Gemini 3.6 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.001875
Output: $0.001875
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 1,000 output tokens:
- Input Cost: $0.187500 (rounded ~ $0.19)
- Output Cost: $0.001875
- Total Cost: $0.054375 (rounded ~ $0.05)
- Cost per 1K tokens: $0.000109
- Tokens per dollar: 9,213,793 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 304 tokens per second and 120ms time to first token:
- Processing Time: 28 minutes, 1.17 seconds
- Latency: 120 milliseconds to first token
- Base Throughput: 304 tokens/second
- Effective Throughput: 298 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.6 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.6 Flash |
|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$0.009125 Best Value | ↓ 83.2% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.011125 (rounded ~ $0.01) | ↓ 79.5% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.011125 (rounded ~ $0.01) | ↓ 79.5% cheaper |
| #4 |
Gemini 3.8 Flash
Google
|
$0.027188 (rounded ~ $0.03) | ↓ 50% cheaper |
| #5 |
GPT-5.6 Luna
OpenAI
|
$0.036500 (rounded ~ $0.04) | ↓ 32.9% cheaper |
| #6 |
Gemini 3.5 Flash
Google
|
$0.054750 (rounded ~ $0.05) | ↑ 0.7% more |
| #7 |
Claude Sonnet 5
Anthropic
|
$0.072500 (rounded ~ $0.07) | ↑ 33.3% more |
| #8 |
Gemini 3.1 Flash
Google
|
$0.073000 (rounded ~ $0.07) | ↑ 34.3% more |
| #9 |
GPT-5.6 Terra
OpenAI
|
$0.091250 (rounded ~ $0.09) | ↑ 67.8% more |
| #10 |
Claude Sonnet 4.6
Anthropic
|
$0.108750 (rounded ~ $0.11) | ↑ 100% more |
| #11 |
Claude Opus 4.7
Anthropic
|
$0.181250 (rounded ~ $0.18) | ↑ 233.3% more |
| #12 |
Claude Opus 5
Anthropic
|
$0.181250 (rounded ~ $0.18) | ↑ 233.3% more |
| #13 |
Claude Opus 4.8
Anthropic
|
$0.181250 (rounded ~ $0.18) | ↑ 233.3% more |
| #14 |
Claude Opus 4.6
Anthropic
|
$0.181250 (rounded ~ $0.18) | ↑ 233.3% more |
| #15 |
Gemini 2.5 Pro
Google
|
$0.182500 (rounded ~ $0.18) | ↑ 235.6% more |
| #16 |
GPT-5.6 Sol
OpenAI
|
$0.182500 (rounded ~ $0.18) | ↑ 235.6% more |
| #17 |
Grok 4.3
xAI
|
$0.284000 (rounded ~ $0.28) | ↑ 422.3% more |
| #18 |
Grok 4.20 Beta
xAI
|
$0.284000 (rounded ~ $0.28) | ↑ 422.3% more |
| #19 |
Claude Fable 5.1
Anthropic
|
$0.287500 (rounded ~ $0.29) | ↑ 428.7% more |
| #20 |
Claude Mythos 5.1
Anthropic
|
$0.287500 (rounded ~ $0.29) | ↑ 428.7% more |
| #21 |
Gemini 3.1 Pro
Google
|
$0.289000 (rounded ~ $0.29) | ↑ 431.5% more |
| #22 |
GPT-5.4
OpenAI
|
$0.361250 (rounded ~ $0.36) | ↑ 564.4% more |
| #23 |
GPT-5.4 Thinking
OpenAI
|
$0.361250 (rounded ~ $0.36) | ↑ 564.4% more |
| #24 |
Claude Fable 5
Anthropic
|
$0.362500 (rounded ~ $0.36) | ↑ 566.7% more |
| #25 |
Claude Mythos 5
Anthropic
|
$0.362500 (rounded ~ $0.36) | ↑ 566.7% more |
| #26 |
GPT-5.5
OpenAI
|
$0.722500 (rounded ~ $0.72) | ↑ 1228.7% more |
| #27 |
GPT-6 Astra
OpenAI
|
$1.450000 | ↑ 2566.7% more |
| #28 |
GPT-6 Astra
OpenAI
|
$1.450000 | ↑ 2566.7% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.5 Flash Google
Claude Sonnet 5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
Gemini 2.5 Pro Google
GPT-5.6 Sol OpenAI
Grok 4.3 xAI
Grok 4.20 Beta xAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Gemini 3.1 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Summarizing high-volume document archives is a cornerstone of modern enterprise intelligence. For teams handling 1 million PDFs per month, the challenge isn’t just the sheer scale—it’s maintaining consistency and accuracy across diverse document types like legal contracts, financial reports, and technical manuals. Gemini 3.6 Flash is engineered specifically for these high-throughput, latency-sensitive environments. Because it supports massive context windows, it allows for end-to-end processing of long-form documents without aggressive chunking, which often loses critical structural context or cross-page references.
When you are processing documents at this scale, the primary bottleneck is often the trade-off between reasoning depth and system latency. Gemini 3.6 Flash excels here by providing a robust balance, allowing your team to move away from simple keyword-based extraction toward genuine semantic understanding. This model is particularly effective for workflows that require consistent, repeatable summarization of complex layouts, such as extracting clauses from contracts or identifying key metrics in quarterly earnings reports. By choosing a model optimized for high-volume, long-context pipelines, you minimize the risk of ‘context loss’ that typically plagues older generation systems. For organizations where document processing is the lifeblood of operations—from legal discovery to supply chain management—this model serves as a reliable, cost-efficient backbone for scaling your AI-driven insights without sacrificing the granular detail required for enterprise decision-making.