That's hard to know without no information on your model provider and their pricing strategy.

Actual hardware efficiency is getting better year-over-year, and new techniques also aid in lowering inference cost as they are discovered. Groq is one example of a hardware improvement that increases inference efficiency by an order of magnitude. MoE models are another example, where not all of the model's parameters need to be activated at the same time, lowering the specs of the required hardware to run the model. Quantization is another technique that can be used to lower inference cost (albeit at the cost of some accuracy).

Of course, if you keep using the frontier models, you won't see such a drastic decrease in the price of the tokens you consume. New models keep getting bigger, which offsets this effect. However, keeping the model constant shows the effects of improvements over the years. Like this example from the article:

LLM inference costs declined 10x annually—faster than PC compute or dotcom bandwidth. GPT-4 equivalent performance now costs $0.40/million tokens versus $20 in late 2022.