> to decide halfway through following my detailed instructions that it would be "simpler" to just... not do what I asked

That's likely coming from the 3:1 ratio of linear to quadratic attention usage. The latest DeepSeek also suffers from it which the original R1 never exhibited.

That sounds awfully similar to what Opus 4.6 does on my tasks sometimes.

> Blah blah blah (second guesses its own reasoning half a dozen times then goes). Actually, it would be a simpler to just ...

Specifically on Antigravity, I've noticed it doing that trying to "save time" to stay within some artificial deadline.

It might have something to do with the system messages and the reinforcement/realignment messages that are interwoven into the context (but never displayed to end-users) to keep the agents on task.

> that it would be "simpler" to just... not do what I asked

That sounds too close to what I feel on some days xD

I've seen behavior like that when the model wasn't being served with sufficiently sized context window

Turn down the temperature and you’ll see less “simpler” short cuts.

> The main quirk I've found is that it has a tendency to decide halfway through following my detailed instructions that it would be "simpler" to just... not do what I asked,

This is my experience with the Qwen3-Next and Qwen3.5 models, too.

I can prompt with strict instructions saying "** DO NOT..." and it follows them for a few iterations. Then it has a realization that it would be simpler to just do the thing I told it not to do, which leads it to the dead end I was trying to avoid.

> it's has spent a fair bit of time going through an infinite seeming loop before finally unjamming itself.

I think this is part of the model’s success. It’s cheap enough that we’re all willing to let it run for extremely long times. It takes advantage of that by being tenacious. In my experience it will just keep trying things relentlessly until eventually something works.

The downside is that it’s more likely to arrive at a solution that solves the problem I asked but does it in a terribly hacky way. It reminds me of some of the junior devs I’ve worked with who trial and error their way into tests passing.

I frequently have to reset it and start it over with extra guidance. It’s not going to be touching any of my serious projects for these reasons but it’s fun to play with on the side.

Some of the early quants had issues with tool calling and looping. So you might want to check that you're running the latest version / recommended settings.

> and it's has spent a fair bit of time going through an infinite seeming loop before finally unjamming itself

I can live with this on my own hardware. Where Opus4.6 has developed this tendency to where it will happily chew through the entire 5-hour allowance on the first instruction going in endless circles. I’ve stopped using it for anything except the extreme planning now.

I don't know much about how these models are trained, but is this behavior intentional (ie, the people pulling the levers knew that this is how it would end up), or is it emergent (ie, pulling the levers to see what happens)?

Have you tried the '--jinja' flag in llama-server?

Around 20ish tokens a second with 6-bit quant at very long context lengths on my AMD AI Max 395+

I’m trying to use local models whenever possible. Still need to lean on the frontier models sometimes.

60 to 70 on a 5080, but only tinkering for now. The smaller models seem exceptionally good for what they are, and some can even do OCR reliably.

I'm getting ~30 tok/s on the A3B model with my 3070 Ti and 32k context.

> Do you feel you could replace the frontier models with it for everyday coding? Would/will you?

Probably not yet, but it's really good at composing shell commands. For scripting or one-liner generation, the A3B is really good. The web development skills are markedly better than Qwen's prior models in this parameter range, too.

In my experience Qwen3.5 is better even at smaller distillations. From what I understand the Qwen3-next series of models was just a test/preview of the architectural changes underpinning Qwen3.5. So Qwen3.5 is a more complete and well trained version of those models.

In my experience qwen 3 coder next is better. I ran quite a few tests yesterday and it was much better at utilizing tool calls properly and understanding complex code. For its size though 3.5 35B was very impressive. coder next is an 80b model so i think its just a size thing - also for whatever reason coder next is faster on my machine. Only model that is competitive in speed is GLM 4.7 flash

We don't have a Qwen3.5-Coder to compare with, but there is a chart comparing Qwen3.5 to Qwen3 including Qwen3-Next[0].

[0] https://www.reddit.com/r/LocalLLaMA/comments/1rivckt/visuali...

It's the number of active parameters for a Mixture of Experts (misleading name IMO) model.

Qwen3.5-35B-A3B means that the model itself consists of 35 billion floating point numbers - very roughly 35GB of data - which are all loaded into memory at once.

But... on any given pass through the model weights only 3 billion of those parameters are "active" aka have matrix arithmetic applied against them.

This speeds up inference considerably because the computer has to do less operations for each token that is processed. It still needs the full amount of memory though as the 3B active it uses are likely different on every iteration.