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
| Model | Input Price | Output Price | Context | Total Cost | vs. Lowest |
|---|---|---|---|---|---|
Llama 4 MaverickMeta | $0.20/1M | $0.60/1M | 1.0M | $0.0112 | Lowest |
Copy-paste code to integrate Llama 4 Maverick into your application
import openai
client = openai.OpenAI(api_key="your-api-key")
response = client.chat.completions.create(
model="llama-4-maverick",
messages=[
{"role": "user", "content": "Your prompt here"}
],
max_tokens=2000
)
print(response.choices[0].message.content)💡 Replace "your-api-key" with your actual API key. Adjust tokens and parameters as needed.
Calculate accurate API costs for Llama 4 Maverick (Meta). Estimate token pricing, input/output costs, context caching savings, and total expenses for your AI application.
$0.20/1M tokens
$0.60/1M tokens
1,000,000
tokens supported
Llama 4 Maverick is ideal for applications requiring high throughput. With input pricing at $0.20 per 1M tokens, it's cost-effective for processing large volumes of text, images, making it suitable for content generation, data processing, and automated workflows.
With a context window of 1,000,000 tokens, Llama 4 Maverick excels at tasks requiring extensive context. Perfect for document analysis, long-form content generation, code review, and multi-turn conversations that span thousands of tokens.
Consider using context caching if available to reduce costs for repeated inputs.
Shorter, more focused prompts reduce token usage. Use system messages effectively and avoid redundant context to minimize input costs.
Track your input/output token ratios. If output costs are high, consider using Llama 4 Maverickfor initial processing and cheaper models for follow-up tasks.
Costs vary based on input/output tokens, caching usage, and additional features. Input tokens cost $0.20 per 1M tokens, while output tokens cost $0.60per 1M tokens. Use the calculator above to estimate costs for your specific use case.
Llama 4 Maverick supports up to 1,000,000 tokens in a single context window. This allows for processing of long documents, extensive conversations, and complex multi-step tasks without truncation.
Yes! Llama 4 Maverick provides a standard API that can be integrated into any application. Check the developer code snippet below the calculator for Python and Node.js integration examples.