Models

2026-09-02 upgraded snapshot of Qwen3.8 Max with stronger coding, collaborative agents, and multimodal document understanding

Open-weight experimental preview of the Qwen4 architecture: hybrid-attention MoE (125B total, 6B active) with vision encoder for coding, agent tasks, and image and video understanding

Qwen vision-language model for visual reasoning, documents, and agent tasks

Dense 27B vision-language model for coding, agent tasks, and image and video understanding

Open-weight sparse MoE (2.4T total, 95B active), the open-weight twin of Qwen3.8 Max for coding, research, complex reasoning, and agentic workflows

2.4-trillion-parameter MoE flagship for coding, professional work, multimodal understanding, and long-horizon agentic workflows

Preview Qwen flagship for million-token multimodal reasoning and long-horizon agentic workflows

Lightweight multimodal Qwen model for high-throughput text, image, and video tasks

Multimodal Qwen workhorse for long-context agents, visual inputs, and coding

Qwen frontier model tuned for agent frameworks, coding assistants, and long tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Flagship Qwen model for complex reasoning, coding, and agentic workflows

Open multimodal Qwen MoE for local agents that need vision, audio, and code

Earlier Qwen multimodal workhorse for million-token agent and document tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen instruction model for multilingual chat, reasoning, and tool use

Qwen vision-language model for visual reasoning, documents, and agent tasks

Large open Qwen multimodal MoE for visual agents and long technical tasks

Open-weight Qwen coding model for agents, repository edits, and multi-turn tool use

Flagship Qwen3 model for coding agents, complex reasoning, and tool use

Qwen vision-language thinking model for visual reasoning, documents, and agent tasks

Qwen vision-language instruct model for visual reasoning, documents, and agent tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Efficient Qwen thinking model for local reasoning, math, and coding agents

Qwen instruction model for multilingual chat, reasoning, and tool use

Efficient Qwen model for fast chat, extraction, and high-volume workloads

Qwen coding model for software agents, repository edits, and code reasoning

Hosted Qwen coder for software agents, repo edits, and long-context code

Updated large open Qwen3 MoE instruct model for multilingual chat, coding, and tool use

Sparse MoE Qwen model with 3B active parameters for efficient chat and reasoning

Smaller Qwen coder for efficient local agents and repo-level fixes

Open Qwen coding heavyweight for repository reasoning and agentic engineering

Large open Qwen MoE for multilingual reasoning, coding, and tool use

Dense open Qwen model for self-hosted chat, reasoning, and coding

Qwen reasoning model for deliberate problem solving, math, and coding

Open reasoning model from the Qwen team for math, coding, and step-by-step problem solving

Open coding-focused Qwen model for code generation, repair, and repository reasoning

Efficient Qwen model for fast chat, extraction, and high-volume workloads

Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen instruction model for multilingual chat, reasoning, and tool use

Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual...

Qwen3-VL-8B-Thinking is the reasoning-optimized variant of the Qwen3-VL-8B multimodal model, designed for advanced visual and textual reasoning across complex scenes, documents, and temporal sequences. It integrates enhanced multimodal alignment and...

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...

Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the...