Document Analysis
Summarize reports, extract key points, and answer questions about long documents.
Qwen3.5-27B — Balanced Dense Model for General Use
Qwen3.5-27B balances speed and depth with 27 billion parameters. Great for longer conversations, analysis, and general-purpose chat. Try it free.
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Qwen3.5-27B is the default model for this page. Balanced Qwen 3.5 model for longer prompts, analysis, and general chat.
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Text
balanced
Starter prompts
Free to try in the browser. The model card includes serving guides if you want to self-host it.
Parameters
27B
Architecture
Dense
Context
262K native
License
Apache 2.0
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Overview
Where Qwen3.5-27B Fits in the Family
Qwen3.5-27B is the denser middle lane in the open Qwen 3.5 lineup. It is the page to open when 9B starts feeling too thin, but you still want the simpler deployment shape of a dense checkpoint rather than a larger MoE route.
Stronger Reasoning
Handles multi-step logic, comparisons, and nuanced instructions better than the 9B model.
Denser, Still Practical
The public release is heavier than 9B, but still far simpler to reason about operationally than the larger MoE checkpoints.
Versatile Generalist
Strong across writing, coding, analysis, and translation without specializing in one area.
Qwen3.5-27B Benchmark
How Qwen3.5-27B compares to nearby models in the Qwen family.
Model
MMLU-Pro
GPQA / GPQA-family
LiveCodeBench v6
Qwen3.5-9B
Light dense model for quick prompts and lightweight coding.
Updated 2026-04-02
MMLU-Pro
82.5
GPQA / GPQA-family
81.7
LiveCodeBench v6
65.6
Qwen3.5-27B
Balanced dense model with better reasoning and coding depth.
Updated 2026-04-02
MMLU-Pro
86.1
GPQA / GPQA-family
85.5
LiveCodeBench v6
80.7
Qwen3.5-35B-A3B
Compact MoE model, also the base model behind Qwen3.5-Flash.
Updated 2026-04-02
MMLU-Pro
85.3
GPQA / GPQA-family
84.2
LiveCodeBench v6
74.6
Scores are from public model cards and the qwen.ai release page. Hosted models are labeled with their open-weight base.
Updated 2026-04-02Use Cases
What Qwen3.5-27B Is Best For
Qwen3.5-27B is the go-to model when you need more depth than 9B but want to keep things simple.
General Chat
Reliable conversational AI for customer support, internal tools, and assistants.
Moderate Coding
Code review, refactoring, debugging, and multi-file context understanding.
Content Writing
Produce well-structured articles, reports, and long-form content with better coherence.
Translation
Translate between languages with good fluency and context awareness.
Data Extraction
Parse structured data from unstructured text, emails, and web content.
FAQ
Qwen3.5-27B FAQ
Common questions about using Qwen3.5-27B.
When should I choose 27B over 9B?
Choose 27B when your tasks need stronger reasoning, longer coherent outputs, or better instruction following. If speed is your top priority and tasks are simple, stick with 9B.
Can I run Qwen3.5-27B locally?
Yes. The model card includes serving examples for the 27B checkpoint. Exact hardware needs depend on precision, framework, and context length, so treat any single VRAM number as only a rough starting point.
How does 27B compare to the MoE models?
The MoE models (35B-A3B, 122B-A10B, 397B-A17B) offer deeper reasoning by activating subsets of a larger parameter space. 27B is simpler to deploy and has more predictable latency.
Is Qwen3.5-27B good for production use?
Yes. Its balance of quality and resource efficiency makes it a strong choice for production chatbots, document pipelines, and content generation systems.
How much VRAM does Qwen3.5-27B need?
Around 16 GB at Q4 quantization, or about 54 GB at full precision. Multi-GPU setups or cloud instances with 24 GB+ VRAM work best.
Is 27B better than 9B for coding?
Yes. Qwen3.5-27B handles more complex code patterns, multi-file reasoning, and longer code contexts than 9B. The trade-off is higher memory usage and slower inference.
Can I fine-tune Qwen3.5-27B?
Yes. Tools like Unsloth and PEFT support LoRA/QLoRA fine-tuning for 27B. You will need at least 24 GB VRAM for QLoRA training.
What context window does Qwen3.5-27B support?
Qwen3.5-27B supports 262,144 native tokens and can be extended further in compatible serving stacks.