In 2025, Alibaba’s Qwen evolved to the “Qwen3” generation, announcing official product lines from text LLMs to vision, audio, code, omni-modal, and cloud API flagship models. Each model has clearly differentiated specifications in input/output modalities, delivery methods (open weights/API), context length, multilingual support, and reasoning modes (Thinking/Non-Thinking), making requirement-based selection essential.
This article concisely organizes the Qwen3 model lineup, features, and implementation perspectives based on primary sources: Qwen official blog, GitHub, and Alibaba Cloud Model Studio.
Table of Contents
- Qwen3 Model Overall Structure
- Features and Strengths of Each Model
- Qwen3 (Text LLM)
- Qwen3-VL (Image/Video × Language)
- Qwen3-ASR (Speech Recognition)
- Qwen3-TTS (Text-to-Speech)
- Qwen3-Omni
- Qwen3-Coder (Code-Specialized LLM)
- Qwen3-Max (Top-tier API)
- Model Selection Guidelines
- Release Format and Licensing
- Summary
Qwen3 Model Overall Structure
First, organize representative models by key dimensions: use case, release format, context length, and language support.
| Model | Input/Output Modality (Input→Output) | Release Format | Context Length Reference | Languages (Key Points) |
|---|---|---|---|---|
| Qwen3 (LLM) | Text→Text | Open weights (Dense 0.6B/1.7B/4B/8B/14B/32B, MoE 30B-A3B/235B-A22B) | Standard long-context, up to 1M token expansion (2507) | 119 languages/dialects (including Japanese) |
| Qwen3-VL | Image/PDF/Video + Text→Text | Open weights | Long document/video analysis (long-context expansion) | 33-language visual understanding/OCR |
| Qwen3-ASR | Audio→Text | API provision | — (single transcription) | 11-language auto-recognition, context bias, noise resistance |
| Qwen3-TTS | Text→Audio | API provision | — (streaming synthesis) | 17 voices, multilingual/dialect support |
| Qwen3-Omni | Text/Image/Audio/Video→Text/Audio | Open weights | Real-time streaming input/output | Text 119, audio input 19/output 10 languages |
| Qwen3-Coder | Code/Text→Code/Text | Open weights (e.g., 480B-A35B) | Native 256K / up to 1M expansion | Multilingual code, agent aptitude |
| Qwen3-Max | Text→Text | API provision (top-tier) | 262K tokens (with context caching) | Multilingual (large-scale pretraining) |
The above specifications are based on explicit statements in Qwen official documentation, repositories, and model cards.
Features and Strengths of Each Model
Here we organize each model’s basic specifications and release format with primary source references.
Qwen3 (Text LLM)
Alibaba’s LLM. Both Dense and MoE configurations, supporting up to 1M token long-context processing.
-
Dense (0.6B–32B) + MoE (30B-A3B / 235B-A22B) released under Apache-2.0.
-
119-language support with high Japanese accuracy. Thinking/Non-Thinking switching adjusts reasoning depth.
-
2507 version extended context length from 256K to up to 1M.
Qwen3-VL (Image/Video × Language)
Vision-language model understanding images, PDFs, and videos. Significantly enhanced OCR and spatial understanding.
-
Dense/MoE from 2B–235B in both Instruct/Thinking variants.
-
33-language OCR and high-resolution input (up to ~16M pixels).
-
Optimal for multimodal processing like document analysis, UI understanding, video summarization.
You may also want to read
Qwen3-VL Guide: Images, Videos, and GUI Operations Explained
Qwen3-ASR (Speech Recognition)
High-accuracy API model converting speech to text. Strong against noise and singing.
-
11-language support (including Japanese) with automatic language identification.
-
Context bias feature strengthens accuracy on proper nouns and technical terms.
-
API format, optimal for real-time transcription and subtitle generation.
You may also want to read
Alibaba’s “Qwen3-ASR” Explained: Multilingual Speech Recognition Features and Applications
Qwen3-TTS (Text-to-Speech)
API generating natural audio from text. Streaming synthesis support.
-
17 voice types, multilingual/dialect support (expanded from previous version).
-
Billing unit is “character count,” real-time response possible.
-
Suitable for narration, narration, dialogue agent audio output.
Qwen3-Omni
Unified model processing text, images, audio, and video in real-time.
-
Thinker-Talker architecture realizes audio understanding and output in a single model.
-
Text 119 languages, audio input 19/output 10 languages.
-
Streaming processing enables natural turn-taking.
Qwen3-Coder (Code-Specialized LLM)
Large-scale MoE model for code generation, auto-correction, and agent-like development support.
-
Representative model is Qwen3-Coder-480B-A35B (256K context, up to 1M expansion).
-
~70% of 7.5 trillion token training is code data.
-
CLI “Qwen Code” executable, strong in development automation.
Qwen3-Max (Top-tier API)
Cloud-based top-tier model of Qwen3 series. Handles high-accuracy large-scale processing.
-
262K token long-context with Search Agent support.
-
Non-Thinking only, specialized for stable responses and agent integration.
-
API-only provision (no weight release), commercial use intended.
You may also want to read
What is Qwen3-Max? Comprehensive Guide to Setup and Operations
Model Selection Guidelines
Reverse-engineering from “what you want to do” naturally narrows candidates.
- General dialogue, RAG, summarization, translation: Qwen3 (LLM)
Open weights enable operational choice, Thinking/Non-Thinking switching allows cost adjustment.
- Document OCR / layout-preserving extraction / video summarization: Qwen3-VL
Visual understanding and 33-language OCR confirmed in primary sources.
-
Recording transcription, subtitle production, meeting minutes: Qwen3-ASR. 11 languages, noise resistance, context bias API features officially specified.
-
Text → natural audio distribution: Qwen3-TTS
17 voices, multilingual/dialect, streaming synthesis API is recommended route.
- Real-time dialogue including video + audio: Qwen3-Omni
Text 119 / audio input/output 19/10 languages enable streaming dialogue construction.
- Code automation (modification, testing, browser operations): Qwen3-Coder
Verify 480B-A35B and 256K–1M token support in model cards.
- High-difficulty task API operations: Qwen3-Max
Design with 262K large capacity, agent aptitude, non-thinking mode specifications.
Ultimately, select open-weight or API format considering data location, security policy, operational SLA for clear design.
Release Format and Licensing
Confirming “open weights or API-only” upfront from primary sources smooths architecture selection.
-
Open weights (Apache-2.0): Qwen3 (LLM) / Qwen3-VL / Qwen3-Omni / Qwen3-Coder. Verify license notation on GitHub and Hugging Face.
-
API provision (no weight release): Qwen3-ASR / Qwen3-TTS / Qwen3-Max. Listed in Model Studio catalog/specifications.
This distinction directly affects cost (inference rate/GPU ownership), operations (update frequency), and governance (data handling).
Summary
Qwen3 centers on open-weight foundation LLM (Qwen3), with vision-language (Qwen3-VL), speech recognition (Qwen3-ASR), text-to-speech (Qwen3-TTS), omni-modal (Qwen3-Omni), code-specialized (Qwen3-Coder), and API top-tier (Qwen3-Max), organized for easy requirement-based selection.
In requirement definition, cross-reference input/output modalities (text/image/audio/video), release format (open weights or API), required context length (expansion to 1M tokens), and required languages (text 119, OCR 33, ASR 11, audio output 10, etc.) from primary sources to converge candidates to 1–2.
For next actions, select the model closest to the target task for small-scale PoC (quality, latency, cost), and once passing grades are visible, proceed to production design including RAG, tool integration, and monitoring operations for efficient migration.