Stop Comparing Coding Models Only by Token Price

When evaluating coding models such as Kimi K2.7 Code, developers often start with price per million tokens.

But production coding agents introduce several additional cost variables:

• prompt caching

• reasoning tokens

• retries

• tool calls

• latency

• human corrections

Kimi K2.7 Code is an interesting case because Moonshot charges \(0.19 per million cache-hit input tokens compared with \)0.95 for cache misses.

For agents repeatedly using the same repository context and tool schemas, cache efficiency can dramatically change the final cost.

Moonshot also offers Batch processing at 60% of real-time rates for supported workloads.

Meanwhile, K2.7 is available through unified API providers such as CometAPI, which currently lists lower standard input and output rates than Moonshot's uncached route.

Rather than asking:

"Which model has the cheapest tokens?"

A more useful production metric may be:

Cost per completed task =
total workflow cost ÷ successful tasks

Track completion rate, retries, cache-hit ratio, tool success, latency, and human corrections alongside token usage.

For coding agents, the cheapest token and the cheapest completed task are not necessarily the same thing.