Costs of Online LLM Usage

Spec-driven development on OpenRouter cut the cost of evaluating a Markdown prompt of up to 180 lines and generating a function to between 1 and 7 cents, according to the author's experiments, far below a coding harness such as OpenCode because less context is required. GPT-6-Sol evaluated a 180-line prompt for 5 cents while Kimi-K3 evaluated a shorter prompt for 32 cents, a gap the author attributes to GPT-6-Sol generating fewer thinking tokens. GLM 5.3 Flash and MiMo-V2.6 Flash were the cheapest options tested, generating output for a tiny fraction of a cent at their per-token prices.

Over the past few months I have been trying to understand how to use LLMs cost-effectively on OpenRouter. AI bills can quickly become excessive. And with agentic coding, code quality can deteriorate as costs go sky-high. Using spec-driven development—where one writes a spec, and then has an agent implement it in chat —can lower costs . In many of my experiments, costs were between 1 and 7 cents to evaluate a Markdown prompt up to 180 lines and generate a function. This is significantly less expensive than a coding harness like OpenCode—less context is needed. Besides generating code from specifications, using cheaper models cost per million tokens is beneficial. For example, GLM 5.3 Flash and MiMo-V2.6 Flash were much cheaper for code generation. • GPT-6-Sol evaluated a 180-line prompt for 5 cents, while Kimi-K3 evaluated a shorter prompt for 32 cents—the difference is that GPT generated fewer thinking tokens. • GLM 5.3 Flash and MiMo-V2.6 Flash had such low per-token costs that they could generate output for a tiny fraction of a cent. • GPT-6-Sol, because it was more frugal with thinking tokens, was cheaper than Kimi-K3; the cheapest options were GLM 5.3 Flash and MiMo-V2.6 Flash.