qwen3.8-flash

By price, qwen3.8-flash ranks 19th cheapest of 89 on input and 17th on output. Its quality score of 90 ranks 27th of 89, and on value (quality score ÷ output price) it ranks 16th of 89. Its blended rate sits 93% below the catalog average.

In real billing terms: a support chatbot handling 50,000 turns a month (2,000 input / 500 output tokens each) costs about $27 per month on qwen3.8-flash, while a RAG workload of 20,000 queries (8,000 input / 800 output tokens each) runs about $32. Because output is priced 3.1× input here, long-generation tasks scale cost fastest — cap output length before you optimise anything else.

A 1000K context window holds roughly 1,333 pages of A4 text (at ~750 tokens per page), ranking 31st of 89 here. That is enough to drop in an entire technical manual or several source files at once, which suits long-document summarization and codebase-wide analysis. Output runs at about 87 tok/s (52nd fastest of 89), acceptable interactively, though long responses will feel slow.

We track 8 Alibaba models, and qwen3.8-flash's blended rate sits 61% below that provider's average. If your task tolerates a little less headroom, Hunyuan HY3 (Tencent) blends about 57% cheaper at a quality score of 87, and is worth benchmarking first. Going the other way, qwen3.5 scores 91 on quality for about 51% less.

The closest model in Alibaba's line-up is qwen3.5-flash ($0.15 input / $0.47 output, quality 82). Against it, qwen3.8-flash scores 8 points higher on quality, runs 113 tok/s slower (about 56%). When two models sit this close on price, the deciding factor is usually the quality ceiling — run both against your real prompts before picking.