Gemini 3.5 Flash Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.011250 (rounded ~ $0.01)
Output: $0.011250 (rounded ~ $0.01)
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 5,000 output tokens:
- Input Cost: $0.109500
- Output Cost: $0.011250 (rounded ~ $0.01)
- Total Cost: $0.101040 (rounded ~ $0.10)
- Cost per 1K tokens: $0.000340
- Tokens per dollar: 2,939,430 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 850 tokens per second and 90ms time to first token:
- Processing Time: 5 minutes, 56.58 seconds
- Latency: 90 milliseconds to first token
- Base Throughput: 850 tokens/second
- Effective Throughput: 833 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.5 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.5 Flash |
|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$0.016840 (rounded ~ $0.02) Best Value | ↓ 83.3% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.021083 (rounded ~ $0.02) | ↓ 79.1% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.021083 (rounded ~ $0.02) | ↓ 79.1% cheaper |
| #4 |
Gemini 3.8 Flash
Google
|
$0.049583 | ↓ 50.9% cheaper |
| #5 |
Gemini 3.6 Flash
Google
|
$0.099165 | ↓ 1.9% cheaper |
| #6 |
Gemini 3.1 Flash
Google
|
$0.134720 (rounded ~ $0.13) | ↑ 33.3% more |
| #7 |
Gemini 2.5 Pro
Google
|
$0.336800 (rounded ~ $0.34) | ↑ 233.3% more |
| #8 |
Grok 4.3
xAI
|
$0.498880 (rounded ~ $0.50) | ↑ 393.7% more |
| #9 |
Grok 4.3
xAI
|
$0.498880 (rounded ~ $0.50) | ↑ 393.7% 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 3.1 Flash Google
Gemini 2.5 Pro Google
Grok 4.3 xAI
Grok 4.3 xAI
For developers building podcast transcription apps, Gemini 3.5 Flash offers a streamlined approach to audio processing. Because it is natively multimodal, you can send raw audio files directly to the model without pre-processing them through a separate speech-to-text pipeline. This native integration is a significant advantage for mobile development, as it reduces the complexity of your backend architecture and minimizes latency issues often associated with chaining multiple API calls.
When handling 100 minutes of podcast audio, accuracy and speaker diarization are paramount. Gemini 3.5 Flash performs exceptionally well in distinguishing between multiple speakers, which is critical for creating readable transcripts of long-form conversations. Its ability to understand the context of the audio rather than just the literal words allows it to better handle interruptions, overlapping speech, and informal dialogue—all common in podcast recordings.
Beyond simple transcription, the model’s reasoning capabilities mean you can perform downstream tasks such as generating show notes, extracting key insights, or creating social media clips directly in the same workflow. This consolidation simplifies your app’s logic. However, for mobile developers, keep in mind that larger audio files should be managed efficiently to stay within the context window limits. If your use case requires high-frequency transcription of very long episodes, you should consider implementing a chunking strategy to maintain performance. Gemini 3.5 Flash is currently a top choice for developers who prioritize an all-in-one multimodal solution over managing complex, multi-service transcription pipelines.