Regarding your question why the miRNA percentage of the first sample, nf774, in the group “WaGa wt cells” is totally different from the other two samples, nf961 and nf962, which show 85.7% and 83.2% miRNA in exceRpt_DiagnosticPlots_WaGa.pdf, I think the drastic difference is due to a batch effect.

More specifically, nf774 was generated in an older sequencing/library preparation batch, whereas nf961 and nf962 were generated in a later optimized batch. Between these batches, the small RNA enrichment/size-selection strategy was different. The older nf774 library likely had less efficient small RNA enrichment and/or stronger adapter/junk read contamination, while nf961 and nf962 benefited from the optimized protocol and therefore show high miRNA percentages. Thus, this difference is most likely technical rather than biological.

Please find the updated sample summary and batch information below.

1. Complete Sample Table

2. Batch Origins of the 6 wt Cell Samples

The 6 wild-type cell samples originate from 3 distinct sequencing runs:

1. 220617_NB501882_0371_AH7572BGXM_smallRNA_Ute_newDemulti — June 2022

   • nf774, WaGa wt cells
   • nf780, MKL-1 wt cells

2. 221216_NB501882_0404_AHLVNMBGXM_smallRNA_Ute_newDemulti — December 2022

   • nf796, MKL-1 wt cells
   • nf797, MKL-1 wt cells

3. 250411_VH00358_135_AAGKGLHM5 — April 2025

   • nf961, WaGa wt cells
   • nf962, WaGa wt cells

In summary, nf774 comes from a different and earlier batch than nf961/nf962, which supports the interpretation that the large difference in miRNA percentage is mainly caused by batch effects and differences in library preparation/small RNA enrichment strategy.

https://www.vava8.com/index.php?app=index&act=view&id=121741

The information you read regarding the performance and the API pricing of Qwen3.8-Max is accurate, but the reason you did not need to pay on chat.qwen.ai is because Alibaba separates its products into two different tiers: the Developer API and the Consumer Web Interface.

1. The Benchmarks You Read Are Real Qwen3.8-Max is Alibaba’s flagship 2.4-trillion-parameter multimodal model released in mid-2026 [[5]]. The scores you mentioned are correct, as it indeed rivals Anthropic’s Fable 5 by scoring 93.0 on PaperBench, 74.8 on CoWorkBench, and 81.9 on WideSearch [[18]].

2. Why the API Has a Price Tag The pricing you saw applies specifically to the Alibaba Cloud API tier where commercial users are charged for input and output tokens [[25]]. This pay-as-you-go pricing is meant for developers and enterprises who want to build their own applications or integrate the model into commercial software via the API [[22]]. Because these users are pulling compute resources programmatically at scale, they are charged based on exact token consumption.

3. Why chat.qwen.ai is Free chat.qwen.ai (also branded as Qwen Studio) is Alibaba’s official consumer-facing chat platform designed for everyday users [[24]]. Rather than charging individual users per token, Alibaba provides this web interface for free to let the general public experience their latest models and build a broad user base [[23]]. The company essentially subsidizes the compute cost for individual web chats.

This is a standard business model in the AI industry. It is very similar to how you can chat with other AI models for free on their official websites, while software companies must pay for the expensive API tokens to power third-party apps and coding agents behind the scenes.