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
90
Reasoning
70
Math
70
Multimodal
80
Long context
80
Speed
80
Cost efficiency
90

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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