Having a coding harness is critical but the differences between them are overstated.
Personally, I’ve replaced OpenCode with a thin wrapper around Pydantic-AI as the pythonic analogue to Pi-Agent for headless use via Hermes
They’d all do the job - I just prefer to compartmentalize for access control.
Keeping the harness’ surface area tiny had the added benefit of preserving my understanding and being able to adapt it to my preferred workflow effortlessly
Say I'm using Claude Code or GPT Codex's harnesses but also sending some queries to the respective Anthropic and OpenAI models via OpenRouter.
Do harnesses and therefore sending the queries directly to the LLM providers have caching and other benefits that OpenRouter does not provide? Would I get any of those benefits if I simply proxied any requests to the major providers' harnesses through OpenRouter? Or only if the requests go straight from the harness to the provider's API?
The term "harness" here is being overloaded for the term "agent", which is worrying. Putting that aside, there are many factors that matter. The "harness" context, the execution pattern (parallel vs sequential), the ability to delegate to other models, etc.
Optimal harnesses use concurrent execution + subagents and are not stuck on one model. Cost and performance are impacted GREATLY by these tactics, regardless of the native agent context (instruction). This kind of single-harness analysis is shallow and misleading, although the finding that "Provider-specific optimization does not guarantee the best pairing" is probably correct, depending on how you measure.
Claude Code/Codex charge the user for their extremely bloated one-size-fits-all system prompts (including safety instructions and other stuff users dont want).
In my experience if you're using OpenAI/Claude models and paying API costs, almost every other harness beats Claude Code/Codex in cost.
In the case of DeepSeek-V4-Flash-0731 on deepinfra.com there was little difference when both used high reasoning. In the case of that same model on together.ai there was a substantial difference between the two (high reasoning for both again). When using together.ai with Kilo Code the LLM was having a lot of trouble making successful edits. In some cases that meant a lot tries at using the tools and in others it worked around by running scripts. Meanwhile it used the tools from my harness just fine. I've specifically tried to make my tools easy for all of the open weight LLMs to use correctly. That was inspired by getting some errors from Kilo Code at the beginning of the year telling me that the model was having trouble and I should use a smarter model.
Much of the extra weight of Claude Code and Codex harness are (ostensibly?) for security and alignment purposes. Whether they are effective is an open question, but leaving those dimensions out and calling it a tax is disingenuous, just turning insecurity into a negative externality.
"Why pay the waste disposal tax? Dumping into the ocean is free!"
Pi actively omits any sort of guardrails and sandboxing in the name of speed and simplicity, so it is not shocking that it is faster and simpler.
Doubling the cost of something in the name of vague security is standard operating procedure for big enterprises, maybe even quite cheap.
It's not hard to sandbox Pi without adding anything substantial to the actual size of the system prompt, which is what HarnessTax is mostly measuring. Wrapping it in nono.sh costs approximately zero tokens.
(I do think Pi should ship with more than zero builtin sandboxing though)
You can sandbox with tool hooks, at least in antigravity, I think a lot of other harnesses support similar features. Sandboxing via the prompt is just dangerous and unreliable.
claude code feels mildly shitty to use in the way that every other vibe-coded-project-got-out-of-hand project does, which is like, not that bad, but it's fucking ridiculous for a 2 trillion dollar company's main companion product
My own findings are in line with this research:
Having a coding harness is critical but the differences between them are overstated.
Personally, I’ve replaced OpenCode with a thin wrapper around Pydantic-AI as the pythonic analogue to Pi-Agent for headless use via Hermes
They’d all do the job - I just prefer to compartmentalize for access control.
Keeping the harness’ surface area tiny had the added benefit of preserving my understanding and being able to adapt it to my preferred workflow effortlessly
Say I'm using Claude Code or GPT Codex's harnesses but also sending some queries to the respective Anthropic and OpenAI models via OpenRouter.
Do harnesses and therefore sending the queries directly to the LLM providers have caching and other benefits that OpenRouter does not provide? Would I get any of those benefits if I simply proxied any requests to the major providers' harnesses through OpenRouter? Or only if the requests go straight from the harness to the provider's API?
The term "harness" here is being overloaded for the term "agent", which is worrying. Putting that aside, there are many factors that matter. The "harness" context, the execution pattern (parallel vs sequential), the ability to delegate to other models, etc.
Optimal harnesses use concurrent execution + subagents and are not stuck on one model. Cost and performance are impacted GREATLY by these tactics, regardless of the native agent context (instruction). This kind of single-harness analysis is shallow and misleading, although the finding that "Provider-specific optimization does not guarantee the best pairing" is probably correct, depending on how you measure.
It is a starting point.
Claude Code/Codex charge the user for their extremely bloated one-size-fits-all system prompts (including safety instructions and other stuff users dont want).
In my experience if you're using OpenAI/Claude models and paying API costs, almost every other harness beats Claude Code/Codex in cost.
If this is mostly because of the size of the system prompt, then perhaps in long horizon tasks the "tax" will be less obvious.
Does this extend to open models like GLM 5.3? This would mean that simply changing the harness to Pi reduces cost in half?
The provider's middleware also plays a role. I just completed some benchmarks on my bespoke harness and Kilo Code. There's a chart on my LI post here: https://www.linkedin.com/posts/joshheitzman_i-ran-a-fuller-r...
In the case of DeepSeek-V4-Flash-0731 on deepinfra.com there was little difference when both used high reasoning. In the case of that same model on together.ai there was a substantial difference between the two (high reasoning for both again). When using together.ai with Kilo Code the LLM was having a lot of trouble making successful edits. In some cases that meant a lot tries at using the tools and in others it worked around by running scripts. Meanwhile it used the tools from my harness just fine. I've specifically tried to make my tools easy for all of the open weight LLMs to use correctly. That was inspired by getting some errors from Kilo Code at the beginning of the year telling me that the model was having trouble and I should use a smarter model.
I've found the experience of using Pi with local models feels a lot snappier than both OpenCode or Claude Code.
As the model gets smarter, you need to tell it less
As the black box gets bigger, you need to stop expressing yourself even more.
Much of the extra weight of Claude Code and Codex harness are (ostensibly?) for security and alignment purposes. Whether they are effective is an open question, but leaving those dimensions out and calling it a tax is disingenuous, just turning insecurity into a negative externality.
"Why pay the waste disposal tax? Dumping into the ocean is free!"
Pi actively omits any sort of guardrails and sandboxing in the name of speed and simplicity, so it is not shocking that it is faster and simpler.
Doubling the cost of something in the name of vague security is standard operating procedure for big enterprises, maybe even quite cheap.
It's not hard to sandbox Pi without adding anything substantial to the actual size of the system prompt, which is what HarnessTax is mostly measuring. Wrapping it in nono.sh costs approximately zero tokens.
(I do think Pi should ship with more than zero builtin sandboxing though)
You can sandbox with tool hooks, at least in antigravity, I think a lot of other harnesses support similar features. Sandboxing via the prompt is just dangerous and unreliable.
claude code feels mildly shitty to use in the way that every other vibe-coded-project-got-out-of-hand project does, which is like, not that bad, but it's fucking ridiculous for a 2 trillion dollar company's main companion product