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AI 模型库/prithivMLmods/Qwen3.8-27B-GGUF
MODEL PROFILE · 模型档案

Qwen3.8-27B-GGUF

prithivMLmods · Qwen3.8-27B-GGUF

> **Qwen3.8-27B** is a 27-billion-parameter dense causal language model with a native vision encoder from the Qwen team, built on the Qwen3.5 architectural foundation as a compact, deployment-friendly member of the newly introduced Qwen3.8 generation — the most capable in the Qwen open-model family to date. Its 64-layer hybrid architecture interleaves Gated DeltaNet linear-attention blocks with periodic Gated Attention layers, trained with Multi-Token Prediction, and supports a native 262,144-token context window (extensible to 1M via YaRN scaling), native image and video understanding from STEM diagrams to hour-scale videos, and flexible thinking control via a `reasoning_effort` parameter (xhigh/medium/low) with thinking enabled by default and historical reasoning preserved across turns. It delivers substantial gains over its predecessor Qwen3.6-27B and often rivals or exceeds larger models like Muse Glimmer-30B and even Opus 4.6 Max on several benchmarks — scoring 73.0 on Terminal-Bench 2.1, 61.7 on SWE-bench Pro, 84.3 on OSWorld-Verified computer-use, 81.9 on AndroidWorld mobile-use, and 90.3 on LiveCodeBench v6 — reflecting particular strength in agentic coding, computer/browser/mobile-use tasks, and multimodal tool use, while remaining competitive on general reasoning benchmarks like GPQA Diamond (89.2) and IFBench (79.5). It's compatible with Hugging Face Transformers, vLLM, SGLang, and TokenSpeed out of the box, released under Apache-2.0. > [!NOTE] Multi-Token Prediction (MTP) GGUF is a specialized GGUF model file format extension that integrates speculative decoding directly into the model weights to significantly accelerate local inference. Unlike traditional speculative decoding which requires a separate, smaller "draft" model, MTP GGUF files include additional output heads within the main model architecture that predict multiple future tokens in a single forward pass. File Name | Quant Type | File Size | File Link | |-----------|------------|-----------|-----------| | Qwen3.8-27B.BF16.gguf | BF16 | 54.7 GB | Download | | Qwen3.8-27B.F16.gguf | F16 | 54.7 GB | Download | | Qwen3.8-27B.Q2_K.gguf | Q2_K | 10.9 GB | Download | | Qwen3.8-27B.Q3_K_L.gguf |

译文排队中，当前展示来源原文。

视觉问答multimodallicense:apache-2.0
上下文窗口来源未披露
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/11/2 19:17:13
OVERVIEW

模型介绍

> **Qwen3.8-27B** is a 27-billion-parameter dense causal language model with a native vision encoder from the Qwen team, built on the Qwen3.5 architectural foundation as a compact, deployment-friendly member of the newly introduced Qwen3.8 generation — the most capable in the Qwen open-model family to date. Its 64-layer hybrid architecture interleaves Gated DeltaNet linear-attention blocks with periodic Gated Attention layers, trained with Multi-Token Prediction, and supports a native 262,144-token context window (extensible to 1M via YaRN scaling), native image and video understanding from STEM diagrams to hour-scale videos, and flexible thinking control via a `reasoning_effort` parameter (xhigh/medium/low) with thinking enabled by default and historical reasoning preserved across turns. It delivers substantial gains over its predecessor Qwen3.6-27B and often rivals or exceeds larger models like Muse Glimmer-30B and even Opus 4.6 Max on several benchmarks — scoring 73.0 on Terminal-Bench 2.1, 61.7 on SWE-bench Pro, 84.3 on OSWorld-Verified computer-use, 81.9 on AndroidWorld mobile-use, and 90.3 on LiveCodeBench v6 — reflecting particular strength in agentic coding, computer/browser/mobile-use tasks, and multimodal tool use, while remaining competitive on general reasoning benchmarks like GPQA Diamond (89.2) and IFBench (79.5). It's compatible with Hugging Face Transformers, vLLM, SGLang, and TokenSpeed out of the box, released under Apache-2.0. > [!NOTE] Multi-Token Prediction (MTP) GGUF is a specialized GGUF model file format extension that integrates speculative decoding directly into the model weights to significantly accelerate local inference. Unlike traditional speculative decoding which requires a separate, smaller "draft" model, MTP GGUF files include additional output heads within the main model architecture that predict multiple future tokens in a single forward pass. File Name | Quant Type | File Size | File Link | |-----------|------------|-----------|-----------| | Qwen3.8-27B.BF16.gguf | BF16 | 54.7 GB | Download | | Qwen3.8-27B.F16.gguf | F16 | 54.7 GB | Download | | Qwen3.8-27B.Q2_K.gguf | Q2_K | 10.9 GB | Download | | Qwen3.8-27B.Q3_K_L.gguf |

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价，请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用？

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗？

不是实时结算价格，而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

huggingface · prithivMLmods/Qwen3.8-27B-GGUF ↗2026/10/29 18:39:32
huggingface · enrich:8f84f857-2470-45b3-9b22-aa1b54fc2e34 ↗2026/10/30 06:42:51

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。