Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.000469
Output: $0.000469
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 500 output tokens:
- Input Cost: $216.187500 (rounded ~ $216.19)
- Output Cost: $0.000469
- Total Cost: $99.446719 (rounded ~ $99.45)
- Cost per 1K tokens: $0.000086
- Tokens per dollar: 11,594,153 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 340 tokens per second and 105ms time to first token:
- Processing Time: 1007 hours, 56 minutes, 0.58 seconds
- Latency: 105 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 318 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.8 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.8 Flash |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$39.778813 (rounded ~ $39.78) Best Value | ↓ 60% cheaper |
| 🥈 |
Gemini 3.6 Flash
Google
|
$198.893438 (rounded ~ $198.89) | ↑ 100% more |
| 🥉 |
Gemini 3.6 Flash
Google
|
$198.893438 (rounded ~ $198.89) | ↑ 100% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.6 Flash Google
Gemini 3.6 Flash Google
Scaling Audio Analysis Workflows
For enterprises processing 10,000 hours of audio content monthly, the primary challenge is not just transcription, but the depth of analysis required to extract actionable insights. Gemini 3.8 Flash introduces advanced agentic capabilities that allow it to navigate long-form audio files effectively. Instead of a linear processing approach, the model can selectively inspect transcripts and audio features, focusing computational effort only where necessary to answer complex queries regarding content, tone, and compliance.
This efficiency is critical for media organizations and call centers that need to summarize thousands of hours of audio without incurring the massive token costs associated with full-file verbatim processing. The model’s ability to handle audio directly as a modality—without requiring intermediate transcription steps for every task—significantly reduces pipeline complexity. By using native audio understanding, teams can bypass legacy speech-to-text bottlenecks and move directly to summarization, sentiment analysis, and metadata tagging.
When evaluating this model, consider the trade-off between its agentic reasoning depth and the latency required for high-volume, real-time tasks. While it excels at deep-dive document and audio-heavy workflows, teams running strictly low-latency, short-form interactions may find it provides more reasoning capability than the task requires. However, for deep narrative analysis and complex audio content enrichment at enterprise scale, its multimodal integration provides a clear throughput advantage over models reliant on fragmented text-only pipelines.