Cheapest LLM API for your workload

The output-token trap

Almost every "LLM pricing" table sorts by input price, because it's the smaller, friendlier number. But real applications are billed on both directions, and output tokens are routinely priced two to six times higher than input. A summariser that reads long documents and writes short answers is input-heavy; a chatbot or code generator that produces long completions is output-heavy. The same two models can swap places in the ranking purely based on which way your traffic leans. That's why a single headline price can't tell you the cheapest LLM API — only your own input-to-output ratio can.

Frontier quality is getting cheap

The gap between premium and budget models has narrowed dramatically. In 2026 you can run a frontier-class open-weight model — Llama 4, Qwen, DeepSeek V4 — for a fraction of what GPT-5.5 or Claude Opus 4.8 cost, and for many production tasks (classification, extraction, routine chat) the quality difference is invisible to end users. The right move is rarely "use the best model everywhere." It's to route by difficulty: a cheap model handles the bulk, and a frontier model is reserved for the hard minority of calls. That single pattern often cuts an AI bill in half without users noticing.

Three levers below the model price

Once you've picked a model, three mechanisms cut the bill further. Prompt caching bills repeated system prompts and context at a fraction of the normal input rate — size it on the prompt caching savings calculator. Batch APIs give roughly 50% off for jobs that can wait — see the batch API savings calculator. And right-sizing context so you stop sending tokens the model doesn't need is the cheapest optimisation of all. Put your whole stack together on the LLM token cost calculator.

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LLM token cost calculator · OpenAI vs Claude vs Gemini pricing · Prompt caching savings · Batch API savings · All AI APIs