Before using M3 for the first time, measure input, cached input, output, and quota consumption with a short task. Do not plan a full month of work based on the advertised quota first.
Have the harness maintain a stable context prefix, reducing irrelevant content and duplicate logs; but confirm that the provider actually supports and recognizes caching.
Set phase boundaries for long tasks to avoid resending the entire project history every round.
Verify the /anthropic and /v1 endpoints separately. The community has reported cases where an endpoint or harness caused abnormal cache statistics.
If caching does not behave as expected, run a small reproducible comparison before switching to PAYG, OpenRouter BYOK, OpenCode Go, or another route.
Before starting the task, report the expected context strategy.
Keep stable instructions and repository facts in a clearly delimited prefix.
Do not resend unrelated history or logs.
After each tool call, report only the new state and the next action.
At the end of each phase, summarize:
- new input tokens
- repeated context that was required
- tool calls
- tests run
- unresolved issues
If the context or cache behavior cannot be verified, say so explicitly.Every single token, whether it is a fresh input, an output, or a piece of code the agent has already read 50 times in… This is a necessary excerpt; read the original source for full context.
Numeric comment:
Expected (cache works), 1.7B paid budget: Total throughput affordable: 8.95B; Repetitive re-reads (90%): 8.05B; New work… This is a necessary excerpt; read the original source for full context.
Counterpoint comment:
uh.... prompt caching exists. I've had no issues. the problem is that you're using opencode. I'm using pi.dev and have n… This is a necessary excerpt; read the original source for full context.
This is a discussion of workflow and billing behavior, not an isolated experiment on prompt quality. Do not mistake “reducing resends of context” for a fix for caching problems on the provider or harness side.
MiniMax M3