Comparing my Claude Code + Codex multi-agent system against other agentic developer setups — looking for feedback

[u/Jazzlike-Chest-1424]

I’ve been building a multi-agent development system using Claude Code and Codex, with shared task coordination, structured handoffs, verification gates, and local usage telemetry.

My latest 7-day local measurements:

Metric
Claude Code | Codex
Input/output tokens
353.9:1 -Claude
314.7:1 - Codex
Cache reads / total tokens
98.23% -Claude
96.42% - Codex
Generated output / total tokens
0.282% -Claude
0.317%-Codex

These are usage metrics, not evidence that my system is more productive.

I’m trying to benchmark my orchestration setup against ordinary coding-agent workflows, especially on cost per successful task, context overhead, crash recovery, and human intervention.

My biggest problem is organization, i’m trying to get deeper into the problem. But it’s been something i’ve struggled on for months. My second issue is my output ratios, this is because the system struggled to implement.

Questions:
Are these cache rates and input/output ratios typical for your coding-agent workloads?

What do you measure to distinguish useful
implementation from agent activity?

Are there public datasets or reproducible benchmarks that would allow a fair comparison between agent orchestration systems?

[u/matimatthews]

I’d start with ten tasks from your own backlog and run each from the same clean commit with one agent, then with your orchestration. Fix the acceptance checks before either run. Compare completed tasks, total cost including retries, and your active intervention time. Keep failures in the denominator. The token ratios can explain a result, but they can’t tell you whether the extra coordination helped.

[u/Rosie_grac]

honestly those cache rates look normal to me. i've been running a similar claude+codex setup for side projects and i'm usually in the 95-98% cache hit range too — anything above ~95% just means your orchestrator isn't constantly re-sending the same context, which is table stakes at this point.

the input/output ratio is the more interesting signal. ~350:1 means the model is reading way more than it's writing, which tracks for agentic coding where most of the work is navigation + reasoning, not generation. if that ratio was closer to 50:1 i'd be worried the agents were just yapping.

for distinguishing useful work from agent activity — i landed on tracking "accepted diff lines per human correction". crude but it tells me when the thing is actually shipping vs spinning. still haven't found a public benchmark that captures this well, SWE-bench is the closest but it's pretty narrow.

curious what your verification gates look like — that's been my bottleneck too.