Gemini 3.1 Flash Lite Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.003000
Output: $0.003000
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 2,000 output tokens:
- Input Cost: $0.250000
- Output Cost: $0.003000
- Total Cost: $0.253000 (rounded ~ $0.25)
- Cost per 1K tokens: $0.000252
- Tokens per dollar: 3,960,474 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 1,000 tokens per second and 80ms time to first token:
- Processing Time: 17 minutes, 32.28 seconds
- Latency: 80 milliseconds to first token
- Base Throughput: 1,000 tokens/second
- Effective Throughput: 952 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.1 Flash Lite| Rank | AI Model & Provider | Total Cost | vs Gemini 3.1 Flash Lite |
|---|---|---|---|
| 🏆 |
Llama 4 Scout
Meta AI
|
$0.080600 Best Value | ↓ 68.1% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.305000 (rounded ~ $0.31) | ↑ 20.6% more |
| 🥉 |
Gemini 3.8 Flash
Google
|
$0.757500 (rounded ~ $0.76) | ↑ 199.4% more |
| #4 |
Gemini 3.6 Flash
Google
|
$1.515000 (rounded ~ $1.52) | ↑ 498.8% more |
| #5 |
Gemini 2.5 Pro
Google
|
$2.530000 | ↑ 900% more |
| #6 |
GPT-5.4
OpenAI
|
$5.045000 (rounded ~ $5.05) | ↑ 1894.1% more |
| #7 |
GPT-5.4 Thinking
OpenAI
|
$5.045000 (rounded ~ $5.05) | ↑ 1894.1% more |
| #8 |
GPT-6 Astra
OpenAI
|
$20.200000 | ↑ 7884.2% more |
| #9 |
GPT-6 Astra
OpenAI
|
$20.200000 | ↑ 7884.2% more |
Llama 4 Scout Meta AI
Gemini 3.5 Flash-Lite Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Gemini 2.5 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Streamlining Enterprise Document Pipelines
For customer support teams managing a high volume of incoming legal documents, the bottleneck is often the initial data ingestion and structural recognition. Traditional OCR workflows require a multi-stage process of conversion, text extraction, and then inference. Gemini 3.1 Flash Lite fundamentally changes this equation by offering native multimodal capabilities that allow for direct processing of document images and complex PDFs within a single call.
This model is specifically engineered for high-throughput scenarios where cost efficiency is as critical as accuracy. In the context of reviewing 50-page contracts, its ability to identify headers, footers, and table structures without losing the linguistic context of the surrounding clauses is a significant advantage. It is particularly effective for large-scale extraction tasks where the primary goal is to populate a database with key terms, dates, and parties from thousands of documents simultaneously.
While larger models might offer deeper philosophical reasoning, the speed and architectural optimization of this lite-tier model make it the practical choice for the heavy lifting phase of a legal review pipeline. It excels at maintaining accuracy across long context windows while providing the low-latency responses required for real-time support dashboards. For leads looking to scale their document processing without the overhead of massive infrastructure, this model represents a balance of high-volume utility and multimodal intelligence.