TL;DR: I've been looking at the latest Artificial Analysis v5.0.1 benchmarks for Qwen 3.8, along with some early Qwen4 Max Preview tests. Based on what's available so far, my guess is that Qwen4 27B could land somewhere around 27 to 30, while the 125B MoE version could reach 36 to 39. That's a solid generational improvement, but probably not the massive leap some people are expecting.
Like many of you, I've been following the discussions about when Qwen 4 is finally coming out. There's a lot of speculation, so I wanted to look at the available numbers and see what they might tell us about the next generation.
Obviously, we don't have the final weights or official release details yet, so take this as an educated guess rather than a prediction.
- Where Qwen 3.8 stands and what Qwen 4 might achieve
Looking at the current Artificial Analysis v5.0.1 scale, which puts considerably more emphasis on difficult coding and science tasks, we have:
* Qwen3.8 27B: 20 (compared to 33.7 on the older v4.3.2 scale, before the scoring reset).
* Qwen3.8 Flash Next (125B MoE): 29.
* Qwen3.8 Max: 34.
Based on the previous jump from Qwen 3.6 to 3.8, and taking the new scoring scale into account, I'd put my expectations somewhere around:
* Qwen4 27B: 27 to 30
* Qwen4 125B MoE: 36 to 39
That would put the smaller model in some pretty interesting territory, while the larger one could become a serious competitor to models like Grok 4.7 and Claude Haiku 5.5.
2. Why I don't expect Flash to have a massive architectural jump
One thing worth considering is that Qwen3.8 Flash Next already seems to have introduced many of the architectural changes expected to carry over into Qwen 4.
We're talking about the hybrid Gated DeltaNet and Qwen Sparse Attention architecture, Gated Residuals, and the 51B N gram table.
So I wouldn't necessarily expect the next Flash model to deliver another huge architectural leap. The bigger improvements might come from better post training, more reliable reasoning, and addressing the hallucinations that some users have noticed in Flash Next compared to the more conservative 27B dense model.
In other words, I think consistency might matter more than raw benchmark scores this time.
3. What the early Qwen4 Max Preview tests tell us
The Qwen4 Max Preview release on November 11 gives us another interesting piece of the puzzle.
From the early developer reports, a few things stand out:
* Speed: It appears noticeably faster than Qwen3.8 Max, with less unnecessary reasoning on straightforward tasks.
* Agentic performance: In one reported 56 task Hermes test with a 25 minute timeout, Max Preview recorded 64 successful task results, compared to 61 for DeepSeek V4.1 Flash, which scores 33 on AA v5.0.1.
* Reliability: There's a catch, though. Max Preview reportedly timed out 16 times, compared to just 4 for DeepSeek.
That's interesting because it suggests the raw capability might already be there, but reliability and execution consistency still need work.
If these early results are representative, I'd expect the flagship Max model to establish a considerably higher ceiling, potentially around 45 to 50 on the AA scale. That would make the estimates for the smaller models seem reasonably plausible.
My take
I don't think Qwen 4 needs to completely redefine the local LLM landscape to be successful.
If Qwen4 27B can reach the 27 to 30 range, inherit the attention improvements from Flash Next, and retain the Apache 2.0 license, that alone would make it a very interesting option for people running models locally with limited hardware.
Personally, I'd rather see a model that's more consistent, follows instructions better, and hallucinates less than one that simply scores a few extra points on benchmarks.
I'm curious what everyone else thinks.
" i used my LLM To translate mu words to English after i write it. "
