Qwen-7B
Qwen-7B established a continuously expanding Chinese open-model family for developers.
FAMILY / ALIBABA CLOUD
An open multilingual family spanning dense, MoE, and hybrid-reasoning models.
01 / RELEASE SEQUENCE
02 / GENERATIONS
Qwen-7B established a continuously expanding Chinese open-model family for developers.
The 72B Qwen1.5 tier with improved multilingual capability and a 32K context window.
Qwen2 expanded multilingual and coding capabilities; the 72B tier uses the Tongyi Qianwen license, unlike the smaller Apache 2.0 models.
The largest general-purpose dense Qwen2.5 tier, improving coding, mathematics, and structured output; the 72B tier is not Apache-licensed.
A representative coding branch of Qwen2.5, with 32.5B parameters and Base and Instruct weights.
A vision-language release group with 3B, 7B, and 72B tiers for image, document, and video understanding.
A 32B open reasoning branch using reinforcement learning to improve complex problem solving.
Uses a Thinker-Talker architecture to understand text, images, audio, and video and stream text and speech.
Qwen3 brought hybrid reasoning and efficient MoE together in an Apache 2.0 open-weight model.
An agentic coding MoE with 480B total and 35B active parameters; native 256K context can be extrapolated to 1M.
An 80B-total, 3B-active architecture preview combining hybrid linear attention and ultra-sparse MoE.
A trillion-plus-parameter Qwen3 flagship for coding, agents, and million-token professional work.
The first Qwen3.5 flagship, extending the Qwen3-Next hybrid architecture into a native multimodal agent model.
The efficient Qwen3.6 MoE tier, using 3B active parameters for multimodal, coding, and tool tasks.
The 27B dense Qwen3.6 tier for local deployment and general multimodal agents.
Qwen3.8’s open checkpoint with 2.4T total and 95B active parameters, native 262,144 context, extensible to about 1M.
Service capabilities and open-checkpoint parameters, weights, and licensing are recorded separately so product limits are not mistaken for checkpoint specifications.
The 27B dense open Qwen3.8 model for local deployment, vision, coding, and tool use.
It introduced QSA, Gated Residual, and N-gram memory to reduce compute while advancing long-context agents.
It turns the flagship release into a traceable service snapshot, concentrating the update on coding, cowork, agent orchestration, and multimodal understanding.
It extends Qwen3.8 from general omni-modal and agent workflows into a dedicated low-latency speech-translation branch.
It extends Qwen3.8-Flash-Next agentic capability into unified audio-video understanding and multimodal workflows with DashScope and OpenAI protocol access.
It advances Qwen’s audio line into a fuller voice-agent interface with additional system voices and a longer context.
It extends Qwen3.8 omni-modal capability from file understanding into real-time audio/video dialogue and MCP tool workflows.
A structured decision model for high-frequency business decisions, supporting classification, yes/no decisions, and scoring with probabilities and confidence; it returns structured decisions rather than natural-language text.