I'd also love to know but I suspect none are quite there yet.
this post was submitted on 27 Apr 2026
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That's my problem. None are there yet, at least with my hardware.
If you've got 20 grand to spend, there's a couple of models out there, like the one mentioned above that should do fine.
What I have yet to learn is how much of the intelligence and accuracy comes from the model itself and how much comes from the agentic tool system. For example, my experience with ChatGPT probably would be much worse with the free version (no thinking or container).
I've been running Qwen 3.5 122B A10B but recently swapped to Qwen 3.6 35B A3B - both using OpenCode e as my agentic harness (though I've also used Pi). I've been happy with the output, though I have to be more precise with my prompts and do planning passes.
If your calibration is Codex and Claude, then the answer is basically 'none'. We're not there yet. Qwen 3.6 27B is meant to be amazing for coding, but I cannot vouch for, beyond what I have seen on video / read from others.
Outside of that, if you have the computer, you can run GLM5.1, which IS pretty good for this sort of thing.
Some of the issues surrounding small LLMs can be routed around using strict gates, checkpoints, edit-one-thing-a-time, sort of approaches. You could even use a cloud model as planner and local model as do-er.
I have a theory of how to address small model as coder issues...but that's probably a different discussion.
TL;DR: Qwen 3.6 27B is the new hotness...but I vote like to cast a vote for something like https://huggingface.co/allenai/SERA-8B-GA or https://huggingface.co/microsoft/FrogMini-14B-2510 as focused agents, co-ordinated by something else
Thank you for your opinion & recommendations. Something I saw today related to "sub-agents" is in Kimi 2.6's model card it says
Elevated Agent Swarm: Scaling horizontally to 300 sub-agents executing 4,000 coordinated steps, K2.6 can dynamically decompose tasks into parallel, domain-specialized subtasks, delivering end-to-end outputs from documents to websites to spreadsheets in a single autonomous run.
So maybe Kimi 2.6 is doing the "type of thing" I am looking for, but I don't have the means to run it practically. Maybe at 1 token per second which would be brutal.
I tried out Qwen 3.6 27B but not yet in an agentic setting, so I can't really judge yet. Maybe it's just me but the small model size seems limiting. I thought gpt-oss-120b was good.
I suspect you may need to create your own orchestration to achieve the effect you're after. As I said, I have some ideas...but it's an engineering proposal, not a drop in replacement.
I'm actually creating my own micro swarm (literally as I type this; waiting for Codex to finish running smoke tests); I have a feeling if you want "Claude at home", you're going to have to uplift something like Qwen 3.6 + swarm + harness.
I could pass the idea on to you and you could get Claude to chew through it and see what you two could jury rig?
Sure, if you have a micro swarm architecture laid out, I would love to hear what it is.
I don't think my micro swarm architecture applies to your use case (I'm doing some funky stuff with sentence transformers as classifiers; well...it's a bit more than that but still, not coding related work), but I do have an end-to-end scoped draft for the coder up-lift. I had the basic idea iterated via me-codex-claude-me-codex-claude back and forth. I'll DM it to you just now; feed it into whatever you use (Opus, Codex, GLM) and see if it suits your purposes, broadly. You'll likely need to refactor it slightly, as Python is my poison of choice, but the broad brush strokes should hold.
Do me a favour and let me know what the clankers say / if it suits your purposes. And if you decide to build it and become rich, give me a shout-out LOL
"Compete with"? Depends on your definition of competition. You can accomplish similar results with smaller, local models but you cannot do it as lackadaisically as with Claude by just throwing a sentence at it and letting it go to town for 15 minutes.
Doing things like this locally will take more time and effort in countless different ways. You need to structure the prompts and the environment much more carefully. You need to wait much longer for much smaller portions of work. You need to retry again when it gets it wrong, which will happen, either relying on better luck or adjusting your plan, your prompts or your context to better guide it to what you're actually looking for.
If you're used to Claude, working the same way with both and comparing them directly side by side, then no. Open models are not directly competitive like that. They can compete with it, if you're willing to be much more involved in the process.
If Claude is like a junior developer with access to an entire library of programming books, open models are like a 14-year-old in their first programming class with access to an entire library of programming books that they don't know how to utilize effectively. They require a lot more guidance.
You may wonder "what's the point if I have to do so much work anyway, maybe I should just do it myself" and indeed, this is the crux of the problem. It's even more obvious with smaller, open models than it is with the commercial AI models. This is not a new problem, it has been a problem even when training new employees. The difference is, real junior developers actually learn and grow based on my efforts to guide them and they eventually become senior developers. I'm not convinced that Claude or any open model ever actually will, despite how much effort goes into "training" them.
I run a quant of Qwen 35B A3B (Qwen3.6-35B-A3B-GGUF:UD_Q4_K_XL) at the moment, using Opencode and llama.cpp. I'm getting useful work out of it - but it's of course not Claude. My hardware is a 5060Ti with 16GB VRAM and then ~20GB or so of system mem is getting used as well.
It's important to put boundaries on less capable models though, so I have two plugins in Opencode as well that really makes a big difference to the results: @tarquinen/opencode-dcp@latest and superpowers@git+https://github.com/obra/superpowers.git.
I want to work in small steps with good control over what the models do so it's not very similar to what you describe with just having them run away for half an hour and do everything.