LLM Cost Calculator

How LLM Pricing Works

Large language model providers charge per token — a token is roughly ¾ of a word. Most APIs charge separately for input tokens (your prompt) and output tokens (the model's response), with output tokens typically costing 2-5× more than input tokens.

Key Pricing Factors

• Model size: Larger models (GPT-4 Turbo, Claude Opus) cost 10-50× more than smaller models (GPT-4o Mini, Gemini Flash)
• Input vs output ratio: RAG systems are input-heavy (large context windows); content generation is output-heavy
• Batch vs real-time: Some providers offer 50% discounts for asynchronous batch processing
• Caching: Anthropic and OpenAI offer prompt caching that reduces costs for repeated system prompts by up to 90%
• Self-hosting: Open models like Llama have no per-token API cost but require GPU infrastructure

Cost Optimization Strategies

The most effective strategy is model routing — use a fast, cheap model for simple tasks and route complex queries to a premium model. Combined with prompt caching and response streaming, most teams can reduce LLM costs by 60-80% without sacrificing quality.

Need Help Choosing?

Cost is one dimension — latency, accuracy, and context window size matter too. Our AI engineering team evaluates models against your specific use case and builds cost-optimized pipelines. Get a free architecture review.