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by jononor14 hours ago |
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upvoteby LaurensBER14 hours ago|
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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.

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upvoteby egeozcan14 hours ago|
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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.

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upvoteby cromka12 hours ago|
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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.
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upvoteby ac294 hours ago|
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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.
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upvoteby cjbgkagh12 hours ago|
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You can override the context length of Sol to 1M
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upvoteby joshstrange11 hours ago|
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Without this change I hated Sol and was burning tokens way faster due to all the forced compacting.

Since making this (obscure/hidden) change I enjoy Codex much more.

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upvoteby cjbgkagh10 hours ago|
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The work I do cannot fit in 256k so it was just relearning the same things over and over again while making zero progress. AFAIK the 1M burns tokens at twice the rate but I use Sol on medium as the work I'm doing now needs more memory than it needs intelligence and it's working great. I use Opus 5.5 for the stuff that needs stronger intelligence.
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upvoteby cromka9 hours ago|
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That's not actually the problem. It forgets the instructions from 15 minutes ago, without context compression involved in-between.
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upvoteby cjbgkagh8 hours ago|
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That has not been my experience after bumping it up to 1M. I do run a lot of agents in parallel ~10-16 and I actively babysit them so perhaps I'm just used to reminding the agents with things I've already told them. But I don't notice much of a difference between Sol 6.1 1M and Opus 5.5 1M
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upvoteby minraws13 hours ago|
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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.

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upvoteby jononor10 hours ago|
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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.

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upvoteby minraws7 hours ago|
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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.

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upvoteby Pragmata14 hours ago|
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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
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upvoteby bashtoni14 hours ago|
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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.

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upvoteby Pragmata14 hours ago|
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I meant quantized. You can get an EXL3 quantized version thats almost the same performance.
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upvoteby bashtoni12 hours ago|
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My experience running quantised models is that they look the same at first but are all noticeably worse once the context size gets beyond anything trivial.
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upvoteby Pragmata11 hours ago|
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I've tried both 2bpw and 3bpw quants for QWEN3.8 27b, and while i do notice a difference in terms of higher rates of getting stuck in loops, they are both reasonably usable.

Are they as good as opus 5.5? No.

Are they about on par with opus 4.6? Yes.

The long run tasks i run seem to be about the same level of quality, though there are obvious differences in model styles, token budgets, etc....

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