Cost Calculator
Monthly Cost Comparison
| Provider | Model | Input $/1M | Output $/1M | Monthly Cost | Context | Speed | Tier | Try It |
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Visual Comparison
How LLM API Pricing Works
Token-Based Pricing
Large Language Model (LLM) APIs charge based on tokens -- small chunks of text that models process. A token is roughly 3/4 of a word in English. Most providers charge separately for input tokens (your prompt) and output tokens (the model's response), with output tokens typically costing 2-5x more than input tokens.
Prices are quoted per 1 million tokens. For example, if a model charges $2.50 per 1M input tokens and you send 10 million input tokens in a month, your input cost alone would be $25.00.
What Affects Your Cost
Your monthly LLM API bill depends on several factors:
- Model choice: Flagship models like GPT-4o, Claude Opus 4, and Gemini 1.5 Pro deliver the highest quality but cost significantly more than budget alternatives.
- Input vs. output ratio: Applications that generate long responses (like code generation) have higher output costs, while search or classification tasks are input-heavy.
- Context window usage: Larger context windows let you include more information but increase input token costs per request.
- Request volume: High-throughput applications like chatbots or document processing can accumulate millions of tokens daily.
Choosing the Right Model
Not every task needs a flagship model. Here is a general guide:
- Flagship models (GPT-4o, Claude Opus 4, Gemini 1.5 Pro): Best for complex reasoning, nuanced writing, and multi-step tasks. Use when quality is critical.
- Mid-tier models (Claude Sonnet 4, Mistral Large, Command R+): Good balance of capability and cost. Suitable for most production applications.
- Budget models (GPT-4o-mini, Claude Haiku 3.5, Gemini 2.0 Flash): Ideal for high-volume, simpler tasks like classification, extraction, and basic chat. Often 10-50x cheaper than flagships.
Cost Optimization Tips
- Prompt caching: Many providers offer cached prompt pricing at 50-90% discount for repeated prefixes.
- Batch APIs: OpenAI and Anthropic offer batch processing at ~50% discount for non-real-time workloads.
- Model routing: Use cheap models for simple tasks and route only complex queries to expensive models.
- Prompt engineering: Shorter, more efficient prompts reduce input costs without sacrificing quality.
How to Use the LLM Pricing Calculator
This calculator is for developers and product teams who are integrating large language model APIs into their applications and need to forecast costs before committing to a provider. It covers all major providers -- OpenAI, Anthropic, Google, Mistral, Meta, and Cohere -- so you can compare apples to apples across the entire market.
Estimating Your Monthly Cost
Enter your expected monthly token volumes in the two input fields: prompt (input) tokens and completion (output) tokens. If you are unsure, use the preset buttons -- Chatbot, Code Assistant, Document Processing, and RAG Pipeline -- which populate realistic token volumes for each use case. The results table instantly recalculates monthly costs for every model in the database.
Understanding Token Pricing
LLM APIs charge per token, where a token is roughly three-quarters of a word in English. Providers quote prices per 1 million tokens and charge separately for input and output. Output tokens typically cost 2-5x more than input tokens because they require more computation (the model generates them one at a time). When budgeting, pay close attention to your input-to-output ratio -- a summarization app that produces short outputs from long documents is input-heavy, while a code generation tool produces verbose output from short prompts.
Choosing the Right Model Tier
Use the tier filter buttons to narrow results. Flagship models deliver the highest quality reasoning and nuanced output but cost 10-50x more than budget models. For high-volume tasks like classification, entity extraction, or simple Q&A, budget-tier models often perform just as well at a fraction of the cost. A common production pattern is to route easy queries to a cheap model and escalate only complex ones to a flagship.
Cost Optimization Strategies
- Shorten your prompts. Every extra word in your system prompt costs input tokens on every single request.
- Use prompt caching (available from Anthropic and OpenAI) to get 50-90% discounts on repeated prompt prefixes.
- Batch non-urgent requests using batch APIs for roughly 50% savings.
- Monitor your actual token usage weekly. Many teams discover their real costs differ significantly from initial estimates.
Frequently Asked Questions
How do I count tokens before sending a request?
OpenAI provides the tiktoken library for Python and JavaScript that counts tokens for GPT models. Anthropic's API returns token counts in the response headers. For rough estimation, divide your text's character count by 4 to approximate the token count in English.
Why are output tokens more expensive than input tokens?
Generating output tokens requires the model to run its full forward pass sequentially, one token at a time. Input tokens can be processed in parallel as a batch. This difference in computational cost is why every provider charges a premium for output.
What does "context window" mean for pricing?
The context window is the maximum number of tokens (input plus output combined) a model can process in a single request. A larger context window lets you include more information per request but increases your input token costs. You pay for every token you send, so stuffing a 128K context window with unnecessary text can be very expensive.
Are these prices up to date?
Prices are sourced from official provider documentation and updated periodically. LLM pricing changes frequently -- providers often lower prices or release new tiers. Always verify current pricing on the provider's website before making budgeting decisions.
Should I use open-source models to save money?
Open-source models like Llama can be self-hosted or accessed through inference providers (Together AI, Replicate, etc.) at lower per-token costs. However, self-hosting adds infrastructure complexity and GPU costs. Hosted open-source APIs through providers like Together AI offer a middle ground -- cheaper than proprietary APIs with no infrastructure management.