Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.001406
Output: $0.001406
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 60,000 input tokens and 1,500 output tokens:
- Input Cost: $0.032850 (rounded ~ $0.03)
- Output Cost: $0.001406
- Total Cost: $0.019474
- Cost per 1K tokens: $0.000110
- Tokens per dollar: 9,073,753 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: 9 minutes, 16.27 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 3.1 Flash Lite
Google
|
$0.006585 (rounded ~ $0.01) Best Value | ↓ 66.2% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.008165 (rounded ~ $0.01) | ↓ 58.1% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.008165 (rounded ~ $0.01) | ↓ 58.1% cheaper |
| #4 |
Gemini 3.1 Flash
Google
|
$0.026340 (rounded ~ $0.03) | ↑ 35.3% more |
| #5 |
Gemini 3.6 Flash
Google
|
$0.038948 (rounded ~ $0.04) | ↑ 100% more |
| #6 |
Gemini 3.5 Flash
Google
|
$0.039510 | ↑ 102.9% more |
| #7 |
Gemini 2.5 Pro
Google
|
$0.067725 (rounded ~ $0.07) | ↑ 247.8% more |
| #8 |
Grok 4.3
xAI
|
$0.099360 | ↑ 410.2% more |
| #9 |
Grok 4.3
xAI
|
$0.099360 | ↑ 410.2% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.1 Flash Google
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Gemini 2.5 Pro Google
Grok 4.3 xAI
Grok 4.3 xAI
Gemini 3.8 Flash for Audio Transcription
For social media managers integrating voice data into their workflows, Gemini 3.8 Flash offers a compelling balance of multimodal reasoning and high-speed processing. When handling 60 minutes of audio content, the model’s ability to ingest raw audio files directly eliminates the need for a separate, error-prone transcription pre-processing step. This native audio capability simplifies your pipeline, allowing you to move straight from raw audio to actionable summaries, sentiment analysis, or social media post drafts in a single API request.
The strength of Gemini 3.8 Flash in this context is its multimodal architecture. Unlike traditional models that require text-based transcripts first, this model interprets the audio stream’s nuances—such as tone, pace, and emphasis—which can be crucial for capturing the intent of customer calls or interview recordings. Because it handles various audio formats natively, your infrastructure remains lean, reducing the complexity of your stack.
However, when evaluating this model for voice-first applications, consider the end-to-end latency requirements. While it is highly capable for batch processing or asynchronous tasks, ensure your architecture accounts for the processing time if you require near-instant responses. For high-volume transcription batches, the ability to process long-form audio in one pass makes it a highly efficient choice, provided your application doesn’t demand sub-200ms real-time interaction. It remains an excellent selection for teams seeking a balance between depth of comprehension and the throughput needed for enterprise-scale content production.