Gemini 3.1 Flash Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.006000 (rounded ~ $0.01)
Output: $0.006000 (rounded ~ $0.01)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Quality: 720p
FPS: 24fps
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 2,000 output tokens:
- Input Cost: $0.089450
- Output Cost: $0.006000 (rounded ~ $0.01)
- Total Cost: $0.095450 (rounded ~ $0.10)
- Cost per 1K tokens: $0.000528
- Tokens per dollar: 1,895,233 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 800 tokens per second and 100ms time to first token:
- Processing Time: 3 minutes, 53.09 seconds
- Latency: 100 milliseconds to first token
- Base Throughput: 800 tokens/second
- Effective Throughput: 777 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.1 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.1 Flash |
|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$0.047725 (rounded ~ $0.05) Best Value | ↓ 50% cheaper |
| 🥈 |
Gemini 2.5 Flash
Google
|
$0.058670 (rounded ~ $0.06) | ↓ 38.5% cheaper |
| 🥉 |
Gemini 3.8 Flash
Google
|
$0.141675 (rounded ~ $0.14) | ↑ 48.4% more |
| #4 |
Grok 4.3
xAI
|
$0.228625 (rounded ~ $0.23) | ↑ 139.5% more |
| #5 |
Gemini 2.5 Pro
Google
|
$0.243625 (rounded ~ $0.24) | ↑ 155.2% more |
| #6 |
Gemini 3.6 Flash
Google
|
$0.283350 (rounded ~ $0.28) | ↑ 196.9% more |
| #7 |
Gemini 3.5 Flash
Google
|
$0.286350 (rounded ~ $0.29) | ↑ 200% more |
| #8 |
Gemini 3.5 Flash
Google
|
$0.286350 (rounded ~ $0.29) | ↑ 200% more |
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
Grok 4.3 xAI
Gemini 2.5 Pro Google
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Gemini 3.5 Flash Google
Processing video for storyboarding is a compute-heavy challenge that requires balancing temporal resolution with inferential depth. Gemini 3.1 Flash has emerged as a go-to model for developers tackling this specific bottleneck. By leveraging its native multimodal capabilities, you can feed raw video clips directly into the model to generate scene descriptions, identify key frames, or track objects across a narrative arc without the need for cumbersome pre-processing pipelines or custom OCR solutions.
The real advantage of using Gemini 3.1 Flash for this workload lies in its architectural efficiency. When you are analyzing 300 seconds of footage, you are essentially asking the model to digest a massive sequence of visual information in a single pass. Gemini 3.1 Flash handles this stream remarkably well, maintaining coherence across temporal gaps that often trip up less capable models. This makes it an ideal candidate for features like automated scene logging or rapid narrative analysis where you need to extract metadata from video files quickly.
However, developers should be mindful that high-quality video analysis is as much about prompt engineering as it is about the model itself. To get the best results, structure your input to focus on the specific elements you need—such as lighting, character position, or action beats—rather than asking for a generic summary. By targeting the model’s attention toward specific storyboard-relevant details, you can significantly improve the accuracy of the output while keeping the overall compute footprint manageable for your app’s architecture.