Qwen 3.8 27B is excellent, but it defaults to overthinking things

To me, the amazing thing is that we now have local models that rival the reasoning of high end models from about a year ago.

I hope this trend continues.

I do agree that Qwen 3.8 27B is excellent but slow and very token inefficient. My benchmark places it near opus 4.6 and codex 5.3 performance. 3.6 27B couldn't even complete the benchmark. Please see below for details:

https://gist.github.com/nharziro/aed0c364ce2f295a493494c6f1b...

This is true, but I think it understates the problem. I did a task I've done with a bunch of small models lately (https://github.com/swelljoe/flar/pull/17), and it did an excellent job, the best of any self-hostable model. But, it took eleven (11!) hours on my dual GPU setup. It really chewed on it, and spent a lot of time checking and re-checking. It is by far the slowest model I've used for the task. GPT 5.5 did a similar task in about 20 minutes. Most big models took about an hour or so, and most small models needed a couple of hours (but did a worse job).

The big problem with overthinking on a dense model is obviously the speed hit you take. Going from Qwen 35BA3B to 27B for me is about 7-8x slower (should be ~9x?). This makes me a lot less patient for useless thinking tokens.

I’d want to compare this to the new Muse 30B model which is super terse and has a whole different way of thinking (no “Wait,”) and in my experiments was way more token efficient to the point that the absolute tok / s didn’t really matter.

To be fair, Opus 5 overthinks things on a regular basis. I interact with the LLM almost entirely through the prompt interface vs. some agentic harness, so I have a lot of granular exposure to its reasoning. For almost every code analysis, it flags all the important issues and at least one non-issue. It suggests some impractical and unnecessary fix for the non-issue that would categorically be a regression.

I've learned that medium effort can improve the outcome relative to higher settings. But I suspect the phenomenon is an artifact of a misguided effort to fix inherent LLM limitations. At least some of its reasoning will miss the target, and more bad reasoning is not the remedy.

It won't satisfy the people who just want to drop a model into their existing toolset and run, but I think there are a lot of ways to deal with this overthinking problem.

For instance, it's a step backward, but I put {"reasoning_effort":"none"} and led it by the nose:

   User: We're going to make <silly demo>.  Please create a plan, but do not write code yet.

   Agent: <short and reasonable plan>

   User: Now please follow that plan and write the code.  No other chat.

   Agent: <reasonable code in reasonable time>

Maybe this can be fixed with Jinja templates or something, or maybe it's a hack to your harness, but it shows you can get the model to reason reasonably.

You have 4 thinking levels.

You can disable it. It's well known issue in Qwen, previous releases I would disable it by default.

Also xhigh seem a new thing.

For agents, token efficiency is an operating cost. I’d rather have a terse model that escalates hard cases than one that overthinks every tool call.

--thinking-budget and --thinking-message is all you need in llamacpp to keep it progressing.

the message can be some combination of tool calling, summarizing, etc. It's overthinking often is a bunch of recursion, so simply stopping t and redirecting is all you need to do.

If someones building a harness for llamacpp, you can set this per message, so it's possible to dynamically control it by watching for the expansion of the thinking traces, and redirecting it.

I use the message to tell it to use subagents, add additional logging and to use opencode's dynamic context pruning.

As such, we'll just whisper here _skill issue_.

woohoo! A no-thinking pelican! I hope to see more, it's surprisingly good for just 2 minutes.

I wonder if this can be fixed with LORAs.

I hope Apple does end up moving to HBM. Unified memory has been a huge godsend, but the low memory bandwidth is just such a killer. Even/especially on M5, where the available compute is starting to starve incredibly badly on ML workloads.