LLM model pricing comparison
One table, every major model. Compare input and output price per million tokens and context window across OpenAI, Anthropic, Google, Mistral, Cohere, Meta, and DeepSeek so you can pick the cheapest model that still fits your content and quality bar.
How it works
Each row lists a model’s published input and output price per million tokens alongside its context window. Output is broken out separately because it is billed several times higher than input — for chat and generation workloads it is the number that actually moves your bill. Filter by provider to focus on one ecosystem, set a minimum context window to drop models that cannot hold your documents, and sort by whichever column matches your cost driver.
Tips and notes
Headline price is only half the story. A “cheap” model that needs longer or retried responses can cost more than a pricier one that answers correctly the first time, and a small context window can force expensive chunking or multiple calls. Use this table to shortlist, then plug your real prompt and expected completion into a per-call calculator to confirm. Always verify the live rate on the provider’s pricing page before you commit a budget.