Qwen3.8-27B

Qwen3.6-27B supports multimodal capabilities, including text reasoning, image understanding, and video processing.

Qwen3.8-27B delivers impressive performance in specific scenarios such as front-end display, and may be one of the best-performing locally deployed large models currently available, showing a significant capability improvement over the Meta 30B model and Qwen3.6-27B.

Qwen3.8-27B is licensed under Apache 2.0, fully free for commercial use, with no additional commercial use restrictions.

Qwen3.8-27B enables Thinking mode by default, with reasoning depth available in three levels: Low, Medium, and X-High.

Qwen3.8-27B is an open-source model with 27 billion parameters.

The higher the reasoning effort level, the stronger the reasoning capability but the faster token consumption, representing a trade-off between cost and intelligence.

The Unsloth team released a new Qwen3.8-27B GGUF model based on the Dynamic v3 quantization scheme, achieving an additional 10% improvement in top-1% accuracy while maintaining the same model size.

Llama 3.1 405B's score on MMLU is already close to the level of GPT-4o, and Qwen 2.5 72B has surpassed GPT-4 Turbo on certain metrics in math and code generation benchmarks.

The Qwen 3.8 Max 27B version is a traditional dense model, suitable for high-performance desktops or servers.

Qwen3.8-27B trails Claude Opus 4.6 on Terminal Bench, GPQA Diamond, and HLE benchmarks (73 vs 78.2, 89.2 vs 91.3, and 30.8 vs 40, respectively).

Qwen3.8-27B adopts open weights and the Apache 2.0 license.

Local inference is constrained by the user's hardware: a 7B-parameter quantized model (such as the Q4 version of Llama 3 8B) has a file size of approximately 4–5 GB, can reach 20–40 tokens per second on a Mac with an Apple M-series chip, and may drop to 3–5 tokens per second on a laptop with only an integrated GPU and 8 GB of RAM.

The original Qwen3 model supports a 256K context, but local deployment is limited to 128K due to VRAM constraints

Perplexity's decision model has 27B parameters and is fine-tuned from Qwen3.8-27B.

On SIE Cloud, Qwen3.8-27B (FP8) is offered at $0.72 per million tokens for both input and output; GLiNER2 Large is $0.186 per million input tokens.

Qwen3.8-27B scored 91.1 on OmniDocBench v1.5, the strongest document parsing score among the generative models in this evaluation.

Qwen3.8-27B是稠密模型，全部270亿参数参与每个token的计算，依靠MTP头预测后续token并在单次前向中验证；Ornith 1.5采用MoE架构，每token仅激活约30亿参数，配合DFlash由小型草稿模型提议token块再由主模型校验。

将MTP草稿深度从默认4提升至6或8时，Qwen3.8-27B的输出速度分别降至53.9和52.4 tokens/秒，低于默认的55.1 tokens/秒。

在800 token输入提示下，Qwen3.8-27B的首token延迟为3.46秒，Ornith 1.5为0.49秒，后者的首token延迟约为前者的七分之一。

Qwen3.8-27B的输出速度为55.1 tokens/秒，Ornith 1.5为90.4 tokens/秒。