Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.001875
Output: $0.001875
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 2,000 output tokens:
- Input Cost: $0.205500 (rounded ~ $0.21)
- Output Cost: $0.001875
- Total Cost: $0.114900 (rounded ~ $0.11)
- Cost per 1K tokens: $0.000105
- Tokens per dollar: 9,556,136 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: 57 minutes, 35.65 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
|
$0.046460 (rounded ~ $0.05) Best Value | ↓ 59.6% cheaper |
| 🥈 |
Gemini 3.6 Flash
Google
|
$0.114900 (rounded ~ $0.11) | Same price |
| 🥉 |
Gemini 4 Argon
Google
|
$0.306400 (rounded ~ $0.31) | ↑ 166.7% more |
| #4 |
Gemini 4 Argon
Google
|
$0.306400 (rounded ~ $0.31) | ↑ 166.7% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.6 Flash Google
Gemini 4 Argon Google
Gemini 4 Argon Google
Optimizing Real-Time Voice Agents with Gemini 3.8 Flash
For developers building voice-first applications, latency and multimodal throughput are the primary bottlenecks. Gemini 3.8 Flash offers a compelling balance for high-volume audio agent pipelines, providing native audio-input capabilities that bypass the need for separate transcription services in many conversational flows. By consolidating the transcription and reasoning steps, developers can significantly reduce round-trip time, which is critical for maintaining sub-second interaction loops in production.
The model’s integration of thinking capabilities allows it to modulate compute effort dynamically, making it a strong candidate for complex agentic workflows where response accuracy is balanced against strict latency budgets. Unlike general-purpose text models that require external speech-to-text pre-processing, Gemini 3.8 Flash handles audio streams natively, reducing the potential for cumulative errors and latency overhead typically introduced by multi-stage pipelines.
For SaaS teams scaling to 1M-token-per-day workloads, the model’s efficiency is notable. It avoids the compute bloat of larger frontier models while maintaining sufficient reasoning depth to handle multi-step agentic instructions without falling back to a secondary model. This makes it a primary choice for high-volume, cost-sensitive applications where the user experience depends on rapid, coherent, and multimodal response cycles.