Comment by jononor
15 hours ago
Above GLM 5.3, nice! That is a very capable model. I do wish that we will see some smaller "flash" models again though - running this one locally is tough (very costly).
15 hours ago
Above GLM 5.3, nice! That is a very capable model. I do wish that we will see some smaller "flash" models again though - running this one locally is tough (very costly).
For comparison, close to Opus 5. Far behind Sol 6.1 and Opus 5.5.
Probably a pretty decent model for 90% of the usecases!
I've been doing a lot of hobby work with GLM 5.3 Flash and I can count the amount of times that I need a stronger model on two hands.
I have access to unlimited tokens at works and Opus is great but also often overkill if you know exactly what you need.
I use Opus for planning, through API on medium thinking, it doesn't cost anything significant and the rest can be happily done with deepseek or glm flash. Using sol 6.1 for review loops at the end. All running on pi.
A very complex task tends to cost like 10$ max. It's comparable to a subscription with enormous flexibility.
It's a steal.
Honestly, I don't know what people see in Sol 6.1. Opus 5.5 is so much more inteligent, as in proactive, avoiding stupid mistakes, where it contradicts itself, ignores the context. Sol 6.1 has a gold-fish memory, it's as annoying for me to work with as Opus 5 was.
I'd rank Sol 6.1 only slightly behind Opus 5.5 in intelligence and both significantly ahead of anything else I've used (though cheaper/faster models are totally fine for a lot of tasks). My only real annoyance is 6.1 Sol is one of the slowest models I've used, which does make Opus 5.5 more pleasant to use.
You can override the context length of Sol to 1M
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By next year we will likely see GLM 5.3 level performance in Qwen 4 MoE models that can run on most higher end consumer systems.
I don't think there is a reason to care about any of it, just get a few dollars on a few openrouter or similar platforms and just use the models for now, wait for cheaper models that are good enough for you to become available for your hardware.
Sure you might never see frontier performance on consumer hardware but do you really need that though? Does anyone? And if you ever do just get a 30-day sub for that work, and cancel it.
Yeah performance at a given compute budget will likely continue to improve.
We are running Qwen 3.8 27B FP8 with 4x 5060 16gb at the office for a small development team. It is doing pretty well for implementing backend and frontend tasks, and is already useful. In 6 and 12 months from how I expect it to get even better. Some planning is still done with a stronger model, but even that is maybe not strictly necessary.
Qwen 3.8 wasn't that nice in my experience Qwen series has lagged a lot but I think Qwen 4 would be better they cleaned up the architecture.
And we are likely to see many companies start building 27-32B MoE models custom built for use cases now that Rubin is going to make training costs much lower.
I expect training costs to get under 100k USD within a year for small models with another 100k maybe for RL.
I think just having all your own data from claude and stuff on your system that you can feed these models and have companies offer great base 27B models will be how it works.
Cant you run it on a pair of DGX spark? Sure it's expensive now, but at the initial retail price that was what, 8k in hardware? Not exactly unachievable
No, this is not a small model. I believe 4x Sparks is possible but 4.1 is pretty big for a flash model.
MiMo 2.6 Flash is the most capable model you can currently run on 2x Sparks without resorting to a quantised version of something.
I meant quantized. You can get an EXL3 quantized version thats almost the same performance.
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