If I let the main agent do the same task sequentially, it was no problem at all. I don't know if it's really just communication and orchestration that makes sub agents so inefficient, or if Anthropic figured that most people using sub agents pay per token on a big corporate account, so this is an easy way to make more money from tokenmaxxers.

That said, if your project is "do this well-planned thing on a bunch of things in parallel" then you should absolutely be instructing to have subagents "step down" to less curious models. Their output may well be more cohesive as a result!

The downside is the code isn’t as good but it is produced a lot faster and more cheaply and often it’s actually fine.

CoT has made LLMs better (say 50% improvement or something) but increases cost by an order of magnitude. That graph is going in the wrong direction and has been for a while now

I write a good prompt, paste the code then copy the output code and place it into my project.

So in the end I hand assemble and I only give it what it needs to know so no extra context wasted.

The human in the loop is of course the secret sauce but this way I am highly efficient, no vibecode and I work really fast too. Everything is audited.

I would also just vibe it if there is no responsibility, but if I do it that way I don't even care what happens with the project.

I get so detached from it that I stop caring and if it has huge critical bugs..I just don't care anymore because it's not my responsibility or my code at all at that point. I'm just there to nudge things along.

Just hook it up to Jira and let the managers add the features then pass it off to QA.

Real engineering is fully automated at that point.

Yep, 100%.

Business has made it clear they don’t care, so there’s no point in burning one’s energy. Throw the whole thing on auto, check out, and go do something else during the day.

If that's what you're doing, you're fucked (in today's society, at least). What happens to your job when they figure out that's what you're doing?

I agree, that work in these places is likely short lived, if for no other reason than working in them is awful and demoralising. My point is, that these places exist, and have such a hard-on for “oh my god, AI!!!1!1!” that putting in extra effort there, is a waste of your own energy.

You can just answer the same rote questions to different companies for max salary, then when the going gets tough you can essentially 'fire' the company and say they've moved past your core consulting paradigm. Maybe suggest a new consultant and move one. Rinse; repeat.

Side note: as someone who has been interested in programming for a while, but didn't end up in a software dev track in life, it's been pretty wild watching the ride you all have been going on lately. I used to be pretty bummed I didn't get to do that kind of work for a living, but lately I've been feeling more and more like I dodged a bullet.

Not that I don't have my own AI-related junk I have to deal with where I did end up, of course. I think most have.

Depends what I want but I can give a completely new context for every generation.

I try to make everything as simple and human readable as possible because I want the audit to go fast.

I think for me I lean towards an audit optimized approach. Everything is still generated but revolves around the human-in-the-loop for review.

A major complaint with AI code was that AIs struggle with complex codebases, don't respect existing conventions, reinvent functionality multiple times over, etc. So, newer high end AIs are tuned with the "explore/exploit" dial turned towards "explore".

You could probably get it to do things "quick and dirty" with prompting, but that, of course, requires prompting for it.

I feel like most mainstream programming languages do this sort of work for their standard libraries and their official docs. Go and Python come to mind, but plenty others do this reasonably well to the point where one mostly doesn’t need to read the implementation code to effectively use the standard library itself.

Give it only what it needs and do things usually 1 file at a time.

Feels like I'm a sort of manual tape editor, if the context was a tape fed into machine, I assemble that and then watch the machine output the results I need.

All the while, it could have just done a two-line probe test and see what happens when it calls the API with "None" for that parameter. Or just assum it would act as expected and wire in debug logs in case it doesn't.

im on local only AI and subagents are only valuable when they avoid polluting the context with extraneous file reads and parallel exploration when fixes are linear.

as OP is on about, subagents burn tokens because they arnt a deterministic intelligent gatherer but like pooluring water into a maze hoping the exit will illuminate.

Unless they are orthogonal they most likely require similar context anyway so multiple sub agent is just wasteful.

I vaguely understand you argument with the context, however is that not solved by sum agents handing their results in to the planner (or a third agent) to run on them again? I'd assume that's what is happening anyway. Let me know if that's wrong

Or are you saying my sub agents burned so many tokens because they were all using Fable, whereas my main agent could do the same job with a lesser model?

Using VS code if it matters.

I run it basically 24/7 on a ~500k line repo, and only rarely run out of quota before the end of the week.

My experience with Claude Code was very good until about 2.5 months ago, and then it suddenly turned unbelievably terrible for me.

I have not and will hopefully never look back.

I still have PTSD from how ungodly terrible it was that last week of using it.

Please, for the sake of everyone suffering from actual PTSD: Don't. It's hard enough already for victims to communicate what difficulties they are facing without people watering down terminology like that.

Sorry just teasing.

How else are we supposed to learn from each other, voice our opinions, point out our mistakes to each other? For me, this is communication. And currently 8 upvotes seem to agree with me and my request. Feel free to ignore it, or consider it, for your own use of language. But, sorry, to me, you're the one acting like a jerk and trying to "police", not me.

If anything, it's a net positive people are talking about mental health and recognize different ailments such as OCD, PTSD, and others.

I disagree that it's insensitive to those or have the illness. If it hurts you when people use PTSD as a literary special effect, I invite you to articulate why exactly it makes you feel (however it makes you feel - I don't want to put words in your mouth). I get the sense that some people take offense the same way a religious fundamentalist doesn't want to hear their Prophet or Messiah used disrespectfully. The source of the anger is built on faith and dogma.

Just tried Claude Code yesterday, and nope, it's the same old bad.

Probably because the general purpose subagents inherit the parent model.

I tell Claude explicitly to use Explore subagents, which use Haiku only, now.

only if you don't specify which model should be used

https://code.claude.com/docs/en/changelog#2-1-198

> The built-in Explore agent now inherits the main session’s model (capped at opus) instead of running on haiku

https://gist.github.com/joshcartme/dd71df7b4c51c356760b28d7f...

If you do it sequentially you only read those files approximately once, and everything hits the same prefix cache

If you plan on continuing on in the parent, and aren't going to necessarily be touching the systems the other agents are exploring, it can be worth it.

It's useful in certain situations where the parent context may need the "10,000 foot" view of something without going back in there. But subsystem-specific AGENTS.md/CLAUDE.md files are still superior and accomplish the same thing. The problem with those is they can become stale.

I say this as I don't actually know how Clade Code does this, since it's not open source, but I fail to see why two agents doing the same thing launched at different times would have different tools and system prompts.

If you make a bunch of identical API calls quickly, before the initial request completes its cache write, you will have cache misses.

If you make a single API call to warm the cache, then make a bunch of subsequent identical requests, you will have cache hits.

The optimization for this sort of thing is to deduplicate in-flight requests, but i've seen no evidence that is being done; the better design pattern would be an api or tool call that explicitly launches multiple agents after a shared prompt processing stage

1. Agent("Test")

2. look at your token usage

3. Repeat a few times

I didn't check again as I type this message but am somewhat sure subagent doesn't cache system prompt as of maybe last week

You could probably engineer your own harness or workflow to warm the cache by running a single warmup agent, but this is not at all how the tools behave out of the box. It's mostly a cost optimizaiton, so Anthropic really don't have any incentive to do it proactively, and if they did, surely they would want to tell their customers how much they were saving?

> CRITICAL: Do NOT spawn sub-agents for any reason. Perform all work in the main session. If a task is too large, ask me to break it down manually.

> This is a big task, and can easily get too large. However, sub-agents make the situation worse, and eat through our token budget way too fast. Do not use them.

> Take on manageable tasks. Don't try to do everything at once. When you start on a big task, break it down into smaller tasks, and make sure you finish each task before starting on the next one.

Or actually Claude put it there for me. Maybe it's a bit much, but it seems to work.

This is why the subscription plans are forced through the harness (the "OpenClaw Wars"): it creates a false equivalence in the minds of many customers between API tokens (latency sensitive, easy to measure) and Claude Code tokens (remnant backfill to stay to the right of the roofline, marginal cost often zero).

Selling sausage as sirloin is a great business if people go for it. And there's nothing inherently wrong with spot pricing, as long as you're honest about it...

In fact, the one model with great price/performance is Deepseek v4 Flash and I suspect that they are subsidizing it deeply to get access to everyone’s prompts for training. We may find that they raise prices on the next version (v5) after they’ve mined the user data.

What's happening this year, with secrecy and all, is saddening, but expected.

I feel like maybe it could have asked for clarification or something rather than go and try to calculate all the digits of pi all of a sudden.

Yesterday, I wanted to review a complex piece after a large refactoring, and requested a review plan beforehand. The first step was 8 agents + one more to verify the findings (all Fable). Looks good, approved.

The verification step turned into an attempt to throw a party with 41 Fable verifiers.

It will find a way.

That'll be $50 — please.

Maybe when they realize there is need to change this they come up with a more configurable interface for us mere mortals who can't afford to gamble their house on a pay as you go subscription.

The best you can do in such an environment is seek to introduce new features at the top tier, and then pull old features down the stack as the cost of those features has been amortized out, or to hurt your competitors by raising the ladder.

But somehow the cost has doubled in the last few months.

  "permissions": {
    "deny": [
      "Task(Explore)"
    ]
  }

"Agent(Explore)"

> In version 2.1.63, the Task tool was renamed to Agent. Existing Task(...) references in settings and agent definitions still work as aliases.

https://code.claude.com/docs/en/sub-agents

At this point I think Dario is just in his wellness retreat adjusting a revenue/profit dial.

Increase the price by 70% and then cut it by 50%, resulting in a 15% cut that sounds like a major deal.

It's still questionable but I don't think it's in the same ballpark as what you describe.

Its not 'nefarious' in that its in their best business interests.

But it'd be difficult to take anyone serious who thinks Anthropic's motivation was to improve the UX, and the other effect were by accident. At the time they specifically started blocking based on openclaw prompt text. Its a walled-garden tactic.

A walled garden is nefarious to people who do not want to be inside one.

So what? When you care about optimising the entire experience, you offer sane defaults.

When you prevent people from changing the defaults, it's about control, not experience.

The nefarious part is because it's non optional. They could give you an option and compete by being better, instead you're given the finger as the option is taken from you. Competition is hard and banning people to create more FUD serves business need better.

You've obviously been gaslit so badly you're desperate to find a way to defend a shitty move and pretend it's the only way to increase usability. But you don't have to deny really! You're allowed to admit control is easier for a company than competition, and that they didn't have to, but did because it increases their control of the ecosystem.

If you want to defend someone, good? But at least save it for someone who actually deserves it. They don't; and you insult you and your readers intelligence by trying.

The only issue is that Anthropic optimizes the entire experience for their bottom line. User experience and price only suffer becaue of that.

It’s like that saying “What Andy giveth, Bill taketh away”, but in this case it is one company.

There is definitely a conflict of interest.

I fully agree.

> It's the same conflict of interest quite literally any business has.

I know that you know what I meant ;) In the long term it is just as you say - overcharging (eventually corrected by competition forces), but in the short term it can be additional revenue, blamed on a bug, but making some manager look good.

no amount of alignment will stop aomeone drom just shutting up.

On the other hand, the newer variants also tend to benchmark higher so it's not quite a clean argument of "hey the new version eats more tokens"

Its rather frustrating, slower tokens and more tokens.

Edit: consumer Claude subs are the 5%. I’d bet most all of CC subs lump in under enterprise.

  - API & Enterprise: 75% to 85% of total revenue.
  - Business Subscriptions: Roughly 10% to 15%.
  - Individual Subscriptions: About 5%.

I don't fully agree with the premise that they intentionally increase system prompts, but the enterprise plan usage is going to make that a huge income for Anthropic.

Anthropic is fine, as long as someone else (a clueless employer drinking Dario Koolaid) is paying for it. But the moment you have to pay for it, people just bail and go for DeepSeek, Kimmi, OpenRouter, OpenCode Go and other alternatives that give more bang for the buck than Anthropic.

I now have unlimited Anthropic and OpenAI plans at work because CEOs bought the hype.

But for personal projects $10/mo OpenCode Go serves me with DeepSeek V4 Flash, MiMo 2.5 and GLM 5.2.

Also I think it’s well known that OpenAI is the much less expensive option (in tokens and $$). For the same $20 you get a lot more mileage.

Curious if folks have strong opinions about the overall UX of OpenCode vs CC…

Especially since compute is such a scarce resource.

Also i doubt there was jira ticket with “make llm more verbose”, rather ticket with “bug makes llms too verbose” gets prioritised taking revenue impact into account.

And people paying $100 or $200 are much more likely to max it out for purely psychological reasons - it crosses that threshold where I want to see my money's worth in full. Whereas people on $20 subs are more likely to be there just to get access to better models and features, and are not necessarily even doing any substantial work.

Your gym members who got a subscription aspirationally and don't show up are absolutely subsidizing the power lifter who is introducing wear on (tens of?) thousands of dollars of equipment three times a week, but if the regulars weren't there you wouldn't have sold those subscriptions at all. Without a poster child there's no poster.

Not sure if intentionally meant as a reference, but it gives "I use Arch btw" vibes.

and the interesting thing about system prompt wastage is its a cost that scales non linearly with subagent use.

I mean, that's a very weak argument? Isn't a much more plausible explanation that with your tooling you'll have more of a lock-in than with just your model?

They get lock-in, and through that lock-in are more effectively able to inflate token usage.

OTOH, this makes typical subscriptions usages consume more tokens, which are included in their flat fee.

This sounds more like incompetence than malice.

To remind Goodhart's law: "When a measure becomes a target, it ceases to be a good measure".

..also to parent's point, yes the upsell is only appealing once user run's out of tokens.

When using Pi, one way to significantly reduce input tokens it yields is to ignore common bookkeeping "dot directories", such as `.git`. How to do so can be found with the following interactive Pi prompt:

  How do I configure Pi to ignore git related artifacts, such 
  as the project's .git directory?

After reading PUSH_AX's valid comment: ``` This is like saying contractor (A) asked for $33,000 to undertake the work and contractor (B) asked for $7,000 Are we measuring and caring about the right thing? ``` We will update the post to include:

1) A more in-depth task. 2) Qualitative results comparison. 3) As soon as possible, a reproduction of the inputs and outputs.

I wonder if a lot of the 33k is context, like from recent conversations.

Therefore, you should include the actual costs associated with the task in API token usage or subscription level. Is there a reasonable way to do apples to apples cost comparison?

https://quesma.com/blog/the-true-cost-of-saying-hi-to-an-ai-...

Tokenflation seems very real: the number of tokens consumed by simple tasks keeps increasing.

I am a bit surprised that you're disappointed that it does exactly what you told it to - people usually have the opposite complaint.

If you're using it interactively and watching what it changes, I'd trigger the tests when you think it's needed. And if you want to go more hands-off, why not add try to encode the same nuance you'd use into the rule?

But yes, anything you want to ensure really needs to be a hook.

edit: realizing with "precommit" you probably meant a git hook not one in their harness. I'd have written the same response more or less though. :)

But then you could just be storing up a lot of problems…

-- note: I've been full time in Beolis for some months already, feedback welcome ;)

I read that this is because it wastes time looking through past conversations and other context to figure one what you might want it to do - a less ambiguous prompt would be better.

I don’t like that Claude became more opaque around February, including the system prompts. 33k feels way too much.

I appreciate that Codex is open source and OpenAI has explicitly said using the subscription with other agents is ok. OpenAI has been much more consumer-friendly recently.

Doesn't the model need at least a basic system prompt to understand what tools are available?

The entire agent system prompt can be seen here:

https://github.com/earendil-works/pi/blob/main/packages%2Fco...

What do you miss? I ask because I do some heavy work with pi + GLM 5.2 (using opencode Go subscription) and my workflow is plan -> implement.

Sure, but you have to add almost everything, no? It deliberately only comes with read, write, edit, and bash. My point wasn't that you can't add stuff, but that I'd just rather use an harness that's a bit more full featured from the start.

(Pi is a bit like old 3D printing where fettling the printer to work is a central part of the hobby. I'd rather just buy a Prusa.)

Though, imo, the fact that pi maintains its "we only include the bare minimum!" statement is part of the draw for me. Especially considering that im in an enterprise env; being able to internally share custom implementations of out-of-the-box Codex/CC stuff is really nice.

I do wonder how they'd go about shipping a default web search tool. Big problem there is the lethal trifecta. Shipping something that arbitrarily allows untrusted content to be retrieved non-deterministically I'm sure is a long conversation on Pi's end. Pushing it off to the user to decide is easy.

"Pi has *ALL* the tools, can you name one it does not".

I've heard really good things but that being my first experience with pi didn't fill me with confidence about it's code quality either.

For now I stay with OpenCode I think - I was using zed editor and agent for the longest time anyway and think I will go back to that. CLI tools for me seem a bit too disconnected from the code.

Every time I read this comments I have the feeling you are talking about mcp or sub agents, otherwise this makes no sense at all.

I guess the cache would only be invalid if the day changed or the root directory, which would technically happen infrequently enough.

But I'll investigate how that works in a session. You got me curious.

If going local, llama.cpp is going to be the more beginner friendly local inference engine that supports more processor types (AMD GPUs, Intel GPUs, CPUs, anything that supports Vulkan, not just Nvidia). LM Studio is a nice wrapper for this if you'd rather avoid cloning repo and compiling yourself, provided you don't mind closed source software; it's much less enshittified than Ollama.

If going local, you will also need model weights in the right format for your inference engine, and with a model that can fit on your hardware. This is going to be .GGUF files if you're using llama.cpp or a wrapper for it like LM Studio.

From there, pick a language, go look up the OpenAI /chat/completions API format (or Anthropic's "Responses" API format), create a DS or array or slice to store messages, and build a loop that accepts user input, formats it according to the API format, sends it to the inference server, retrieves and parses the response, adds the response to the DS/array/slice, and repeat.

There's a lot more beyond this - tool calling, other API formats (optionally), MCP servers, transport layers besides terminal stdin/stdout, permission models, starting with a system message, clearing your message stack correctly (hint: don't reset it mid tool-call), message compaction, web searching and page fetching, semantic search RAG over embeddings, memory layers - way too much to cover exhaustively in a single message.

To learn yourself:

<$20 on a cloud AI api for a chunk of tokens and have the AI teach you. "help me write an AI Agent using (language) and walk me through the steps"

Realize that these agent are REPL/while loops that maintain a conversation state and then based upon the tagging syntax like <TOOL:bash:uptime>uptime for system run time</TOOL> and the agent extracts the tool and then does sub commands.

https://app.codecrafters.io/catalog

Incidentally, I also have zero supply chain attack surface as I have zero dependencies in my agent, just go stdlib. Pi, again, has 130+ transitive dependencies asking me to trust the security of my system to 150+ additional people I've never met in exchange for a bunch of bloat I do not want.

For reference, pi-coding-agent, by itself (not including dependencies, tests, or pi-ai, pi-tui, pi-agent-core, etc), is ~41,653 SLOC taking up ~1658.9 KiB across 163 files.

My agent, excluding dependencies (all go stdlib) and tests, is 3 files, 946 SLOC, taking up 36.3 KiB, and includes a basic TUI and an XMPP transport channel (including TLS for XMPP), with dynamically configurable delivery to and receipt from either or both, including allowlists for XMPP message partners. It has tool calling, a permission model with whitelisting and interactive permission querying on a per-tool basis, full thinking support, including the ability to toggle hiding or showing it across either or both transports repeatedly throughout an individual session, the same tools as pi comes with out of the box, plus web search, and a tool to vet, build, and git commit golang projects all in one go, stopping if errors are observed. Configurable model and endpoint, too.

Incidentally, the open source xmpp server (prosody) and metasearch engine (SearXNG) are both self-hosted, too.

I used mitmproxy (setup assisted by Claude, natch) to capture Claude Code's entire initial system prompt and the whole thing was (I just double-checked) 162k of JSON.

This led me to start experimenting with Pi, OpenCode, and Hermes...

   Opus 4.8 (1M context)
   claude-opus-4-8[1m]
   23k/1m tokens (2%) 

   Estimated usage by category
   System prompt: 3.9k tokens (0.4%)
   System tools: 13.9k tokens (1.4%)
   Custom agents: 235 tokens (0.0%)
   Memory files: 28 tokens (0.0%)
   Skills: 4.9k tokens (0.5%)
   Messages: 8 tokens (0.0%)
   Compact buffer: 3k tokens (0.3%)
   Free space: 974k (97.4%)

I was simply supporting the article's data - their reported 33k tokens is probably roughly 150-165k.

I enable tools specific to each project only in that project, and have very very few in my global config. Like <5k tokens worth.

Current /context on a fresh session (compare to that above) is:

  Opus 4.8
  15.8k/1m tokens (2%)
  System prompt: 4.5k tokens (0.4%)
  System tools: 7.9k tokens (0.8%)
  Memory files: 441 tokens (0.0%)
  Skills: 3.1k tokens (0.3%)
  Messages: 8 tokens (0.0%)
  Free space: 984.2k (98.4%)

If you don't use a subscription, and pay per token instead, you can easily move to another harness.

My $20 sub using gpt 5.6 sol thinking-off lasts for hours using pi.

Claude is much better in OpenCode then in Claude Code, OpenCode is just better than Claude Code. Claude Code feels like a complete mess to use comparatively.

I'm quite impressed with Grok 4.5 because its speed and single-task effectiveness feels better than anything else for a human-in-the-loop workflow. (For the stuff I do, I'm not interested in having AI race ahead of what I can oversee.)

All models do things in a way I personally disagree with at least some of the time. The "dumber" models sometimes fail to recognise how to fit a solution into existing code. The "smarter" models sometimes get too clever and over-engineer their solutions. Cleverness is occasionally interesting, but is just as likely to trigger a distracting rabbit hole where I spend time analysing whether something unexpected was a legit insight, or mere opinion.

If I self host a local model is there some way to make Android studio not time out after 10 minutes?

> When context gets too long, maki compacts history automatically: strips images, thinking blocks, and summarizes older turns.

Don’t the summaries of older turns effectively invalidate the context cache, such that you consume less tokens but more expensive tokens?

I asked both a trivial question (summarize last commit). Opus cost 50 cents, Fable about $1.

That checks out because Fable's twice as much in the API (though I think its emphasis on correctness makes the difference larger for bigger tasks).

But, at $1 per question, I think I will stick to the subscription for now! I was certainly glad GPT-5.6-Sol is included in OpenAI's subscription, and I'm curious if they'll be able to do the same for GPT-6.

All the VC money appears to have run out a few weeks ago.

I haven't tested it on anything bigger but it doesn't seem to do the kind of proactive testing, that they do in bigger harnesses.

Codex at least has a system prompt that tells it not to consider a feature a complete until it has verified it. I'm not sure about Claude Code.

I suppose I could add that one line to the prompt, and it would get me much closer to agi :) I think Fable does this proactively even without a prompt, but I haven't tested that yet.

If Fable in my own harness is significantly cheaper than Claude Code, that would be very appealing. (I could actually afford to use it for most things!) But I think most of the cost comes from the testing it does. So we'll have to see.

[0] https://minimal-agent.com/

Competition is good.

That would depend entirely on what your device is. This sounds likely not to be an issue with the harness, but the capabilities of the models you've tried.

I experience almost no tool call failure using my nothing-special harness and DSv4 Flash.

Qwen 3.6 35B A3B and Qwen 3.6 27B can both do reliable tool calls on Pi at Q4_K_M using llama.cpp

Ultimately this combo worked:

1. https://pi.dev/packages/pi-tool-guard —- corrects key name synonyms and common structure errors, so tool calls succeed automatically (e.g if the model hallucinates old_str instead of oldText). It also wraps top level oldText/newText in an edits array if the tool didn’t do it.

2. https://pi.dev/packages/@aboutlo/pi-smart-edit - white-space-tolerant edits, as Qwen would sometimes add a fifth space to a four space indent

Hashline edit tools didn’t work well for me at all, they confused the model and it still failed to edit correctly. Also line removals would invalidate the rest of the file requiring re-reads. I tried pi-hashline-edit-pro, though I see it now keeps a database of hashes to help keep them stable across edits. Regardless Qwen kept thinking that the hashline prefixes were part of the source.

Are we measuring and caring about the right thing?

Supposed to be hacker news and half the posts are like "this harness steals this" like it cant be avoided.

These API costs are mad.

It pays to be marginally ahead of people stuck on open models.

It works great for long-horizon tasks, and feels like it saves a boatload of tokens.

I'm generally against this context pruning without prompting or details. Sleev is very opaque about how it works and definitely will bust your cache.

Suspect that many have covered the "Comparing agentic coding tools" angle before, and that the differentiator is depth of analysis + conclusions.

So not only is this article AI-written, but the testing was entirely done by AI, too? I can't see any other reason to use such an old model.

> Our traffic passes through a local LLM gateway that wraps requests in its own envelope, a constant we measured at roughly 6,200 tokens with bare calibration requests

Why do you need to do calibration requests to figure out how your own gateway is affecting requests?

> Its subagent lane did not complete cleanly through our gateway

> We attempted to toggle extended thinking in both harnesses and are declining to publish numbers. Our gateway applies its own thinking policy, neither harness's toggle demonstrably survived the path, and anything we quoted would be noise.

Why is your own gateway screwing with your testing?

Cost, mainly. The runs went through a Claude Max subscription rather than metered API billing, and pinning an older stable snapshot kept run-to-run comparisons clean and cheap. The fixed harness payload (system prompt plus tool schemas), so the headline numbers shouldn't change too much.

That said, happy to re-run the matrix on Fable and publish the diff; payload figures should barely move, tool-calling behaviour might.

Gateway:

Meridian (github.com/rynfar/meridian); proxy that bridges the Claude Code SDK to a standard Anthropic endpoint so a Claude Max subscription can drive OpenCode-et-al.

It's the auth route for all agent traffic on the machine, not something built for the benchmark.

This is posed as some sort of discovery, but both Claude Code and OpenCode display token usage clearly after starting a chat or agent, and 30k and 7k is exactly what you see.

Also, I have seriously used most harnesses - One feels like it's being built in a place that truly understands AI and where agentic engineering is headed. You might not like it, but peak performance exists in CC when it comes to orchestration of bulk parallel work / subagents. The open source agents are catching up or accell in different way (Im preferable to pi.dev), but I'm not sure they're architecting orchestration the right why.

If you’re using API, on the other hand, there is absolutely no reason to use Claude Code, or Codex.

I find it hilarious people think they can build on slop or on captured output from quality engineers. Intent is the most important aspect, and no code base could ever capture that

In our case the alternative was nothing so I'm happy to have it, but currently claude is not as competitive as I'd have maybe expected given the hype

What matters even more is tooling quality. Bad/buggy tooling causes a lot more roundtrips that wipes out all gains from initial greedy approach.

A few months ago, I did a full benchmark run of 7 agents over 8 tasks (https://github.com/dirac-run/dirac has the data and traces). I cannot claim neutrality because of the obvious connection to one of those, but the data should be reproducible and useful. Importantly, Dirac wins there significantly on those tasks because they are mostly refactoring related (which is where approaches like hash-anchoring and AST parsing tend to shine)

We already know company is not making any profit. To break even they need ppl to use a lot more tokens AND pay for them premium price.

We also know LLMs dont give such a huge productivity boost do warrant spending of THAT size.

At this point you only wait for more and more shady plays.

In a pay per token situation, there is a huge conflict of interest with the harness provider and the token seller being the same party ... efficiency is less profitable.

I have accused claude code of trying to run up the meter on me and it confirmed I was absolutely right.

Except there’s a competitive incentive to either use less tokens or make the tokens go further

There is a world where, to hit the next quarters revenue projection, you add 2,000 tokens to the system prompt and "beat" expectations at the next earnings release.

Remember is it not OpenAI vs Anthropic as bad guys vs good guys. They are all bad guys trying to profit from your data while maximizing dependency. Just buy or rent GPUs.

https://github.com/asgeirtj/system_prompts_leaks/blob/main/A...

Brainstorm - Gemini/Antigravity

Plan - Gemini/Antigravity

Detailed Plan - Sonnet

Coding - Fable

Do not use any subagents, especially the default ones. They are dumb. The top level agent works well enough

[0]: https://github.com/baalimago/clai

The only interesting thing is whether OpenCode is more effective at writing code with it's reduced system prompt.

1) We have added a repository reproducing the methods.

2) We have commented on output quality in the post (in addition to tokens/cost).

3) Per a previous update, we now handle Fable 5 in the post.

Is that true? My understanding is that the subagent only returns the result of the request without the main agent consuming its entire transcript.

Wouldn't be surprised if you're paying full price for those tokens, but they cost ~$0 for the provider.

Anthropic even changed this silently: https://www.reddit.com/r/ClaudeAI/comments/1sk3m12/followup_...

Although saying that out loud makes me question it - each per-user chat and growing cache would need eventually to own its own ~contiguous memory block.

I think that Anthropic will bill you in any case :D

I switched to the PI CLI and have no problems with hanging processes anymore. OpenCode Go allows for API access so I'm keeping this sub.

Thats why i got irritated and wanted something thats scalable and lightweight.

https://github.com/corporatepiyush/yantra-coding-agent

Claude code seems to have a pretty robust harness and memory. It's a time saver to not include project context in our prompt, since Claude manages claude.md but I haven't used OpenCode to compare.

I know you can give instructions to guide an agent, but they are still plain English, the interpretation still isn't absolute and the more rules, the less likely it is to respect the whole set of rules in the preamble, but the whole process seem so... "Unprofessional"?

As I see it, a tool should be reliable and the less bloat, less possible points of failure.LLMs seem overly verbose and you can't shake the feeling it might veer away from the objective at any point. You can pay to mitigate or work around the risk, but it still doesn't inspire confidence in anything with more than 30 lines