Compare pricing across GPT-4o, Claude Sonnet, Gemini, Llama, Mistral, DeepSeek, and more. Choose a use-case preset or enter custom token counts to see costs side by side.
~600 words
~300 words
6,000/month
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.
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.
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.
Output tokens require autoregressive generation — the model predicts one token at a time, running a full forward pass for each. Input tokens are processed in parallel during a single pass. This computational asymmetry is why providers charge 2–5× more for output. Use our AI token counter to estimate your input/output ratio before calculating costs.
Pricing is sourced from each provider's published rate card and updated regularly. Actual costs may vary with volume discounts, prompt caching, batch processing, or committed-use agreements. The estimates give a reliable baseline for planning.
Model routing — sending simple queries to a cheap model and complex ones to a premium model — typically reduces costs by 60–80% without sacrificing quality. Our AI development team builds intelligent routing pipelines tailored to your use case. Check the RAG cost estimator if your workload includes retrieval-augmented generation.