 | $3.00 | 83/100 | $0.0120 | 150% more |
| Claude Haiku 4.5 | Claude | $1.00 | $5.00 | 82/100 | $0.0225 | 369% more |
| Gemini 3.5 Flash | Gemini | $1.50 | $9.00 | 85/100 | $0.0360 | 650% more |
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 91/100 | $0.0450 | 837% more |
| GPT 5.6 Terra | GPT | $2.00 | $12.00 | 92/100 | $0.0480 | 900% more |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 87/100 | $0.0480 | 900% more |
| GPT 5.4 | GPT | $2.50 | $15.00 | 91/100 | $0.0600 | 1150% more |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 90/100 | $0.0675 | 1306% more |
| GPT 5.6 Sol | GPT | $4.00 | $20.00 | 90/100 | $0.0900 | 1775% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 88/100 | $0.1125 | 2244% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 89/100 | $0.1200 | 2400% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 90/100 | $0.2250 | 4587% 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
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
JSON Extraction
$0.001560/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 pdf summarization per API call?
A typical pdf summarization call using GPT 5.6 Luna costs about $0.004800 per call. This estimate uses 15,000 input tokens and 1,500 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 pdf summarization?
GPT 5.6 Luna scores 84 out of 100 on pdf summarization benchmarks overall. It ranks #4 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 92/100 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 pdf summarization with good quality?
The most affordable model for pdf summarization above 80/100 quality is Gemini 3 Flash. It comes from Gemini at $0.50 per million input tokens. Output costs $3.00 per million tokens with a quality score of 83/100. Switching from GPT 5.6 Luna to Gemini 3 Flash saves about -150% per API call.
How can I reduce my GPT 5.6 Luna costs for pdf summarization?
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 $0.004800 to about $0.000240 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 pdf summarization 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 pdf summarization, typical calls use 15,000 input and 1,500 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.
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.