tokevenGemini · Fast / Budget · 128K context window
Gemini 2.5 Computer Use pricing for pdf summarization
$0.0338 per call at 15,000 input / 1,500 output tokens.
Per call
$0.0338
10K calls/mo
$337.5
Cache savings
0% off input
Batch savings
50% off all
Calculate your Gemini 2.5 Computer Use 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 Gemini 2.5 Computer Use with the Google Generative AI SDK:
import google.generativeai as genai
model = genai.GenerativeModel("gemini-2.5-computer-use")
response = model.generate_content("Your prompt here")
# Cost: ~$0.0338 per call at standard rates
# Input: $1.25/MTok
# Output: $10.00/MTokHow Gemini 2.5 Computer Use compares on cost
Per-call cost for all models scoring 50+ on pdf summarization benchmarks. Hover for details.
Cheapest viable option: GPT 5.6 Luna at $0.004800/call (86% less, 84/100 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 Gemini 2.5 Computer Use specifically, that means output tokens cost 8.0x more than input, at $10.00 vs $1.25 per MTok. At this use case’s 15,000/1,500 token split, output accounts for 44% of the $0.0338 per-call cost.
Complete Gemini 2.5 Computer Use pricing breakdown
Standard token rates
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $1.25 | 15,000 |
| Output | $10.00 | 1,500 |
Prompt caching: 0% off input tokens
Caching drops input cost from $1.25 to $1.25/MTok. For repeated system prompts, that saves $0.0000 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 | $0.0338 | $337.5 | $337.5 | $0.0000 |
| 50,000 calls | $0.0338 | $1,687.5 | $1,687.5 | $0.0000 |
| 100,000 calls | $0.0338 | $3,375 | $3,375 | $0.0000 |
| 500,000 calls | $0.0338 | $16,875 | $16,875 | $0.0000 |
| 1,000,000 calls | $0.0338 | $33,750 | $33,750 | $0.0000 |
Gemini 2.5 Computer Use quality benchmarks for pdf summarization
Benchmark data for pdf summarization is pending for Gemini 2.5 Computer Use. Test against your own evaluation suite before moving to production.
Performance across all task types
Gemini 2.5 Computer Use scores by task type. PDF Summarization highlighted.
| Task type | Confidence score | Rank out of 72 |
|---|
Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 10 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: GPT 5.6 Luna (86% less, 84/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs Gemini |
|---|---|---|---|---|---|---|
| GPT 5.6 Luna | GPT | $0.20 | $1.20 | 84/100 | $0.004800 | 86% cheaper |
| Gemini 3 Flash | Gemini | $0.50 | $3.00 | 83/100 | $0.0120 | 64% cheaper |
| Gemini 3.5 Flash | Gemini | $1.50 | $9.00 | 85/100 | $0.0360 | 7% more |
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 91/100 | $0.0450 | 33% more |
| GPT 5.6 Terra | GPT | $2.00 | $12.00 | 92/100 | $0.0480 | 42% more |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 87/100 | $0.0480 | 42% more |
| GPT 5.4 | GPT | $2.50 | $15.00 | 91/100 | $0.0600 | 78% more |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 90/100 | $0.0675 | 100% more |
| GPT 5.6 Sol | GPT | $4.00 | $20.00 | 90/100 | $0.0900 | 167% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 88/100 | $0.1125 | 233% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 89/100 | $0.1200 | 256% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 90/100 | $0.2250 | 567% more |
About Gemini 2.5 Computer Use
Provider
Gemini
Tier
Fast / Budget
Context
128K tokens
Released
Not published
Backfilled on 2026-09-23. Prices confirmed by the provider pricing page and the LiteLLM cross-check. Release date not published in a machine-readable source. Not yet benchmarked, so never recommended.
Other models from Gemini
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| Gemini 3.1 Pro | Mid-tier | $2.00 | $12.00 | 1,048.576K |
| Gemini 3.5 Flash | Fast / Budget | $1.50 | $9.00 | 1,048.576K |
| Gemini 3 Flash | Fast / Budget | $0.50 | $3.00 | 1,048.576K |
| Gemini 3.5 Flash-Lite | Fast / Budget | $0.30 | $2.50 | 1,048.576K |
| Gemini 2.5 Flash | Fast / Budget | $0.30 | $2.50 | 1,048.576K |
| Gemini 2.5 Flash-Lite | Fast / Budget | $0.10 | $0.40 | 1,048.576K |
| Gemini 2.5 Pro | Mid-tier | $1.25 | $10.00 | 1,048.576K |
| Gemini 3.1 Flash-Lite | Fast / Budget | $0.25 | $1.50 | 1,048.576K |
| Gemini 3.1 Pro Custom Tools | Mid-tier | $2.00 | $12.00 | 1,048.576K |
| Gemini 3.6 Flash | Fast / Budget | $0.75 | $3.75 | 1,048.576K |
| Gemini 3.7 Flash | Fast / Budget | $0.75 | $3.75 | 1,048.576K |
| Gemini 3.8 Flash | Fast / Budget | $0.75 | $3.75 | 1,048.576K |
| Gemini Omni 1.1 Flash | Fast / Budget | $1.50 | $9.00 | 1,048.576K |
| Gemini Omni Flash | Fast / Budget | $1.50 | $9.00 | 1,048.576K |
| Gemini Robotics ER 2 | Fast / Budget | $1.00 | $5.00 | 131.072K |
You can compare Gemini 2.5 Computer Use 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.
Gemini 2.5 Computer Use pricing for other use cases
See how Gemini 2.5 Computer Use costs change across different workload types.
Code Generation
$0.0425/call
Unit Test Generation
$0.0538/call
SQL Generation
$0.0119/call
API Generation
$0.0638/call
Code Refactoring
$0.0450/call
Code Review
$0.0263/call
PR Review
$0.0400/call
Security Review
$0.0325/call
Bug Detection
$0.0263/call
Document Summarization
$0.0225/call
Meeting Notes
$0.0300/call
Email Summarization
$0.008750/call
Chatbot
$0.0125/call
RAG Pipeline
$0.0225/call
Customer Support Bot
$0.0138/call
FAQ Bot
$0.009875/call
Data Extraction
$0.0150/call
JSON Extraction
$0.0118/call
Invoice Parsing
$0.008500/call
Resume Parsing
$0.0111/call
Chain of Thought
$0.0538/call
Legal Analysis
$0.0525/call
Math Reasoning
$0.0319/call
Financial Analysis
$0.0500/call
Content Moderation
$0.001625/call
Sentiment Analysis
$0.001625/call
Categorization
$0.002500/call
Intent Detection
$0.001375/call
Copywriting
$0.0206/call
Blog Writing
$0.0813/call
Marketing Copy
$0.0310/call
Product Descriptions
$0.0156/call
Related pages
Frequently asked questions
How much does Gemini 2.5 Computer Use cost for pdf summarization per API call?
A typical pdf summarization call using Gemini 2.5 Computer Use costs about $0.0338 per call. This estimate uses 15,000 input tokens and 1,500 output tokens at standard rates. Standard rates are $1.25 per million input tokens and $10.00 per million output tokens.
Is Gemini 2.5 Computer Use the best model for pdf summarization?
Our benchmark data does not yet include a pdf summarization ranking for Gemini 2.5 Computer Use. We recommend testing it against your own evaluation criteria before production usage.
What is the cheapest model for pdf summarization with good quality?
The most affordable model for pdf summarization above 80/100 quality is GPT 5.6 Luna. It comes from GPT at $0.20 per million input tokens. Output costs $1.20 per million tokens with a quality score of 84/100. Switching from Gemini 2.5 Computer Use to GPT 5.6 Luna saves about 86% per API call.
How can I reduce my Gemini 2.5 Computer Use costs for pdf summarization?
Three strategies lower Gemini 2.5 Computer Use costs for this task type effectively. Prompt caching gives a 0% 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 $0.0338 to about $0.0169 per call.
How does Gemini 2.5 Computer Use pricing compare to other Gemini models?
Gemini has 15 other models alongside Gemini 2.5 Computer Use today: Gemini 3.1 Pro at $2.00/$12.00 per million tokens (mid-tier), Gemini 3.5 Flash at $1.50/$9.00 per million tokens (fast / budget), Gemini 3 Flash at $0.50/$3.00 per million tokens (fast / budget), Gemini 3.5 Flash-Lite at $0.30/$2.50 per million tokens (fast / budget), Gemini 2.5 Flash at $0.30/$2.50 per million tokens (fast / budget), Gemini 2.5 Flash-Lite at $0.10/$0.40 per million tokens (fast / budget), Gemini 2.5 Pro at $1.25/$10.00 per million tokens (mid-tier), Gemini 3.1 Flash-Lite at $0.25/$1.50 per million tokens (fast / budget), Gemini 3.1 Pro Custom Tools at $2.00/$12.00 per million tokens (mid-tier), Gemini 3.6 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini 3.7 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini 3.8 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini Omni 1.1 Flash at $1.50/$9.00 per million tokens (fast / budget), Gemini Omni Flash at $1.50/$9.00 per million tokens (fast / budget), Gemini Robotics ER 2 at $1.00/$5.00 per million tokens (fast / budget). The right choice depends on whether your pdf summarization workload prioritizes quality, cost, or speed.
What context window does Gemini 2.5 Computer Use support?
Gemini 2.5 Computer Use supports a context window of 128K 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.