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.
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.
Cost efficiency is a plus
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Terminal Bench 2.1 is saturated. Many token saving techniques would save money and score basically the same running Fable 5 against Terminal Bench 2.1. (They claim a better score but don’t say how much better. I’d bet my favorite hat that it’s not statistically significant.)
This is at least the fourth time I’ve seen a project hit front page with a “save money with same score on saturated benchmark” claim.
However, for this kind of customisation, Pi is actually quite great. One of the most things I love about Pi is ability to ask it to create an extension and it does it quite well as it’s part of their docs. Also ability to customise the system prompt to avoid the clutter that Claude Code add (around 20k system prompt that mostly had nothing to do with the code).
The demo was showing something I have created for my Pi setup, which is asking me in each new session which skills and MCP I would to enable for the session. This works quite well if you have multiple projects where you don’t need all skills but just a small subset
It's wild to me to claim that it's tricky to customize one of these harnesses and for that to be the entire justification for an entirely different harness.
It's really not that hard. If you want to reduce costs then all you need to do is practice delegation: instead of using the strong model, all the time to do everything, instead, you have the stronger model delegate well-defined tasks to a weaker model. Patterns like these are really easy to wire up.
Should give you some competition.
The big threat to AWS is that coding agents dont need all of their complicated infrastructure, which was built for humans. Agents can use low level primitives, i.e. just a raw server
I would seriously rather see a plain unstyled html page with a marquee tag saying "A HUMAN WROTE THE NEXT THREE PARAGRAPHS"
I switch between Pi, Autolith, and my own harness. I come to this website. I don't know what the hell a strand agent is. I don't know who is putting this out. I don't understand whether I'm meant to use a GUI or write code in order to configure it. All the screenshots show a GUI. "use one line of python or javascript to configure." I don't know if this is a harness, an orchestrator, an agent framework, or all of the above. I think it's hard. People get so caught up in making their own thing - they lose track of the state of the art. I have no idea what the value prop for Strands Harness is. But maybe I'm not the target audience.
Also, I don't really care about 30% token efficiency gains. Maybe I should. But this is not my experience. Don't get me wrong, I'm conservative with tokens. But I also have a LOT of convenience / essentials which bloat up context. And I'd rather bloat context and have it work well than not have it work well. And when GLM 5.3 Flash came out - all of a sudden I had more tokens than I knew what to do with. That's when I started using OpenClaw type agents for the first time.
I don't have the tokens/cash to throw at something like Gas Town or OpenRig. And I'm still not at all convinced that those software factories are actually the future of everything. Maybe so. But I just don't see why I would invest the time to learn a piece of software without really understanding the value prop. It's crazy to say, but it's just not enough anymore for software to be free. Because the cost of me implementing my own software is rapidly approaching free. And I like mine better.
"Oh, well I just need something that works out of the box." Wait, why? What does it need to work for? For making software? lol.
I wish this wasn't the case. I love software, but it's becoming increasingly easy to just build my own.
Setting up projects and working with the AI on local files has been great, but only for my personal account. I've been trying to get it set up for work that provides OpenAI models through a 3rd party tool, company hosted models, as local-machine models and the UX is just straight up awful.
there's no GUI for profiles or custom endpoints, the config.toml sucks and the overall experience is primitive.
At this stage I really want a Codex-like harness but I need more fluid control over the models, I want features like pinning a project to a provider, as well as pulling in all the models from that provider, also having all providers available.
So if I need to pop over to one project to consult about product A, then pop to another project to do some code analysis on product B I can do so fluidly and have my tokens billed to the right place for each concern.
or a project that can span all of the resources. like having the OpenAi models orchestrate sub-agents on the local or hosted models.
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1. Any tool result longer than 1500 tokens gets a link instead.
2. Forced compaction at 85%, removing all past messages apart from the last 4?
I'm sure these guys made it look good on the benchmarks, but the frontier labs have been hill-climbing this game for a while now, so odds are this just doesn't work for long-horizon tasks labs are optimizing for.
Ive heard from a 25 yoe consultant in a meetup group in person that aws agentcore was THE best way to handle enterprise agentic workflows with all of the proper knobs for governance etc since it comes with the iam integrations and arns etc.
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But the moment you build your own agent, you’re on your own. It’s tricky wiring up the right primitives just well enough to match that “it just worked” feeling."
Im sorry, but who is saying this? If you just throw this statement into agent of your choice- and ask what native integrations exist to cover this use: OAI and Anthropic both have a handful of options here. Claude Agent SDK, Claude managed agents, Codex exec, Codex sdk, Codex app server, openai agents sdk, openai agents api.
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The non-pro version is mostly useless
> The model matters more than the harness anyway
>
> Everyone is benchmaxxing
>
> ...harnesses tend to be chosen on voodoo and hunches...
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.
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.
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...
It’s like you didn’t even read my comment.
I do agree that in the case of benchmarking, it's a bit silly to put the onus on the benchmark runner to try and pick the right plugin for each capability that won't make someone point to /that/ as the reason Pi didn't do as well as expected.
[1] https://pi.dev/packages/pi-mcp-adapter?name=pi-mcp-adapter
In that same vein, Pi is less bloated then Deepseek and Oh My Pi, which are built on top of Pi. Isn’t it dubious to leave it out?
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I hear you tho about saturation. We're working on a follow-up deep dive post with more harnesses, so could look into Terminal Bench 4.0?
“We run Terminal-Bench 2.1 with the Terminus 2 agent harness in an e2b sandbox and report pass@1 averaged over 3 repeats per task.”
This is a different agent harness than those Strands was testing with, and there’s no guarantee the pass criteria matches up the same either.
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Is this corporate confabulation?
I haven’t tested it though
I do keep an OpenRouter account topped up for things that Ollama doesn’t have. 99% of my usage there is embeddings, the other 1% is wanting to test some new model Ollama doesn’t have.
The one that everyone else uses, the most popular. Exactly the one I listed.
I wrote "pi install npm:pi-mcp-adapter". It would install the mcp adapter.
> Now answer the same questions for all of the other plugins necessary for Pi to reach parity with Strands.
That's the whole point of Pi, it doesn't have to reach checklist level parity with Strand. Just benchmark it with its default config. Add mcp adapter if your benchmark really requires it, but that's it.
> It’s like you didn’t even read my comment.
I did, but it seems you didn't read my comment, or you didn't understand that the command I gave you is an actual recipe, and not a pseudo instruction.