I am increasingly hesitant to use non-native harnesses - model providers are now starting to train their agents for use within the harness. An eval like terminal bench can only capture so much data. I don't want to have to assess each harness every model release to make sure it's working as well as it can.
I mostly use GLM. But I will not touch ZAI's harness with a 10 mile long pole no matter how efficiently they couple it with GLM.
Is your main concern privacy? I've been using the zcode of and on for a few months now and it's definitely improved over what it was back in the spring. I also use DeepSeek's harness. Not sure which I prefer at this point. Used to be I preferred DSH, but zcode has some features I like over DSH.
Did you miss the whole fiasco from last week?
It's why I asked if your main concern was privacy (data leakage). They open-sourced their harness this week so you can always audit it.
I seemingly did - what happened?
I just looked it up: apparently they silently uploaded your entire repo with the entire git history to the cloud. A feature not controlled by any settings, operating and retrying silently.
Their excuse was that it was a file indexing optimisation and they forgot to create a toggle or setting around it. As an apology they open sourced the harness, but wiped out the history.
It sounds to me like a good distillation technique if the project has hundreds of commits written by Claude and detailed commit descriptions. Also for completeness Grok did the exact same thing two months ago.
https://www.theregister.com/security/2026/09/22/zai-says-sor...
I’m the opposite. I want one open source harness to rule them all
Cost efficiency is a plus
Right! I had the exact opposite view when reading this comment. The good timeline is where one of (or perhaps a small number of) the open source harnesses becomes so dominant that the models compete to have the model that is the best trained to work with that harness. (I'm hoping this would be Pi, because it's my favorite, but mostly I just want it to be some model-agnostic open source harness that wins.)
Realistically, it's never going to be Pi, for the same reason that video game consoles are always more performant than PCs for a given hardware spec.
There's no canonical Pi harness, it's too variable. There might be a world where something Pi based is the target, like OMP, but then you have to hesitate when you start extending the harness because you don't know what effect any given extension will have on model performance.
Yeah this does seem right. Oh well.
It won't be Pi because there's really no singular Pi that is broadly useful without explicit configuration of plugins. Pi requires plugins to do lots of things that are OOB on other harnesses that people care about. MCP, OpenTelemetry, etc. It may be some offshoot or something built on top of Pi that is more standardized, but it won't be Pi.
Yes, definitely fair. Ideally all the models would be trained against "Pi in exactly the configuration I personally prefer", but alas :)
Interestingly, MiMo was trained across multiple harnesses and it improved capabilities
https://mimo.mi.com/docs/en-US/news/latest/v2-6
MiMo _needed_ it. MiMo 2.5 Pro really struggled when given more tools, causing it to lose other capabilities. e.g. in security benchmarks I was doing, most of the medium-to-large models got better or stayed roughly the same at finding security vulnerabilities when given more tools (e.g. treesitter, semgrep, a full bash/python environment, etc.) vs. when only given the ability to read the files in the repo. But, MiMo got notably worse. It seemed to get confused by all the options. MiMo 2.5 Pro just reading files is an excellent security bug finder, at the pareto frontier at the time (cheapest option to find as many bugs as Opus 4.8, which was current at the time), but adding tools cratered it down to small model territory.
I haven't tested the new version, and I still have a bunch of tokens on my token plan, so I might give 2.6 a go in some similar tests to see if it still has the analysis paralysis problem of 2.5.
Mimo 2.5 and mimo 2.5 pro are completely different beasts.
The non-pro version is mostly useless
I only tested MiMo 2.5 Pro. I have updated my comment to make that clear in the first mention.
This is the very reason why I avoid to use native harness. They are optimized for the economic benefit of the provider, not for mine. Love Pi because it does a good job managing the context, open code meh, claude code nope.
This matters less as models get better and everyone settles on the same overall harness architectures. The model matters more than the harness anyway.
The bigger issue is that the use cases and harnesses for models is infinite, which is hard to compress into benchmark numbers that actually apply to you.
Everyone is benchmaxxing, desperate to sell, and almost nobody except the labs is doing actual science on the results, so harnesses tend to be chosen on voodoo and hunches, like which company made it. There isn't necessarily a good alternative though, bearing the cost of being a harness researcher is probably not many people's goal.
Fable 5 on Claude Code scored 61.8% at a cost of $248.05 while Fable 5 on OpenCode beat it at 66.3% at $73.42. The same model, the same benchmark; only the harness is different with a ~5 point difference in accuracy while costing significantly less. So if we are to believe the author and these results are repeatable, then it would seem that the harness matters.
The point of this framing here is specifically to address 1) benchmaxxing by using the same model, 2) NOT choose a harness on "voodoo and hunches" by using actual data to back the assertions. Your comment feels misguided and completely hand waves the actual data points here.
Agreed, especially since the more frontier models are able to accomplish in a vacuum, the more people will trust them. That being said, tool use is still really important for pulling in the right information.
And yet, open harnesses works better and use less tokens than native harnesses trained to consume as much tokens as possible.
[dead]