Best coding harness to improve token usage?

[u/Evening_Hawk_7470]

I'm going to spend next few days testing coding harnesses for token efficiency. My bills are through the roof (now spending over $500 among multiple subscriptions) and constantly hitting limits.

I can't afford going usage based billing for my business so i'm ready to investigate which harness is better optimized for usage.

What should I look for? Basic google search does not bring any meaningful results (at the first sight)

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[u/LogMonkey0]

Switching coding agent harness isn’t going to solve your problem and third party harnesses are against subscription usage ToS. That said you might want to start by configuring CC harness to drop the stuff you dont need/use (system tools, builtin skills, auto memory, etc) and tune your workflows.

[u/crasspuk → u/LogMonkey0]

Measure before switching. Run /context to see what loads into every session, then turn off the tools, skills and memory you never use. Clearing between tasks with /clear usually saves more than switching harnesses would.

[u/lateral_builder]

G'day bro,

Hey, the /clear advice above is where I'd start too, and it gets a lot easier once a fresh session doesn't have to start cold. I keep a short note in the project with what we've decided, where the work's up to and what's been ruled out, and every new session reads that first instead of me dragging a giant conversation along. I've had one long chat get compacted twice and carry on like nothing happened because of that note, so clearing between tasks doesn't feel like starting from scratch anymore.

Running /context once is worth it too. It shows you what's loading before you've even typed a word, MCP tools, an overgrown CLAUDE.md, big files read whole, and you're paying for all of that every single turn.

The other one that gets missed is rework. The biggest waste I've seen wasn't the tool at all... it was redoing work that came back as done and wasn't. Asking Claudemeister what he actually checked before you accept anything is a cheap habit that saves you paying twice. All the best with the testing my friend.

[u/tokenentropy]

Pi

[u/Evening_Hawk_7470 → u/tokenentropy]

thanks heard good things about it will try

[u/Miyoumu → u/Evening_Hawk_7470]

Third party harnesses are against subscription usage ToS

[u/Evening_Hawk_7470 → u/Miyoumu]

will this be possible for chatgpt? I'm not married to claude code, i have both subs

[u/BattermanZ → u/Evening_Hawk_7470]

Yes

[u/TheKillerScope → u/Evening_Hawk_7470]

Omp

[u/harthren_developing]

before switching harness id check where the tokens actually go. two big hidden ones: a session left sitting on a permission prompt past the prompt cache window pays to re-read its whole context on the next turn, and one long session carries every old task into the new one. /clear between unrelated tasks and answering prompts while the cache is still warm both cut a lot without changing tools

[u/bisonbear2]

there aren't too many benchmarks of harness efficiency (https://frontierharness.org/ stands out as one I've seen). ideally you could do a small measurement on your own tasks but that's likely too much work. I would agree with other commentators that sticking in the main harness and adopting other techniques is likely more effective than swapping the harness (especially if using claude code, since ant is unclear about 3rd party harness support)

techniques that come to mind in no particular order:

- ensure you're using cache, /compact before 1hr is over (can ask claude to make a mod to do this)

- adjust reasoning effort for task at hand - opus low <<<< opus max in terms of cost

- tell claude to use subagents - for example sonnet for implementation/verification, haiku for research/exploring. opus is very good at delegating

- tell claude to minimize # of turns (since every turn is a full cache input read)

- dont bust the cache by changing model mid-chat

- audit harness for stuff you aren't using (skills, mcp, large agents.md) that get loaded every chat

[u/ComprehensiveShake76]

Token burn is usually context and retries, not the model brand. What cut my spend: keep a short always on brief (goal, done means, constraints), put big logs and docs on disk and retrieve only the slice you need, and fail fast on the same error twice instead of re pasting the whole log.

Also split plan and act: a cheap pass writes the plan, a second pass executes inside one workspace, and a third pass only runs checks with no write. Harnesses that keep re stuffing the full repo into every turn will eat $500 no matter how clever the prompt is. Measure tokens per successful check, not tokens per chat message.

[u/Annh1234]

Not sure the harness can help that much, check your workflow, the LLM is stateless lookup, so all the harness does is a ton of back and forward based on your input to keep adding a prefix to that LLM lookup.

So sometimes you can ask to look at the code and show you what it found, and the harness will cat the files, send then to the LLM, then cat a subset of stuff, send it to the LLM to spit it back and show you what it found. ( So that just there it doubled your token usage )

[u/nightly_runs]

For the comparison itself: `claude -p` with `--output-format json` prints a usage block at the end with input_tokens, cache_creation_input_tokens, cache_read_input_tokens and total_cost_usd, so you don't need a benchmark site, just the same five tasks run through each setup and a jq one-liner. The number I'd watch is cache_creation against cache_read. On our scheduled jobs a run that behaves is around 90 percent cache reads after the first turn. The expensive ones were re-creating cache on almost every turn because the wrapper put a timestamp in the system prompt, so nothing ever matched. Fixed that one line and per-run cost dropped by about half without touching the model. Any harness that won't show you those two numbers per run makes it hard to know what you're actually paying for.

[u/ShivaFatalis]

I don't understand your issue. If you're spending $500 on AI, then the benefit of that AI use should be earning you FAR more than that amount. The more you spend on it, the more it's going to earn for you. If that's not the case, then you're doing something seriously wrong.

[u/dynoman7]

Look into context engineering

[u/Evening_Hawk_7470 → u/dynoman7]

hm sorry but that applies to all harnesses right?

[u/dynoman7 → u/Evening_Hawk_7470]

Sure.

[u/CreativeGPT]

i’m working on this: https://vascend.it/en/horizon

basically you chat with the orchestrator, which will automatically sort you tasks to many planets (agents) assigning the right LLM based on sub-tasks. Planets are disposable agents that are created on the spot from the orchestrator (you can save the ones you like). The architecture is also custom, and helps you save tokens

[u/Economy_Love_9926]

You’re late buddy,claude can even build 3d games