Qwen3.8 Flash Next

Qwen3.8-Flash-Next is a multimodal Mixture-of-Experts model from Qwen, released as an early preview of the architecture that will underpin Qwen4. It takes text and images as input and produces text. The language model carries 125 billion parameters with 6 billion active per token, plus a 51 billion parameter N-gram embedding table and a 4 billion parameter multi-token prediction module. Context length is 262,144 tokens natively and extends to 1,000,000 tokens with YaRN.

Key Features

• Hybrid attention with Qwen Sparse Attention — three of every four layers use Gated DeltaNet, which compresses history into a fixed-size recurrent state, and the fourth uses Qwen Sparse Attention. QSA scores importance at the micro-block level rather than per token, so indexing overhead shrinks along with the attention itself. Qwen reports attention-kernel speedups of up to 10.2 times on prefill and 6.6 times on decode at one million tokens.
• N-gram embedding — embeddings are looked up on short n-grams of the local context rather than on a single token. This adds 51 billion parameters at almost no extra per-token compute, and because the lookup addresses are known in advance the table can be held in host memory and prefetched while the model computes.
• Gated Residual — the residual stream widens into four parallel branches with an element-wise data-dependent read gate and a per-branch write gate, adding expressiveness across layers while keeping training stable.
• Ultra-sparse Mixture-of-Experts — 512 experts with 10 routed plus one shared expert active per token, with global load balancing.
• Built-in multi-token prediction — a multi-step-trained MTP module whose own attention layers also use QSA, which supports speculative decoding.
• Always reasoning — the model always produces a thinking trace, and reasoning depth is requested per call. It also preserves thinking blocks across conversation turns by default, which helps agent workflows keep decisions consistent.

Benchmark Results

On coding, Qwen reports 58.7 on DeepSWE 1.1 agentic coding, 62.5 on SWE-bench Pro, 81.0 on SWE-bench Multilingual and 91.9 on LiveCodeBench v6, ahead of both Qwen3.8-27B and the much larger Qwen3.7-Plus on each. On agentic work it reports 73.9 on CoWorkBench long-horizon office tasks, 55.7 on JobBench professional job tasks and 73.5 on Toolathlon Verified real-world tool use. General reasoning scores are 91.7 on GPQA Diamond, 81.3 on IFBench instruction following and 35.9 on Humanity's Last Exam.

On vision-language work Qwen reports 84.5 on AndroidWorld mobile use, 64.0 on Vision2Web visual web development, 88.5 on RealWorldQA real-world perception, 76.6 on LVBench long video understanding and 72.3 on ERQA embodied intelligence. Visual math and chart analysis score 90.6 on MathVision and 84.6 on CharXiv reasoning questions.

Qwen states that against Qwen3.7-Plus the model cuts both training and inference cost substantially, with training taking roughly one ninth as much, while holding comparable overall quality.

Use Cases

• Long-horizon coding agents working across a whole repository
• Computer use and mobile use agents driving a real desktop or phone
• Tool-using assistants that call functions and read back results
• Document, chart and diagram understanding
• Long video understanding and visual question answering
• Long-context retrieval and analysis over very large inputs

Model Card

The full model card, including the license, is on Hugging Face.