Gemini 3.1 Pro Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.013500 (rounded ~ $0.01)
Output: $0.013500 (rounded ~ $0.01)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 1,500 output tokens:
- Input Cost: $2.000000
- Output Cost: $0.013500 (rounded ~ $0.01)
- Total Cost: $1.293500 (rounded ~ $1.29)
- Cost per 1K tokens: $0.001292
- Tokens per dollar: 774,256 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 42 minutes, 8.97 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 396 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 |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.048938 (rounded ~ $0.05) Best Value | ↓ 96.2% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.121406 (rounded ~ $0.12) | ↓ 90.6% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.242813 (rounded ~ $0.24) | ↓ 81.2% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$0.811250 (rounded ~ $0.81) | ↓ 37.3% cheaper |
| #5 |
GPT-5.4
OpenAI
|
$1.616875 (rounded ~ $1.62) | ↑ 25% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.616875 (rounded ~ $1.62) | ↑ 25% more |
| #7 |
GPT-6 Astra
OpenAI
|
$6.475000 (rounded ~ $6.48) | ↑ 400.6% more |
| #8 |
GPT-6 Astra
OpenAI
|
$6.475000 (rounded ~ $6.48) | ↑ 400.6% more |
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
Large-Context Financial Parsing
Processing 10-K filings and other multi-hundred-page financial reports requires significant working memory. With a 2,000,000 token context window, Gemini 3.1 Pro is uniquely positioned to handle entire financial filings in a single pass. For healthcare administrators and analysts, this eliminates the need for complex chunking strategies or sliding-window approaches that often break the continuity of financial tables and linked disclosures.
Why Gemini 3.1 Pro for Finance? The primary advantage here is native multimodal support and the massive context capacity. Many financial filings contain dense tables and visual charts embedded within text. Gemini’s ability to process these as a cohesive document allows for more accurate extraction of tabular data compared to models that must process text and vision separately or require manual document splitting.
Operational Considerations: When analyzing these filings at scale, Gemini 3.1 Pro allows for a ‘single-prompt’ approach to complex queries. Instead of querying section-by-section and aggregating results, you can prompt the model to compare revenue trends directly against risk disclosures found hundreds of pages later. This reduces the latency of the overall pipeline and significantly simplifies the orchestration logic required in your backend services.
For teams focused on volume, Gemini 3.1 Pro offers a highly efficient path to structured data. It performs best when your prompts are specific about the desired output schema, such as JSON-formatted income statements or risk factor categorization. The model’s deep reasoning capabilities are particularly effective for identifying anomalies or inconsistencies that would otherwise require hours of manual auditing.