Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.001875
Output: $0.001875
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Resolution: Medium
Tokens: 25,800
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 2,000 output tokens:
- Input Cost: $0.098588 (rounded ~ $0.10)
- Output Cost: $0.001875
- Total Cost: $0.056098 (rounded ~ $0.06)
- Cost per 1K tokens: $0.000106
- Tokens per dollar: 9,408,514 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: 27 minutes, 41.20 seconds
- Latency: 105 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 318 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Gemini 3.8 Flash. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to Gemini 3.8 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.8 Flash |
|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$0.018824 (rounded ~ $0.02) Best Value | ↓ 66.4% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.022939 (rounded ~ $0.02) | ↓ 59.1% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.022939 (rounded ~ $0.02) | ↓ 59.1% cheaper |
| #4 |
GPT-5.6 Luna
OpenAI
|
$0.075298 (rounded ~ $0.08) | ↑ 34.2% more |
| #5 |
Gemini 3.6 Flash
Google
|
$0.112196 (rounded ~ $0.11) | ↑ 100% more |
| #6 |
Gemini 3.5 Flash
Google
|
$0.112946 (rounded ~ $0.11) | ↑ 101.3% more |
| #7 |
Claude Sonnet 5
Anthropic
|
$0.149595 | ↑ 166.7% more |
| #8 |
Gemini 3.1 Flash
Google
|
$0.150595 | ↑ 168.4% more |
| #9 |
GPT-5.6 Terra
OpenAI
|
$0.188244 (rounded ~ $0.19) | ↑ 235.6% more |
| #10 |
Claude Sonnet 4.6
Anthropic
|
$0.224393 (rounded ~ $0.22) | ↑ 300% more |
| #11 |
Claude Opus 4.7
Anthropic
|
$0.373988 (rounded ~ $0.37) | ↑ 566.7% more |
| #12 |
Claude Opus 5
Anthropic
|
$0.373988 (rounded ~ $0.37) | ↑ 566.7% more |
| #13 |
Claude Opus 4.8
Anthropic
|
$0.373988 (rounded ~ $0.37) | ↑ 566.7% more |
| #14 |
Claude Opus 4.6
Anthropic
|
$0.373988 (rounded ~ $0.37) | ↑ 566.7% more |
| #15 |
Gemini 2.5 Pro
Google
|
$0.376488 (rounded ~ $0.38) | ↑ 571.1% more |
| #16 |
GPT-5.6 Sol
OpenAI
|
$0.376488 (rounded ~ $0.38) | ↑ 571.1% more |
| #17 |
Grok 4.3
xAI
|
$0.586380 (rounded ~ $0.59) | ↑ 945.3% more |
| #18 |
Gemini 3.1 Pro
Google
|
$0.596380 (rounded ~ $0.60) | ↑ 963.1% more |
| #19 |
Claude Fable 5.1
Anthropic
|
$0.698681 (rounded ~ $0.70) | ↑ 1145.5% more |
| #20 |
Claude Mythos 5.1
Anthropic
|
$0.698681 (rounded ~ $0.70) | ↑ 1145.5% more |
| #21 |
GPT-5.4
OpenAI
|
$0.745475 (rounded ~ $0.75) | ↑ 1228.9% more |
| #22 |
GPT-5.4 Thinking
OpenAI
|
$0.745475 (rounded ~ $0.75) | ↑ 1228.9% more |
| #23 |
Claude Fable 5
Anthropic
|
$0.747975 (rounded ~ $0.75) | ↑ 1233.3% more |
| #24 |
Claude Mythos 5
Anthropic
|
$0.747975 (rounded ~ $0.75) | ↑ 1233.3% more |
| #25 |
GPT-5.5
OpenAI
|
$1.490950 | ↑ 2557.8% more |
| #26 |
GPT-6 Astra
OpenAI
|
$2.991900 (rounded ~ $2.99) | ↑ 5233.3% more |
| #27 |
GPT-6 Astra
OpenAI
|
$2.991900 (rounded ~ $2.99) | ↑ 5233.3% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 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
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
Real estate inspection workflows require high-precision document and image analysis to identify structural issues or compliance gaps. When automating these tasks via agentic browser navigation, the choice of model hinges on the ability to handle long-context, multimodal input—such as inspection photos and site reports—and perform reliable tool calls for data extraction.
Gemini 3.8 Flash offers a compelling balance for high-volume inspection pipelines. Its integrated multimodal capabilities allow for seamless processing of visual evidence alongside descriptive text, significantly reducing the need for heavy pre-processing steps. This model is particularly effective when navigating web-based property management systems, as its tool-calling mechanism is optimized for complex, multi-step navigation tasks often found in site reporting.
For specialists dealing with high-stakes inspection reports, the decision often comes down to the trade-off between the model’s reasoning depth and its inference latency. While newer models push the boundaries of agentic performance, evaluating how effectively a model manages long-context sessions—where maintaining the state of a browser session is critical—remains the top priority. Choosing the right model for browser-based automation also involves considering ecosystem maturity. Integration with existing OCR and data processing pipelines is often as important as raw inference performance. When scaling inspection automation to thousands of properties, reliability becomes paramount. Look for models that minimize hallucination in structured data extraction, ensuring that lease details or property specifications are captured with high fidelity. By focusing on models that support robust reasoning and reliable tool execution, you can build an agentic architecture that enhances the overall quality of your inspection data.