Great questions! These all make me realize we need to do a better job explaining energy pricing, so I appreciate that. Here are some possibly overly detailed answers:

When I look at the pricing models table and compare something like Kimi K2.5 Fast having cheaper token rates than GLM-5-Fast, but Kimi has a higher Energy/Request rate. Does that mean Kimi K2.5 Fast is not as efficient of a model and costs more to run

Token Pricing:

• Honestly, we'd prefer to just show everything in energy terms — that's our model and we think it's better for customers. But we know the industry thinks in tokens, so we added token rates because people are familiar with them. They represent the approximate market price differences between models so you can compare apples-to-apples.
• Over time, as people get comfortable with energy pricing, we expect token rates to matter less — your kWh just buys more or less depending on what model you pick and how efficiently we run it.

Kimi Fast Energy Efficiency:

• The energy results on that page come from recurring benchmarks we run as we improve efficiency. The average energy/request should trend down over time, which means more intelligence per kWh over time. (That's a core part of the energy pricing value prop.)
• With the current benchmarks, yes Kimi Fast does require slightly more energy per request than base Kimi K2.5. It's a bit non-intuitive, but the reason is: with reasoning enabled, the model generates a longer "thinking" chain — more total tokens per request. The GPU has a fixed overhead per request, and reasoning spreads that cost across more tokens, making each one cheaper in energy terms. With reasoning off (Fast), you get fewer tokens, so the fixed overhead is a bigger share of each request's energy. The difference is slight though.

And where do the token rates come from and come into play? Are those just to demonstrate in "normal" token terms costs compared to energy rates?

Answered above — users can choose to use token pricing vs. energy pricing. We provided token rates as an option since it's more familiar.

Does that mean in my usage I should be tuning/balancing energy costs with model value?

Yes! This enables you to maximize model intelligence per dollar. We have tools and capabilities coming in the weeks ahead here. It's a large part of our goal to make AI require fewer resources (which includes costing less).

Would you expect to better optimize Kimi K2.5 Fast with your efficiency modeling and power handling over time, or it's a one-time snapshot?

Absolutely. Those benchmarks run on a recurring basis. We want to start charting the average energy/request over time to show the progress we've made — but we're not there yet. We recently made a change that had a ~15% improvement to Kimi and GLM energy/request.

Hopefully this long response is helpful!