When comparing LLM APIs, it’s easy to start with pricing. A lower cost per million tokens looks attractive, especially when usage begins to scale.
But once an application moves into production, I don’t think token price alone is enough to judge whether an API is actually cheaper or better.
Latency and reliability can change the real cost significantly. If an API has a low advertised price but frequently times out, gets rate-limited, or requires retries, the savings can disappear quickly. For interactive applications, consistency also matters — a stable response time can sometimes be more useful than a very fast average with occasional large spikes.
Model capability is another part of the equation. Different models behave differently depending on the workload. Coding, long-context analysis, reasoning, tool calling, structured outputs, and multimodal tasks can all lead to different model choices.
That’s why I find cost per successful task more interesting than token price alone. If one model costs slightly more but consistently completes the task correctly with fewer retries, it may actually be the better production choice.
The same applies when using a multi-model API or routing layer. A common interface can simplify access to models such as Claude, GPT, Gemini, or Kimi, but it doesn’t make those models interchangeable. Their capabilities, limits, and provider-specific features still need to be considered.
So when evaluating an LLM API for production, I’d personally look at the combination of cost, latency, reliability, and task performance rather than optimizing for one metric.
I’m curious how others here approach this.
What metrics matter most to you when choosing an LLM API for production?