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This guide details practical strategies for minimizing LLM inference costs on OpenRouter by utilizing free tier models, appending the `:floor` parameter to select the cheapest provider, and setting hard budget caps with `max_price`. It also highlights critical billing pitfalls, such as hidden quantization endpoints that may inflate expenses if precision requirements are overlooked.
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How to Get the Lowest-Cost LLM Inference on OpenRouter

OpenRouter10 min read

Append :floor for the cheapest provider, cap spend with max_price, and start free with 20+ zero-cost models. Plus the billing gotchas to avoid.

Since you’re here, you already know the model isn’t the only cost variable. The same model can cost several times more on one provider than another, and that spread compounds fast at scale. This guide shows you the exact config to capture the lowest price automatically, plus the gotchas that raise your bill if you miss them. If you already got a billing surprise, start with the gotchas section. If you want to know whether the 5.5% platform fee is worth it, the cost math is in the decision table at the bottom. If you just want the cheapest provider for a model you’ve already chosen, append :floor to your model slug and skip to that section. Start with free models. OpenRouter has 20+ at $0 token cost. A free account gets 50 requests per day. Add $10 in credits once and that jumps to 1,000 per day. The default router already favors cheaper providers. You don’t need to configure anything to benefit from it. Append :floor to any model slug to always route to the cheapest provider for that model. Use max_price when you need a hard budget cap. If no provider qualifies, the request fails instead of spending…

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