Gemini 3.6 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.002813
Output: $0.002813
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 1,500 output tokens:
- Input Cost: $0.375000 (rounded ~ $0.38)
- Output Cost: $0.002813
- Total Cost: $0.124688 (rounded ~ $0.12)
- Cost per 1K tokens: $0.000125
- Tokens per dollar: 8,032,080 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 304 tokens per second and 120ms time to first token:
- Processing Time: 56 minutes, 0.48 seconds
- Latency: 120 milliseconds to first token
- Base Throughput: 304 tokens/second
- Effective Throughput: 298 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.6 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.6 Flash |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.025313 (rounded ~ $0.03) Best Value | ↓ 79.7% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.062344 (rounded ~ $0.06) | ↓ 50% cheaper |
| 🥉 |
Gemini 2.5 Pro
Google
|
$0.417500 (rounded ~ $0.42) | ↑ 234.8% more |
| #4 |
GPT-5.4
OpenAI
|
$0.829375 | ↑ 565.2% more |
| #5 |
GPT-5.4 Thinking
OpenAI
|
$0.829375 | ↑ 565.2% more |
| #6 |
GPT-6 Astra
OpenAI
|
$3.325000 (rounded ~ $3.33) | ↑ 2566.7% more |
| #7 |
GPT-6 Astra
OpenAI
|
$3.325000 (rounded ~ $3.33) | ↑ 2566.7% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.8 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 Clinical Documentation with Gemini 3.6 Flash
For high-volume clinical pipelines processing up to 50 million tokens monthly, efficiency and token economics are the primary drivers for technical architecture. Gemini 3.6 Flash has emerged as a specialized workhorse for this scale of healthcare documentation, balancing high-speed inference with the multimodal capabilities necessary for modern medical record generation.
Clinical note generation often requires the ingestion of not just text, but diagnostic charts, lab reports, and even annotated sketches. Gemini 3.6 Flash is uniquely positioned for this workload due to its native multimodal understanding, which allows it to process complex visual artifacts alongside transcriptions in a single pass. This reduces the latency often introduced by multi-stage pipelines that separate OCR from summarization.
The model’s high token efficiency ensures that the cost-per-note remains stable even as volume increases. For SaaS platforms and agency-managed clinical services, this predictability is vital. Furthermore, the model’s performance in multi-step orchestration tasks—where the AI must query external clinical databases, summarize findings, and output structured JSON records—is highly optimized for low-latency production environments. While larger frontier models might offer slightly higher peak reasoning, the marginal gains are often offset by the operational speed and cost-efficiency that Flash provides at this massive scale.