Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.000469
Output: $0.000469
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 15,000 input tokens and 500 output tokens:
- Input Cost: $1.082813 (rounded ~ $1.08)
- Output Cost: $0.000469
- Total Cost: $0.888375 (rounded ~ $0.89)
- Cost per 1K tokens: $0.000154
- Tokens per dollar: 6,501,196 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: 4 hours, 48 minutes, 46.68 seconds
- Latency: 105 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 333 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
|
$0.296156 (rounded ~ $0.30) Best Value | ↓ 66.7% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.355475 (rounded ~ $0.36) | ↓ 60% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.355475 (rounded ~ $0.36) | ↓ 60% cheaper |
| #4 |
Gemini 3.6 Flash
Google
|
$0.888375 (rounded ~ $0.89) | Same price |
| #5 |
Gemini 4 Argon
Google
|
$2.369000 | ↑ 166.7% more |
| #6 |
Gemini 3.1 Flash
Google
|
$2.369250 | ↑ 166.7% more |
| #7 |
Gemini 2.5 Pro
Google
|
$5.923125 (rounded ~ $5.92) | ↑ 566.7% more |
| #8 |
Grok 4.3
xAI
|
$9.473000 (rounded ~ $9.47) | ↑ 966.3% more |
| #9 |
Grok 4.3
xAI
|
$9.473000 (rounded ~ $9.47) | ↑ 966.3% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.6 Flash Google
Gemini 4 Argon Google
Gemini 3.1 Flash Google
Gemini 2.5 Pro Google
Grok 4.3 xAI
Grok 4.3 xAI
Medical transcription workflows demand high throughput and reliable audio processing. Gemini 3.8 Flash stands out in this domain due to its native multi-modal capabilities, specifically its ability to handle audio-input streams without requiring a separate transcription service. For clinical environments processing 50 hours of audio monthly, the efficiency of a single-pass model significantly reduces architectural complexity.
When evaluating this model for healthcare compliance, consultants must prioritize the model’s ability to maintain context over long, unstructured conversations. The 1,048,576 token context window allows for entire patient-physician encounters to be processed in a single call, which is vital for maintaining semantic coherence during summarization or coding tasks.
However, the primary decision factor for healthcare IT teams is often the trade-off between latency and accuracy. While Flash-tier models are optimized for speed, RAG-based systems utilizing them for clinical coding must ensure that the retrieval pipeline provides precise, high-quality documentation. We recommend running a pilot to validate the model’s performance on domain-specific terminology before scaling to full production volumes. The integration of structured reasoning ensures that clinical summaries are consistent with standard medical documentation requirements, minimizing the need for human oversight. This model is a strong candidate for teams looking to balance performance with the operational realities of high-volume clinical document processing.