Gemini 3.5 Flash Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.002250
Output: $0.002250
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 1,000 output tokens:
- Input Cost: $0.187500 (rounded ~ $0.19)
- Output Cost: $0.002250
- Total Cost: $0.139125
- Cost per 1K tokens: $0.000278
- Tokens per dollar: 3,601,078 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: 10 minutes, 30.85 seconds
- Latency: 90 milliseconds to first token
- Base Throughput: 850 tokens/second
- Effective Throughput: 794 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
|
$0.023188 (rounded ~ $0.02) Best Value | ↓ 83.3% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.028000 (rounded ~ $0.03) | ↓ 79.9% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.028000 (rounded ~ $0.03) | ↓ 79.9% cheaper |
| #4 |
Gemini 3.8 Flash
Google
|
$0.069375 | ↓ 50.1% cheaper |
| #5 |
GPT-5.6 Luna
OpenAI
|
$0.092750 (rounded ~ $0.09) | ↓ 33.3% cheaper |
| #6 |
Gemini 3.6 Flash
Google
|
$0.138750 (rounded ~ $0.14) | ↓ 0.3% cheaper |
| #7 |
Claude Sonnet 5
Anthropic
|
$0.185000 (rounded ~ $0.19) | ↑ 33% more |
| #8 |
Gemini 3.1 Flash
Google
|
$0.185500 (rounded ~ $0.19) | ↑ 33.3% more |
| #9 |
GPT-5.6 Terra
OpenAI
|
$0.231875 (rounded ~ $0.23) | ↑ 66.7% more |
| #10 |
Claude Sonnet 4.6
Anthropic
|
$0.277500 (rounded ~ $0.28) | ↑ 99.5% more |
| #11 |
Claude Opus 4.7
Anthropic
|
$0.462500 (rounded ~ $0.46) | ↑ 232.4% more |
| #12 |
Claude Opus 5
Anthropic
|
$0.462500 (rounded ~ $0.46) | ↑ 232.4% more |
| #13 |
Claude Opus 4.8
Anthropic
|
$0.462500 (rounded ~ $0.46) | ↑ 232.4% more |
| #14 |
Claude Opus 4.6
Anthropic
|
$0.462500 (rounded ~ $0.46) | ↑ 232.4% more |
| #15 |
Gemini 2.5 Pro
Google
|
$0.463750 (rounded ~ $0.46) | ↑ 233.3% more |
| #16 |
GPT-5.6 Sol
OpenAI
|
$0.463750 (rounded ~ $0.46) | ↑ 233.3% more |
| #17 |
Grok 4.3
xAI
|
$0.734000 (rounded ~ $0.73) | ↑ 427.6% more |
| #18 |
Grok 4.20 Beta
xAI
|
$0.734000 (rounded ~ $0.73) | ↑ 427.6% more |
| #19 |
Gemini 3.1 Pro
Google
|
$0.739000 (rounded ~ $0.74) | ↑ 431.2% more |
| #20 |
Claude Fable 5.1
Anthropic
|
$0.896875 (rounded ~ $0.90) | ↑ 544.7% more |
| #21 |
Claude Mythos 5.1
Anthropic
|
$0.896875 (rounded ~ $0.90) | ↑ 544.7% more |
| #22 |
GPT-5.4
OpenAI
|
$0.923750 (rounded ~ $0.92) | ↑ 564% more |
| #23 |
GPT-5.4 Thinking
OpenAI
|
$0.923750 (rounded ~ $0.92) | ↑ 564% more |
| #24 |
Claude Fable 5
Anthropic
|
$0.925000 (rounded ~ $0.93) | ↑ 564.9% more |
| #25 |
Claude Mythos 5
Anthropic
|
$0.925000 (rounded ~ $0.93) | ↑ 564.9% more |
| #26 |
GPT-5.5
OpenAI
|
$1.847500 (rounded ~ $1.85) | ↑ 1227.9% more |
| #27 |
GPT-6 Astra
OpenAI
|
$3.700000 | ↑ 2559.5% more |
| #28 |
GPT-6 Astra
OpenAI
|
$3.700000 | ↑ 2559.5% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Claude Sonnet 5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
Gemini 2.5 Pro Google
GPT-5.6 Sol OpenAI
Grok 4.3 xAI
Grok 4.20 Beta xAI
Gemini 3.1 Pro Google
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Gemini 3.5 Flash is engineered for high-throughput, latency-sensitive applications, making it a compelling choice for companies managing large-scale document pipelines. When processing 50 million tokens monthly, the efficiency of your model selection becomes a significant factor in both system responsiveness and long-term architectural stability.
For a RAG chatbot deployed across large datasets—such as thousands of property descriptions, historical CMA reports, and market analyses—Gemini 3.5 Flash offers a robust balance of multimodal ingestion and rapid generation. It is particularly effective for workflows that require OCR or video analysis alongside text, allowing your chatbot to parse not just PDFs, but also site survey videos and floor plan images seamlessly within the same context window.
The performance profile of Gemini 3.5 Flash shines in scenarios where immediate, reliable responses are required at scale. Its large context window ensures that you can feed extensive documentation into the prompt without losing coherence, which is vital for maintaining the accuracy of complex real estate inquiries. Unlike models optimized purely for reasoning, Flash is built for throughput, ensuring that as your user base grows from hundreds to thousands of concurrent queries, the latency overhead remains minimal. Teams focusing on rapid deployment of RAG features should prioritize this model for its reliability in high-volume, multi-modal search and retrieval tasks, providing a consistent user experience without sacrificing depth.