"Half the money I spend on advertising is wasted; the trouble is I don't know which half." -John Wanamaker
This applies even more strongly to model choosing. I know for a fact that majority of my work doesn't require a very strong model, but separating the trivial and non-trivial tasks is a famously hard problem (if at all decidable).
I don't see why it should be all that difficult. All you have to do is first find a library that implements a decent solution to the halting problem and you're off to the races.
You can use my script p-noteq-np.sh too if that helps.
Luna is comparable to GPT 5.4 from 4 months ago on many benchmarks. I know many who have said during that time, myself included, that if that's the model they had to use for the rest of their lives, they'd be fine.
GPT 5.4 is/was a very capable model.
Cosmically apt username given the substance of this comment.
Highlighting https://news.ycombinator.com/item?id=49113236 in response.
HN could be run as a BBS on 70's hardware. Instead of using a CPU with ~10 thousand transistors, you're likely using one with ~10 billion to do basically the same thing, and you don't think twice about it.
Exactly. I haven't reached the "let 1000 agents bloom" mode yet, so currently I'm spending real headspace managing agents doing work, and that work is all important, so why "settle" for sub-frontier models for that work? Maybe I'll get there for non-coding work.
If you have agents and users, you can run evals and see how far the models go. Luna is not greatest in tool calls, but if you define your problem well and the tools well, it is comparable to Gemini 4 Flash with much lower price tag.
Luna is good as an end user model for simple tasks like classification, but not as a coding model. Also do you mean Gemini 3.6 Flash? 4 doesn't exist, and Gemma 4 exists but doesn't have a Flash option.
Yeah, been running too many evals in the past week I start to mix the versions up. Probably should sleep...
We run an agent company and outside coding the new Gemini 3.6 Flash and GPT 5.6 Luna are very interesting. Luna can do a bit of research and create reports. Gemini is great for computer use.
For programming it's all Kimi K3 now.
don't expect it to be opus, but luna does coding just fine for its size/price
> separating the trivial and non-trivial tasks is a famously hard problem (if at all decidable).
Famously, this is also a problem for human coders in sprint planning.
I get frustrated with a poor quality model leaving my codebase littered with wrong comments, which then later trip up smarter models.
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You just need a very strong frontier model to do triage of your tasks.
/s
That's not necessarily a joke; the article proposes exactly that.
It's funny how codex itself can't do Sol orchestrator / Luna implementor out of the box.
That's surprising. Does it have no sub agent support at all or does it just use the same agent as the parent?
They do have subagents, released v2 of that feature with the launch of 5.6 model series in fact. It's just... very poorly executed, is a significant regression from subagents v1 and thousands of miles behind subagents of Claude code.
- Models that can be launched as subagents are hardcoded (can only be another Sol or Terra, but not Luna). Most of the time it'll just launch same model as parent anyway.
- They encrypt initial task delegation from root agent to subagent, for whatever reason
- You can't switch into subagent view at all, despite the fact that apart from initial root>subagent task handoff, all session is visible in transcript.