LLM Pricing Calculator
Compare API costs across GPT-4o, Claude, and Gemini. See how much you could save with TOON optimization.
Tokens in your prompt/context
Tokens in model response
API calls per day
Want to reduce your LLM costs?
TOON format can reduce input tokens by 30-60% for structured data.
LLM Pricing Calculator - Compare AI API Costs
Calculate and compare API costs across major LLM providers including OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and Google Gemini 1.5 Pro. Estimate your monthly spend based on token usage and see potential savings with TOON format optimization. All calculations happen in your browser — no data is sent to any server.
LLM (Large Language Model) pricing refers to the cost structure used by AI providers to charge for API access to models like GPT-4, Claude, and Gemini. Unlike traditional SaaS subscriptions, LLM APIs typically charge based on token usage — the number of text chunks processed in each request.
Most providers charge separately for input tokens (your prompts and context) and output tokens (the model's responses), with output tokens often costing 3-5x more than input tokens. Understanding this pricing model is crucial for budgeting and optimizing your AI application costs.
Tokens are the fundamental unit of LLM pricing. A token is roughly 4 characters or about ¾ of a word in English. For example, "ChatGPT is great" is 4 tokens.
- Input tokens — Everything you send to the model: system prompts, user messages, conversation history, and any context or data.
- Output tokens — The model's response. These typically cost 3-5x more than input tokens.
- Context window — The maximum tokens per request (e.g., 128K for GPT-4o). Larger contexts enable more complex tasks but increase costs.
Pricing is usually quoted per million tokens. For example, GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens.
There are several strategies to minimize your LLM API spending:
- Optimize prompts — Remove unnecessary instructions, use concise language, and avoid redundant context.
- Use efficient data formats — TOON format can reduce structured data size by 30-60% compared to JSON, directly cutting token counts.
- Choose the right model — Use smaller, cheaper models for simple tasks. Not every request needs GPT-4o.
- Cache responses — Store and reuse responses for identical or similar queries.
- Limit output tokens — Set max_tokens to prevent unexpectedly long responses.
- Batch requests — Some providers offer discounts for batch processing.
Try the TOON Converter to see how much you could save by optimizing your data format.
| Provider | Top Model | Input (per 1M) | Output (per 1M) |
|---|---|---|---|
| OpenAI | GPT-4o | $2.50 | $10.00 |
| Anthropic | Claude 3.5 Sonnet | $3.00 | $15.00 |
| Gemini 1.5 Pro | $1.25 | $5.00 |
*Prices as of February 2026. Check official pricing pages for current rates.
- Compare costs across major providers instantly
- Calculate per-request and monthly projections
- See TOON optimization savings potential
- Separate input/output token calculations
- 100% client-side (no data sent to servers)
- No signup or account required
- Updated pricing from official sources
- Links to official pricing pages
How accurate is this calculator?
The calculator uses official pricing from each provider's pricing page. Actual costs may vary based on volume discounts, committed use contracts, or pricing changes. Check the "last updated" date and verify with official sources for production budgeting.
What is TOON and how does it save money?
TOON (Token-Optimized Object Notation) is a data format designed specifically for LLM contexts. It removes unnecessary syntax (quotes, colons, brackets) that JSON requires, reducing token count by 30-60% for structured data. Fewer tokens means lower API costs.
Why are output tokens more expensive?
Output tokens require the model to generate new content, which is more computationally intensive than processing input. The model must run inference for each output token, while input tokens are processed in parallel. This is why output typically costs 3-5x more than input.
How do I estimate my token count?
A rough rule of thumb: 1 token ≈ 4 characters or ¾ of a word in English. For precise counts, use OpenAI's tiktoken library or each provider's tokenizer. Most API responses also include token usage in the metadata.
Which model should I use?
It depends on your use case. GPT-4o is great for complex reasoning and code generation. Claude 3.5 Sonnet excels at long-form content and analysis. Gemini 1.5 Pro offers the best value for general tasks. Consider testing multiple models and using cheaper options for simpler tasks.
Is my data private?
Yes. All calculations happen entirely in your browser using JavaScript. No data is sent to any server. This tool is completely client-side.
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