Pick a model, set your agent's usage (including prompt caching), and see the per-token bill (live OpenRouter pricing) against Standard Compute's flat plan.
Monthly per-token cost (input/output + caching), cheapest first. Your selected model is highlighted.
Public OpenRouter per-token pricing (input + output, with your cache-hit rate applied where the model supports prompt caching). Standard Compute is flat-rate within fair use; suggested plan scales with workload.
Point any OpenAI-compatible agent at Standard Compute and the bill stops scaling with usage. One flat price, smart routing to a cost-efficient frontier model per request.
Every major LLM API (OpenAI, Anthropic (Claude), Google (Gemini), xAI (Grok)) bills per token. You pay for input tokens (your prompt, context, files) plus output tokens (what the model generates), each priced per million. Output usually costs 3–5× more than input. Many models also offer prompt caching, where repeated context is billed at a fraction of the input price.
That model punishes AI agents: they resend large context every step and fire background requests, so a single always-on agent can burn millions of tokens a day. This calculator estimates that monthly per-token bill across 30+ frontier models (live OpenRouter pricing, caching included) and compares it to a flat-rate plan where cost stops scaling with usage.
LLM API cost = (input tokens ÷ 1,000,000 × input price) + (output tokens ÷ 1,000,000 × output price), multiplied by your number of requests. Input (prompt) tokens and output (completion) tokens are usually priced differently, and output is typically 3–5× more expensive per token. This calculator does the math across 30+ models using live OpenRouter pricing.
Per-token API pricing varies widely by model, from cents per million tokens for small open models to $15+ per million output tokens for frontier models like GPT-6 Astra and Claude Opus. Pick any model above to see its exact per-token cost at your usage, or compare several side by side.
Agents are input-token heavy: they resend large context (files, history, tool results) on every step, and background heartbeats, retries, and parallel tool calls multiply the request count. A single always-on agent can push millions of tokens a day, so per-token bills scale fast, which is why flat-rate pricing changes the economics for agent workloads.
Input (prompt) tokens are what you send to the model: your prompt, context, files, and message history. Output (completion) tokens are what the model generates back. Output tokens usually cost several times more than input tokens, so a chatty agent with long responses can be pricier than the request count suggests.
Not truly unlimited: every LLM API meters something. Flat-rate ones exist, though. Standard Compute is an OpenAI-compatible API at a flat monthly price (from $19). It auto-routes each request to a cost-efficient model and runs at full speed until the plan's monthly budget is used, then stops, with no overage charges. That's what the comparison above shows.
Yes, a lot, for agents. Many models bill repeated input (system prompt, context, files you resend every step) at a discounted cache-read rate, often around 10% of the normal input price. Since agents resend large context constantly, a high cache-hit rate can cut the input portion of the bill dramatically. This calculator lets you set your cache-hit rate and applies the model's cache-read price where the model supports it.
It depends on context size and how often it runs, but agent inputs commonly range from ~10,000 tokens up to 1,000,000+ per request for large-context or codebase-aware agents, with outputs from a few hundred to a few thousand tokens. Set your real numbers on the sliders above to estimate the bill.