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 |
|---|---|---|---|---|---|
o3 (Reasoning)OpenAI | $10.00/1M | $40.00/1M | 200.0K | $0.5800 | Lowest |
Copy-paste code to integrate o3 (Reasoning) into your application
import openai
client = openai.OpenAI(api_key="your-api-key")
response = client.chat.completions.create(
model="o3",
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 o3 (Reasoning) (OpenAI). Estimate token pricing, input/output costs, context caching savings, and total expenses for your AI application.
$10.00/1M tokens
$40.00/1M tokens
$2.50/1M tokens
200,000
tokens supported
o3 (Reasoning) is ideal for applications requiring high throughput. With input pricing at $10.00 per 1M tokens, it's cost-effective for processing large volumes of text, making it suitable for content generation, data processing, and automated workflows.
With a context window of 200,000 tokens, o3 (Reasoning) 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.
Leverage context caching at $2.50 per 1M tokens to reduce costs for repeated inputs. Ideal for applications with recurring prompts, template-based generation, and batch processing where input context can be reused.
o3 (Reasoning) includes advanced reasoning capabilities, making it perfect for mathematical problem-solving, logical analysis, code debugging, and tasks requiring step-by-step thinking. Note that reasoning tokens may incur additional costs.
Enable context caching to reduce input costs from $10.00 to $2.50 per 1M tokens for repeated contexts.
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 o3 (Reasoning)for initial processing and cheaper models for follow-up tasks.
Costs vary based on input/output tokens, caching usage, and additional features. Input tokens cost $10.00 per 1M tokens, while output tokens cost $40.00per 1M tokens. Use the calculator above to estimate costs for your specific use case.
o3 (Reasoning) supports up to 200,000 tokens in a single context window. This allows for processing of long documents, extensive conversations, and complex multi-step tasks without truncation.
Context caching allows you to reuse input context across multiple requests, reducing costs from $10.00 to $2.50 per 1M tokens. This is ideal for applications with repeated system prompts, templates, or shared context.
Yes! o3 (Reasoning) 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.