Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.000938
Output: $0.000938
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 1,000 output tokens:
- Input Cost: $0.187500 (rounded ~ $0.19)
- Output Cost: $0.000938
- Total Cost: $0.104063 (rounded ~ $0.10)
- Cost per 1K tokens: $0.000104
- Tokens per dollar: 9,619,219 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: 52 minutes, 30.39 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
|
$0.041875 (rounded ~ $0.04) Best Value | ↓ 59.8% cheaper |
| 🥈 |
Gemini 3.6 Flash
Google
|
$0.208125 (rounded ~ $0.21) | ↑ 100% more |
| 🥉 |
Gemini 2.5 Pro
Google
|
$0.695000 (rounded ~ $0.70) | ↑ 567.9% more |
| #4 |
GPT-5.4
OpenAI
|
$1.386250 (rounded ~ $1.39) | ↑ 1232.1% more |
| #5 |
GPT-5.4 Thinking
OpenAI
|
$1.386250 (rounded ~ $1.39) | ↑ 1232.1% more |
| #6 |
GPT-6 Astra
OpenAI
|
$5.550000 | ↑ 5233.3% more |
| #7 |
GPT-6 Astra
OpenAI
|
$5.550000 | ↑ 5233.3% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.6 Flash Google
Gemini 2.5 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Scaling Marketing Copy with High-Throughput Models
For solo founders building marketing automation tools, the ability to generate bulk campaign variations at scale is essential. Gemini 3.8 Flash provides a unique advantage by balancing rapid throughput with complex instruction-following capabilities. When handling high-volume workloads—potentially reaching 1 billion tokens monthly—you need a model that maintains consistent quality without succumbing to latency issues. This model is particularly effective for generating hundreds of A/B test variations simultaneously, allowing you to iterate on your marketing hypotheses faster than your competitors.
Beyond pure speed, its multimodal capabilities offer an interesting edge. If your marketing pipeline involves processing visual assets alongside copy, the ability to handle both within a single request can significantly simplify your infrastructure. This reduces the need for complex, multi-stage pipelines that often introduce points of failure. As you scale, the reliability of this model ensures that your marketing assets remain consistent, even as you increase your output volume. Its integration within a broader enterprise ecosystem makes it a robust choice for founders looking to build a sustainable, cost-effective growth engine. By leveraging its efficient token processing, you can focus on optimizing your conversion funnels rather than worrying about the underlying infrastructure constraints. Testing this model against your specific use cases early will reveal how its performance characteristics align with your long-term growth goals.