LLM Cost Calculator: Jev vs GPT vs Claude vs Gemini

It depends on what you need back. For yes/no, label and score decisions, Jev bills $0.042 per million input tokens and nothing for output, far below any generative model. For generating text, the small models (GPT-6 Luna, Gemini 3.5 Flash-Lite) are the cheapest at list price. Compare them on your own call shape above, since output-heavy work changes the ranking.

Multiply calls per day by tokens per call, split into input and output, then by each price per million tokens and by 30 days. Input is everything you send (system prompt, context, the user message); output is what the model writes back. Most teams underestimate input, because retrieved context and chat history add up fast.

Input tokens are processed in parallel in one pass, while output tokens are generated one at a time, each needing its own pass through the model. That makes output several times more expensive to serve, typically 4 to 8 times the input price. Models that answer with a typed value instead of text, like Jev, avoid most of that cost.

In English, roughly 0.75 words per token, so 1,000 tokens is about 750 words. Code, non-English text and numbers use more tokens per word. A short classification prompt is a few hundred tokens; a RAG prompt with retrieved documents is often several thousand.

Yes, at list price. GPT-6 Luna is $0.10 input and $0.50 output per million tokens; Claude Haiku 4.5 is $1 and $5. Price per token is only half the answer: run a few of your real prompts through both and keep the cheaper one if its output holds up.

When the output is a decision: spam or not, which team, how urgent, does this match. Prompting a chat model to write a label and parsing it back costs output tokens and adds a failure mode. A model built for typed judgments returns the probability directly, which you can threshold in code.