Gemini 3.1 Flash Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.001500
Output: $0.001500
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 500 output tokens:
- Input Cost: $576.050000
- Output Cost: $0.001500
- Total Cost: $316.829000
- Cost per 1K tokens: $0.000275
- Tokens per dollar: 3,636,348 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: 408 hours, 2 minutes, 8.32 seconds
- Latency: 100 milliseconds to first token
- Base Throughput: 800 tokens/second
- Effective Throughput: 784 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
|
$39.603625 (rounded ~ $39.60) Best Value | ↓ 87.5% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$47.524438 (rounded ~ $47.52) | ↓ 85% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$47.524438 (rounded ~ $47.52) | ↓ 85% cheaper |
| #4 |
Gemini 3.8 Flash
Google
|
$118.810781 | ↓ 62.5% cheaper |
| #5 |
Gemini 3.6 Flash
Google
|
$237.621563 (rounded ~ $237.62) | ↓ 25% cheaper |
| #6 |
Gemini 3.5 Flash
Google
|
$237.621750 (rounded ~ $237.62) | ↓ 25% cheaper |
| #7 |
Gemini 2.5 Pro
Google
|
$792.072500 (rounded ~ $792.07) | ↑ 150% more |
| #8 |
Grok 4.3
xAI
|
$1267.312000 (rounded ~ $1,267.31) | ↑ 300% more |
| #9 |
Grok 4.3
xAI
|
$1267.312000 (rounded ~ $1,267.31) | ↑ 300% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 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
Scaling Audio Pipelines for EdTech
For EdTech platforms, transcribing educational podcasts at scale—such as 10,000 hours of content—requires a model that balances speed with high-fidelity diarization. Gemini 3.1 Flash is designed for these high-throughput requirements, offering native multimodal capabilities that handle long-form audio files efficiently. When processing large archives of lecture materials or student-tutor interactions, the ability to maintain context across lengthy sessions is paramount.
Gemini 3.1 Flash excels in workflows where latency and cost-efficiency are critical, particularly for platforms that need to generate searchable transcripts or automated summaries immediately after a recording is uploaded. Unlike general-purpose text models that require secondary conversion steps, this model handles audio input natively, reducing the complexity of the ingestion pipeline. For teams managing massive datasets, the stability of the audio ingestion process is a significant operational advantage, ensuring that transcription quality remains consistent even during peak usage hours. When evaluating this model for your transcription infrastructure, consider how its multimodal integration streamlines the path from raw audio to structured, actionable learning insights, helping you focus resources on pedagogical improvements rather than infrastructure maintenance.