For Developers/Models/Qwen 3.8 27B
Open SourceAlibabaReleased 2026-08

Qwen 3.8 27B

Apache 2.0 dense multimodal model designed for local deployment on consumer hardware

Context window

262K

Input / 1M tokens

Free

Output / 1M tokens

Free

Provider

Alibaba

Data verified 2026-08-18

Qwen 3.8 27B is a 27-billion parameter dense multimodal model released under Apache 2.0 license. It features native image and video understanding, a 262,144-token context window (extendable to 1M via YaRN), and flexible reasoning control. Positioned for local deployment and edge AI on consumer GPUs and workstations.

Capability index

Relative estimates (0-100) to place this model against its peers, grounded in published benchmarks.

Coding
9
Reasoning
7
Math
7
Multimodal
8
Long context
8
Speed
8
Cost efficiency
9

How to access it

Open-weight model available on Hugging Face and ModelScope. Download weights directly from Qwen/Qwen3.8-27B repository or use quantized variants through community packages.

Strengths

  • Runs on consumer hardware (16-17GB VRAM at quantized precision)
  • Native multimodal: text, image, and video understanding
  • Strong performance on coding and office automation tasks
  • Open-source with permissive Apache 2.0 license
  • Substantial improvements over Qwen 3.6-27B

Best for developers who...

Local AI deployment on edge devices and workstationsSoftware engineering and coding tasksDocument analysis and office productivityAutonomous agent developmentOn-premise AI solutions

When to choose it (and when not to)

Reach for Qwen 3.8 27B when...

  • When you need to run AI locally without cloud dependency
  • For coding and agent use cases
  • When data privacy is critical
  • For teams with limited GPU infrastructure

Look elsewhere if...

  • If you need the largest reasoning capabilities (use Qwen 3.8-Max instead)
  • For tasks requiring extreme throughput at scale

How to use it

  • Configure reasoning_effort dial to control reasoning depth
  • Works well for tool-calling and JSON output
  • Supports OpenAI-compatible Chat Completions API

Quickstart

Python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3.8-27B')
model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen3.8-27B', device_map='auto')

For quantized inference, use community GGUF builds or LM Studio. OpenRouter offers hosted endpoint at $0.45/$3.20 per 1M tokens.

API model id: Qwen/Qwen3.8-27B

Benchmarks

BenchmarkScoreNotes
SWE-Bench Pro61.7%Software engineering benchmark - Alibaba evaluation
DeepSWE 1.142.2%Significant improvement from predecessor's 13.3%
QwenSWEBench79.0%
LiveCodeBench v690.3%
GPQA Diamond89.2%
CoWorkBench70.7%Office productivity tasks

Source: Official Qwen 3.8 27B Model Card

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