Qwen3.8-27B

3.8-27B · Family: Qwen3

Qwen (Alibaba) dense 27B multimodal model: 64 layers, mixed Gated DeltaNet + Gated Attention, 262K context (up to 1M), thinking mode, Apache 2.0.

✓ Active✓ Public access⚖ Open sourceMultimodalLLMReasoning model📁 Qwen3

Context window

262K

tokens

Parameters

27B

parameters

Release date

1 August 2026

🏢QwenProducer

Access:APIDownloadHostedDeployment:💻 Local☁ Cloud

Overview

Qwen3.8-27B is a dense multimodal language model from the Qwen family developed by the Qwen team (Alibaba), released in August 2026. It has 27B parameters and 64 layers with a mixed attention architecture combining Gated DeltaNet and Gated Attention.

The model has a built-in reasoning (thinking) mode, enabled by default and toggleable, with adjustable reasoning_effort and preserved thinking across conversation history. It offers vision-language capabilities (image, video) and native support for agentic tasks and autonomous planning, including computer use and browser use.

The native context window is 262,144 tokens, extensible to 1,000,000 tokens. A quantized Qwen3.8-27B-FP8 variant is also available. Weights are released under the Apache 2.0 license.

Classification

MultimodalLLMReasoning model

Family: Qwen3

Access & deployment

APIDownloadHosted

LocalCloud

Weights: Open source

Key parameters

📏 Context: 262K

🧩 Parameters: 27B

✓ Tools · ✓ Fine-tuning

📥 Input: text, image, video

Technical specification

Context window

262K

tokens

Parameters

27B

parameters

License

Apache 2.0

Hardware requirements

Open weights on Hugging Face (Apache 2.0); an FP8 variant is available for lower-memory deployments.

Features:✓ Tool use✓ Fine-tuning

Modalities

⬇ Input

textimagevideo

⬆ Output

textcode

Capabilities and applications

Native model capabilities

Reasoning

The model's ability to reason logically and solve complex problems.

Category: reasoning

Multi-step reasoning

Carrying out multi-step chains of reasoning across long, complex tasks.

Category: reasoning

Mathematical reasoning

The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.

Category: reasoning

Coding

Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.

Category: coding

Agentic coding

Multi-hour, multi-step programming tasks performed autonomously by the model: cloning a repository, running tests, iterating on fixes, integrating with CLI tools. Characteristic of Codex variants (GPT-5.1-Codex-Mini, Codex-Max).

Category: coding

Agentic capability

The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.

Category: planning

Tool use

The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.

Category: planning

Long context

Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.

Category: language

Multilingual

Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.

Category: language

Structured output

Producing data in structured formats such as JSON.

Category: structured_generation

Extended thinking mode

A reasoning-model variant with a larger inference budget: more thinking cycles, higher answer precision at the cost of response time. Choice between 'standard' and 'extended' thinking is left to the user (e.g. the selector in GPT-5.2 Pro).

Category: reasoning

Adaptive reasoning effort

The model decides how much 'thinking' to allocate to a given query: simple questions are answered quickly, complex problems receive more inference cycles. A GPT-5.1 feature (both Instant and Thinking) that shortens time on easy tasks and extends it for hard ones.

Category: reasoning

Language modeling

Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.

Category: language