Frontier LLM APIs compared, price, context, tool use and deprecation policy Pricing Compared

- Headline $/M is the least useful number here; four things distort it - **Output tokens dominate:** a reasoning model burns tens of thousands of thinking tokens, billed at the output rate - A cheap-input, expensive-output provider is dear for reasoning, cheap for RAG answering - Model your actual input:output ratio before comparing - **Cached input is where the money is:** cache reads bill at ~10% of input at Anthropic and OpenAI - An agent loop resending its history is mostly cache reads - Caching is a prefix match; a timestamp, non-deterministic JSON, or a per-user tool list destroys it silently - Watch the cache-read token counter, not the invoice - **Long-context surcharges:** Google charges more above 200K tokens; others meter cache storage per hour - A million-token window at the short-context rate is not what you pay - **Batch, but check the column first:** where an async endpoint exists the discount is usually a flat 50% - Anthropic, OpenAI, Google, Mistral, Qwen, Bedrock and Together all publish that rate - DeepSeek offers no batch endpoint; its list price already sits below most batch rates - OpenRouter offers none either, a real cost if half your workload tolerates async - xAI, Moonshot, Z.ai, Cohere, Fireworks and Cerebras publish nothing confirmable - Groq documents a batch discount without a percentage we could stand behind - Where the halving exists and latency is negotiable, no negotiation beats it; where it is not, no committed spend conjures it