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 |
GPT-5OpenAI | $1.75/1M | $14.00/1M | 256.0K | $0.1155 | +$0.1043 (931%) |
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 GPT-5 (OpenAI) and Llama 4 Maverick (Meta). Find out which AI model offers the best value for your use case.
Llama 4 Maverick is more affordable
89% cheaper input pricing
Llama 4 Maverick has larger context
3.9x more context capacity
Llama 4 Maverick offers lower input pricing at $0.20 per 1M tokens compared to $1.75 for GPT-5. 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.
GPT-5 supports up to 256,000 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 GPT-5 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. GPT-5 offers cached input at $0.44 per 1M tokens.Additionally, optimize your prompts, use streaming for faster responses, and consider agentic workflows to minimize token usage.