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 |
|---|---|---|---|---|---|
Llama 4 MaverickMeta | $0.20/1M | $0.60/1M | 1.0M | $0.0112 | Lowest |
Claude 4.5 SonnetAnthropic | $3.00/1M | $15.00/1M | 200.0K | $0.1800 | +$0.1688 (1507%) |
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.
Compare pricing, features, and total cost of ownership between Claude 4.5 Sonnet (Anthropic) and Llama 4 Maverick (Meta). Find out which AI model offers the best value for your use case.
Llama 4 Maverick is more affordable
93% cheaper input pricing
Llama 4 Maverick has larger context
5.0x more context capacity
Llama 4 Maverick offers lower input pricing at $0.20 per 1M tokens compared to $3.00 for Claude 4.5 Sonnet. 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.
Claude 4.5 Sonnet supports up to 200,000 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 Llama 4 Maverickfor high-volume, cost-sensitive operations and Claude 4.5 Sonnet 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. Claude 4.5 Sonnet offers cached input at $0.30 per 1M tokens.Additionally, optimize your prompts, use streaming for faster responses, and consider agentic workflows to minimize token usage.