Cost Calculator
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Monthly Cost Comparison
| Provider | Model | Input $/1M | Output $/1M | Monthly Cost | Context | Speed | Tier | Try It |
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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.