Gemini 3.1 Pro Google 2000000
💰 Total Cost Calculation (from Plugin)
Output: $0.018000 (rounded ~ $0.02)
Output: $0.018000 (rounded ~ $0.02)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 2,000 output tokens:
- Input Cost: $2.000000
- Output Cost: $0.018000 (rounded ~ $0.02)
- Total Cost: $1.118000 (rounded ~ $1.12)
- Cost per 1K tokens: $0.001116
- Tokens per dollar: 896,243 tokens
- Context Window: 2000000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 42 minutes, 35.28 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 392 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.1 Pro| Rank | AI Model & Provider | Total Cost | vs Gemini 3.1 Pro |
|---|---|---|---|
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Grok 4.20 Beta
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$0.278000 (rounded ~ $0.28) Best Value | ↓ 75.1% cheaper |
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$0.702500 (rounded ~ $0.70) | ↓ 37.2% cheaper |
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$1.397500 (rounded ~ $1.40) | ↑ 25% more |
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$1.397500 (rounded ~ $1.40) | ↑ 25% more |
| #5 |
GPT-5.4 Thinking
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$1.397500 (rounded ~ $1.40) | ↑ 25% more |
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.4 Thinking OpenAI
When scaling legal contract review to 1 Million Tokens Monthly, the choice of model hinges on the ability to maintain logical consistency across massive document sets. Gemini 3.1 Pro provides a robust framework for handling multi-document due diligence, where extracting specific clauses like liability caps or termination dates requires both high-precision reasoning and deep context retention.
For educational content creators developing training modules on M&A or regulatory compliance, this model offers a distinct advantage in its ability to process entire libraries of contracts without losing track of cross-referenced definitions. The key here is the model’s capacity to digest extended legal prose while maintaining a structured output that can be easily parsed by downstream database systems.
While many smaller models struggle with the nuanced interplay between standard boilerplate language and custom deal-specific clauses, this architecture is designed to prioritize factual grounding, reducing the likelihood of hallucinated obligations. When constructing your curriculum, emphasize how the model’s multimodal capabilities allow for the inclusion of scanned, non-searchable PDF exhibits, which are common in legacy legal databases. By focusing on the model’s ability to maintain a coherent narrative across high-volume batches, you can teach students how to build reliable, scalable legal tech stacks that save time without sacrificing the integrity of the review process. This approach helps students understand the real-world trade-offs between speed, accuracy, and depth in AI-assisted legal engineering.