Qwen3.8-Flash-Next

Qwen3.8-Flash-Next ranks #25 out of 226 models on the public BenchAlign leaderboard, with a score of 67.54/100. Its evidence status is Estimated, and this profile shows 24 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Qwen3.8-Flash-Next has source-displayable benchmark coverage for knowledge and understanding, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Qwen3.8-Flash-Next ranks #32 out of 144 eligible models for coding and programming, with a public category score of 61.2/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Qwen3.8-Flash-Next ranks #13 out of 138 eligible models for agentic tool use and computer tasks, with a public category score of 62.7/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Qwen3.8-Flash-Next has source-displayable benchmark coverage for multimodal and grounded tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Qwen3.8-Flash-Next ranks #5 out of 42 eligible models for instruction following, with a public category score of 94.4/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Qwen3.8-Flash-Next is an open-weight model from Alibaba. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

No. Qwen3.8-Flash-Next currently has 24 source-displayable rows across 406 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

Qwen3.8-Flash-Next has a documented context window of 262K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.