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Qwen3.8-Flash-Next

qwen.ai

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Re: Qwen3.8-Flash-Next

#81

Post by naasking »

dist-epoch wrote: post #51
pram wrote: post #25

It's in Unsloth Desktop already. Looks like it's 73GB, so 128GB Mac or Strix Halo etc will work. Exciting!

73GB for the 1 bit model...

That probably includes the 51b ngrams too. It's possible that those could be streamed from NVMe on-demand. The Engram paper that developed this technique streamed from RAM to VRAM at only ~1% performance degradation, but these strix halo boxes and the spark have much slower memory, so it's possible moving down another rung on the memory hierarchy wouldn't affect their performance too much.

This will almost certainly require changes to llama.cpp or vllm to do it right.

Re: Qwen3.8-Flash-Next

#82

Post by dannyw »

lnenad wrote: post #21

Adding to my homelab stack, hopefully it doesn't overthink like the little model. Actually, hoping it thinks a bit less. Wait actually I'm really praying it reasons a bit more directly. But wait, I'm really sure that it must be a bit better.

You might already know this, but a large part of test-time compute / 'overthinking' is just letting the model do more passes, and refine its activation residuals more.

For example, even if you make thinking tokens literally just '....' (absolutely meaningless; zero information), you still see significant performance improvements: https://arxiv.org/abs/2404.15758 and https://arxiv.org/abs/2607.22925 for some starters.

Treat thinking more like a "loading screen message" that's been RL'd to somewhat resemble its actual internal state; which happens in its activations, not tokens.

Re: Qwen3.8-Flash-Next

#83

Post by xatnys »

lnenad wrote: post #21

Adding to my homelab stack, hopefully it doesn't overthink like the little model. Actually, hoping it thinks a bit less. Wait actually I'm really praying it reasons a bit more directly. But wait, I'm really sure that it must be a bit better.

Did you observe the model overthinking on practical tasks? While 3.8 does think a lot on xhigh I've found that it really depends on the task. On one-shot prompts that are usually the first to be posted during new releases it will tend to spend a lot more time thinking than doing. In other words the more open ended a problem space becomes, the more Qwen will tend to second-guess itself.

Conversely I've found that it can be as succinct as Muse Glimmer when it has a clear path forward. This can be either through well defined requirements or through unambiguous steps to take based on its own reasoning. While I do think it's fair to call out how much smaller model overthinks especially on one-shot prompts, in practice it hasn't led to an overall increase in time to task completion at least for what I've been using it for.

Re: Qwen3.8-Flash-Next

#84

Post by kzrdude »

dalant979 wrote: post #75
kzrdude wrote: post #32

Earlier quoted context omitted.

(Edited: I thought Qwen3.8 Flash Next was smaller, but it's not, in bytes. Here's how they compare.) DSV4 Flash 304B params, 167 GB download (at full size) Qwen3.8 Flash Next 180B params, 360 GB download (at full size)

180B?

125B regular params, 51B engrams, 4B MTP. Something like that. It should have a label of effectively 125B params with A6B (6B active).

Re: Qwen3.8-Flash-Next

#85

Post by samsari »

raducu wrote: post #80
redrix wrote: post #53

Earlier quoted context omitted.

You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each: 1. It overthinks — Just like the previous iteration. High confidence. 2. It doesn’t overthink — Improvement from the last model for your use case. Regression for others. 3. It sometimes overthinks — Best case all around. A feature, not an impairment. One final thing worth mentioning: (I made myself irrationall…

On one hand I love your joke, on the other, this is HN not reddit and I usually downvote such responses, not sure what is the HN etiquette for such humor?

70% of the posts on HN are already satire and performance art

Re: Qwen3.8-Flash-Next

#86

Post by criley2 »

user43928 wrote: post #17
rohansood15 wrote: post #9

Didn't expect it to beat 3.8 27B so cleanly. Opus 4.6 Max self-hosted at 30 tok/s on a 5k Macbook in Aug 2026. The LLM timelines are crazy.

For comparison with hosted models, GPT 5.6 Luna scores 67% on DeepSWE, compared to 59% here for Qwen. Luna is $0.20 / $1.20 vs $0.16 / $0.47 with Qwen.

Those prices are just tokens? Since each model uses different amounts of tokens to do the same thing, it's a misleading price that often makes open-weights look more competitive than they are, since most open weights models use dramatically more tokens and time to complete tasks than many frontier models.

In Artifical Analysis's cost per task, Luna(max) costs $0.05 per task, and Qwen 3.8 27B costs $0.25 per task, a 5X increase. We'll see how 3.8-flash-next does.

Re: Qwen3.8-Flash-Next

#87

Post by dannyw »

throwaw12 wrote: post #56
jrflo wrote: post #39

Earlier quoted context omitted.

The luna cost cuts were real though, not a one time promotion or something, due to some optimization (probably distillation?) that openai did.

what if it was because of quantization and they haven't released the new benchmarks for it? Anything which changes the model needs new benchmarks I guess to compare with other models, otherwise you can benchmark Fable, and distill it to student model and keep claiming this is the Fable model

ARC Prize has retested Luna after the discount and validated identical performance.

(Also, quantization isn't inherently bad or damaging when done properly, e.g. QAT).

These APIs are used heavily by enterprises at scale; with lots of performance telemetry, live evals, etc. You can't really silently nerf API models at scale without people noticing.

Of course, what I said doesn't apply to non-API consumer sub models; there's many documented and officially confirmed instances of under-the-hood "juice/effort" adjustments. (Juice = a number your effort tier maps to underneath the hood; much like Inkling's effort=0.00 to 0.99).

Re: Qwen3.8-Flash-Next

#88

Post by claudeIsDown »

user43928 wrote: post #17
rohansood15 wrote: post #9

Didn't expect it to beat 3.8 27B so cleanly. Opus 4.6 Max self-hosted at 30 tok/s on a 5k Macbook in Aug 2026. The LLM timelines are crazy.

For comparison with hosted models, GPT 5.6 Luna scores 67% on DeepSWE, compared to 59% here for Qwen. Luna is $0.20 / $1.20 vs $0.16 / $0.47 with Qwen.

Sounds like discrete propaganda

Re: Qwen3.8-Flash-Next

#89

Post by mcny »

redrix wrote: post #53
lnenad wrote: post #21

Adding to my homelab stack, hopefully it doesn't overthink like the little model. Actually, hoping it thinks a bit less. Wait actually I'm really praying it reasons a bit more directly. But wait, I'm really sure that it must be a bit better.

You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each: 1. It overthinks — Just like the previous iteration. High confidence. 2. It doesn’t overthink — Improvement from the last model for your use case. Regression for others. 3. It sometimes overthinks — Best case all around. A feature, not an impairment. One final thing worth mentioning: (I made myself irrationall…

I reached point three and was nodding all along. I guess I am the NPC

Re: Qwen3.8-Flash-Next

#90

Post by lnenad »

xatnys wrote: post #83
lnenad wrote: post #21

Adding to my homelab stack, hopefully it doesn't overthink like the little model. Actually, hoping it thinks a bit less. Wait actually I'm really praying it reasons a bit more directly. But wait, I'm really sure that it must be a bit better.

Did you observe the model overthinking on practical tasks? While 3.8 does think a lot on xhigh I've found that it really depends on the task. On one-shot prompts that are usually the first to be posted during new releases it will tend to spend a lot more time thinking than doing. In other words the more open ended a problem space becomes, the more Qwen will tend to second-guess itself. Conversely I've found that it c…

Especially on practical tasks. One shot prompts work better at Q6_K_XL for me. It loads a file, then analyses then second guesses itself then again then again then it tries to come up with a solution then second guess rinse and repeat. 122b is the perfect balance but it lacks quality for harder to solve stuff. I've ran DS Flash 0731 at Q4KXL, 3.8 Q6KXL, GLM 5.2 Q4KXL and they all over-reason. At least that's how it looks like to me when comparing with frontier models, even weaker ones.
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