neuron-explorer
Steering-first neuron explorer for Qwen3.5-4B: push every MLP neuron, keep every rollout, browse the results
Agents re-send their whole context every step, so input tokens grow with the square of the step count. Model it properly across 26 models. Free, no signup.
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Estimates what one multi-step AI agent run actually costs. Models quadratic context accumulation, tool-result size, retries and prompt caching across 17 models.
Taken from the repository README
Loading the full README. The saved excerpt is shown below.
A calculator that estimates what one multi-step AI agent run actually costs.
Every other LLM cost calculator prices a single API request: input tokens times rate, plus output tokens times rate. That is correct for a chatbot.
An agent is not a chatbot. It makes dozens of calls in a loop, and because language models hold no state between calls, every step re-sends the entire conversation so far. Step 10 pays for steps 1 through 9 all over again.
The second term is quadratic. Double the step count and the input cost roughly quadruples.Read the full README ↗The full README is unavailable right now. Read it at the original link. The saved excerpt is shown below.
A calculator that estimates what one multi-step AI agent run actually costs.
Every other LLM cost calculator prices a single API request: input tokens times rate, plus output tokens times rate. That is correct for a chatbot.
An agent is not a chatbot. It makes dozens of calls in a loop, and because language models hold no state between calls, every step re-sends the entire conversation so far. Step 10 pays for steps 1 through 9 all over again.
The second term is quadratic. Double the step count and the input cost roughly quadruples.Read the full README ↗