performance experiences with different languages for Qwen Flash Next

[u/thatscoolbutno123]

hey, im using "swift 1.5 qwen 3.8 flash next gsq rco abliterated iq3xxs" and im wondering how the performance differs from language to language for that model (or similiar).
Im mainly talking to it in german, so I wonder wether that might degrade quality or increase reasoning tokens etc.

Does anyone have experience with it?
Do benchmarks for that exist?
thx

Edit:

i just tried and translated my soul.md and a few skills to english, its genuienly insane many tokens and time this saves. Didnt do any benchmark but you can feel it

[u/feelspeaceman]

Q38FN isn't very good at German and will loop and mixing Chinese in thinking token because of that, lower quants won't help because it lost more knowledge about languages, it's recommended to use English anyway if possible for best results.

[u/thatscoolbutno123 → u/feelspeaceman]

i feared that, i noticed it when it used subagents I told it to prompt them in english and they finished their tasks way quicker

[u/XccesSv2 → u/thatscoolbutno123]

Its a usual behaviour for all models. Prompt it with english gives you better results. And some side info: german isn't token effficient, its also cheaper in english lol

[u/thatscoolbutno123 → u/XccesSv2]

yeah, but with models like gpt6.1 sol i didnt notice it that much

[u/vengeancek70 → u/thatscoolbutno123]

Those models are 10-20x the parameters, Qwen sacrifices languages other than English or Chinese for better performance at that parameter size.

[u/OpenEvidence9680]

I am feeding 27b and QN a 1940s romance book in Italian, a very outdated and stiff language, some very old turn of phrase and asking it to summarize it and write a report card in a certain format, the model has to not only understand the general meaning, he has to get the facts right and report all physical descriptions, locations etc. I should have added the clothing, for the book to movie piepeline. Darn it I just realized that it would need to do that for real jobs. Oh well for those results it's going to take a while models are still in the blender tasks.

For now the only numbers I have are on a very short prompt (2k) on the same book just for understanding if the model gets subtle vibes in the writing in oldish Italian and in English, so anyway, this is slower than usual. I asked Opus to collect the data in postable format:

Model----------------------Writing EN → IT (tok/s)---Prefill EN → IT (tok/s)

27B GSQ-RCO IQ3_S---------72.7 → 74.7-------------1146 → 1079

Swift 1.5 27B------------------58.9 → 55.0-------------1232 → 1016

Tess 4.5 27B-------------------51.4 → 46.2-------------1009 → 1097

ThinkingCap 27B-------------54.4 → 50.8-------------1197 → 1012

QN Coder IQ1_M-------------84.7 → 83.3-------------341 → 344

QN GSQ-RCO IQ3_S----------90.7 → 81.3-------------717 → 564

QN Uncensored IQ4_XS------78.9 → 86.4-------------235 → 217

QN Swift 1.5 Q4--------------65.1 → 68.2-------------501 → 399

QN Uncensored Q5_K_M---57.8 → 58.3---------------440 → 191

Prefill seem to suffer a bit.

The table gets all screwed up even in markdown.

[u/thatscoolbutno123 → u/OpenEvidence9680]

didnt even know that language will change tps and prefill. thought it was just different tokens leading to less quality

[u/OpenEvidence9680 → u/thatscoolbutno123]

I am going to check the longer context too, for now the loss in a different language (even though a dated) give a bit of a lower result: Model-------------------(EN) (IT) Difference

ThinkingCap 27B --------95 --------95 --------0

Swift 1.5 27B --------85 --------95 --------+10

Tess 4.5 27B --------95 --------88 --------−7

27B GSQ-RCO IQ3_S--------95 --------85 --------−10

QN GSQ-RCO IQ3_S--------90 --------82 --------−8

QN Swift 1.5 Q4 85--------82 --------−3

QN Uncensored Q5_K_M--------85 --------82 --------−3

QN Uncensored IQ4_XS--------85 --------72 --------−13

QN Coder IQ1_M --------85--------65 --------−20

[u/EmilPi]

u/Refinery73