Compare inference costs across GPT-5, Claude 4.5, Gemini 3, and more.
Now with agentic loops, caching, and reasoning tokens
In 2026, most AI applications use multi-step "agentic loops" rather than single prompts. Calculate costs for workflows like: "5 research steps + 1 summary step = 6 total steps."
Modern APIs (Anthropic, Gemini, OpenAI) offer context caching that reduces input costs by up to 90% for repeated data. Set your cache hit rate to see potential savings.
Reasoning models (like OpenAI's o-series) "think" before responding. These "thinking tokens" cost extra money and are often missed by older calculators. Enable reasoning to see the true cost.
Enable context caching to save up to 90% on repeated inputs
Visual comparison showing which model performs best for different use cases
| Model | Input Price | Output Price | Context | Total Cost | vs. Lowest |
|---|---|---|---|---|---|
Gemini 3 FlashGoogle | $0.07/1M | $0.30/1M | 1.0M | $0.004350 | Lowest |
Llama 4 MaverickMeta | $0.20/1M | $0.60/1M | 1.0M | $0.0112 | +$0.006850 (157%) |
Copy-paste code to integrate Gemini 3 Flash into your application
import google.generativeai as genai
genai.configure(api_key="your-api-key")
model = genai.GenerativeModel('gemini-3-flash')
response = model.generate_content(
"Your prompt here",
generation_config={
"max_output_tokens": 2000
}
)
print(response.text)💡 Replace "your-api-key" with your actual API key. Adjust tokens and parameters as needed.
Compare pricing, features, and total cost of ownership between Gemini 3 Flash (Google) and Llama 4 Maverick (Meta). Find out which AI model offers the best value for your use case.
Gemini 3 Flash is more affordable
63% cheaper input pricing
Llama 4 Maverick has larger context
1.0x more context capacity
Gemini 3 Flash offers lower input pricing at $0.07 per 1M tokens compared to $0.20 for Llama 4 Maverick. However, total costs depend on your usage patterns, output requirements, and whether you can leverage context caching. Use the calculator above to estimate costs for your specific use case.
Gemini 3 Flash supports up to 1,000,000 tokens, while Llama 4 Mavericksupports 1,000,000 tokens. Llama 4 Maverickcan handle longer documents and conversations without truncation, which is important for applications requiring extensive context.
Yes, many developers use multiple AI models for different tasks. You might use Gemini 3 Flashfor high-volume, cost-sensitive operations and Llama 4 Maverick for tasks requiring specific capabilities. The calculator above helps you compare costs across both models.
Both models support context caching, which can significantly reduce costs for repeated inputs. Gemini 3 Flash offers cached input at $0.02 per 1M tokens.Additionally, optimize your prompts, use streaming for faster responses, and consider agentic workflows to minimize token usage.