qwen3.8-flash-next:125b-a6b-nvfp4

860 6 hours ago

This experimental preview of the architecture that will underpin Qwen4.

vision tools thinking 
ollama run qwen3.8-flash-next:125b-a6b-nvfp4

Details

6 hours ago

7d1a29603bb2 · 113GB ·

json
{ "architectures": [ "Qwen4ExpForConditionalGeneration" ], "image_token_id": 248056, "language_model
json
{ "bos_token_id": 248044, "do_sample": true, "eos_token_id": [ 248046, 248044 ], "pad_token_id": 248
json
{ "size": { "longest_edge": 16777216, "shortest_edge": 65536 }, "patch_size": 16, "temporal_patch_si
json
{ "version": "1.0", "truncation": null, "padding": null, "added_tokens": [ { "id": 248044, "content"
json
{ "add_prefix_space": false, "added_tokens_decoder": { "248044": { "content": "<|endoftext|>", "lstr
json
{ "size": { "longest_edge": 25165824, "shortest_edge": 4096 }, "patch_size": 16, "temporal_patch_siz
json
{ "!": 0, "\"": 1, "#": 2, "$": 3, "%": 4, "&": 5, "'": 6, "(": 7, ")": 8, "*": 9, "+": 10, ",": 11,
template
{{ .Prompt }}
license
Qwen Community License 1.0 Copyright (c) 2026 Qwen Permission is hereby granted, free of charge, to
params
{ "min_p": 0, "num_ctx": 262144, "presence_penalty": 0, "repeat_penalty": 1, "te
1658 tensors

Readme

Qwen3.8-Flash-Next is the first open-weight model built on the architecture that will underpin Qwen4. It is a multimodal mixture-of-experts model with 125B total parameters and only 6B active per token, designed for cost-efficient inference across agentic coding, tool use, and vision tasks, with a native 256K context window.

Highlights

  • A preview of the Qwen4 architecture: an experimental release of Qwen’s next-generation architecture, published early so developers can prepare for the full Qwen4 family. Qwen3.8-Flash, the production model in Qwen’s API, is based on this release.
  • Sparse activation: 125B total parameters with 6B active per token, routed across 512 experts (10 routed plus 1 shared), so per-token compute stays close to that of a small model.
  • Hybrid attention with Qwen Sparse Attention (QSA): Gated DeltaNet linear-attention layers are interleaved 3:1 with QSA layers that attend over micro-blocks of context rather than individual tokens, cutting long-context latency for agentic workloads.
  • N-gram embedding: an additional 51B parameters live in a 20M-entry embedding table indexed by bigrams and trigrams. This is a new axis for scaling parameters that needs less compute than adding experts and is more amenable to offloading on memory-constrained hardware.
  • Gated residual: element-wise, data-dependent read gates and per-branch write gates on the residual stream add expressiveness with low inference overhead.
  • 256K context, extensible to 1M: 262,144 tokens natively, extensible to 1,000,000 tokens with YaRN.
  • Thinking mode on by default: thinking can be disabled per request, and reasoning depth can be tuned with reasoning_effort (low, medium, or xhigh). Reasoning context from historical messages is retained via preserve_thinking.
  • Native image and video understanding: from STEM diagrams, charts, and documents to hour-scale videos, along with computer-use and mobile-use tasks.
  • Strong agentic coding and tool use: outperforms Qwen3.8-27B, Qwen3.7-Plus, DeepSeek-V4-Flash, and Claude Opus 4.6 on SWE-bench Pro (62.5) and CoWorkBench (73.9), and scores 81.0 on SWE-bench Multilingual and 73.5 on Toolathlon Verified.

Benchmarks

Language

Qwen3.8-Flash-Next Qwen3.8-27B Qwen3.7-Plus DeepSeek-V4-Flash-0731 Claude-Opus-4.6 (Max)
# Params 125B 27B 397B 284B –
# Activated params 6B 27B 17B 13B –
# N-gram embedding params 51B – – – –
Agentic coding
DeepSWE 1.1 58.7 42.2 16.5 54.4 –
SWE-bench Pro 62.5 61.7 55.8 56.0 53.4
SWE-bench Multilingual 81.0 73.8 75.8 – 77.5
NL2Repo-Bench 48.1 42.3 41.1 54.2 47.6
Agentic tasks
CoWorkBench 73.9 70.7 65.1 45.1 68.2
JobBench 55.7 33.4 27.6 41.3 36.6
Agents’ Last Exam (Pass@1 / Score) 24.3 / 51.2 20.4 / 42.9 13.2 / 33.6 25.2 / – –
Toolathlon Verified (Pass@1) 73.5 67.1 50.6 70.3 –
General
IFBench 81.3 79.5 79.1 79.2 62.5
GPQA Diamond 91.7 89.2 90.3 90.8 91.3
HLE 35.9 30.8 34.7 33.8 40.0
LiveCodeBench v6 91.9 90.3 89.6 90.6 88.8

Vision Language

Qwen3.8-Flash-Next Qwen3.8-27B Qwen3.7-Plus Claude-Opus-4.6 (Max)
Agentic multimodal
ClawEval-MM (Pass@3 / Average) 64.4 / 60.4 57.4 / 56.9 57.4 / 60.1 52.5 / 54.7
RecreationBench 49.9 47.1 30.2 –
AndroidWorld 84.5 81.9 81.0 62.0
OSWorld 2.0 (Binary / Partial) 19.4 / 52.3 19.4 / 48.0 2.8 / 21.5 –
Vision2Web 64.0 62.9 42.1 –
General multimodal
ERQA 72.3 65.5 69.8 40.8
LVBench 76.6 72.4 76.2 63.0
RealWorldQA 88.5 85.9 86.9 73.9
MathVision (without CI / with CI) 90.6 / 95.7 90.0 / 94.6 90.3 / 88.7 65.5 / –
CharXiv (RQ) (without CI / with CI) 84.6 / 90.6 83.7 / 90.2 85.8 / 85.9 66.0 / –

Best results in each row are shown in bold. “–” indicates an unavailable or inapplicable score. CI refers to a code interpreter.

References