2026-09-02 upgraded snapshot of Qwen3.8 Max with stronger coding, collaborative agents, and multimodal document understanding
Models
Every model in the catalog with source-linked pricing, context limits, provider availability, and published benchmark results.
Qwen vision-language model for visual reasoning, documents, and agent tasks
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
Flagship Qwen model for complex reasoning, coding, and agentic workflows
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
Flagship Qwen3 model for coding agents, complex reasoning, and tool use
Qwen coding model for software agents, repository edits, and code reasoning
Efficient Qwen model for fast chat, extraction, and high-volume workloads
Hosted Qwen coder for software agents, repo edits, and long-context code
Efficient Qwen model for fast chat, extraction, and high-volume workloads
Qwen instruction model for multilingual chat, reasoning, and tool use
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...
Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling...
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-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...
Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...
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...
Qwen3.8 Max 0902 is an updated snapshot of Qwen3.8 Max from Alibaba's Qwen team. It is a 2.4-trillion-parameter mixture-of-experts model that accepts text, image, and video input and returns text,...
Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.
Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers...
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...
Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over...
Qwen3 Coder Plus is Alibaba's proprietary version of the Open Source Qwen3 Coder 480B A35B. It is a powerful coding agent model specializing in autonomous programming via tool calling and...
Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its...
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out of 2.4 trillion total. It is...
Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It...
Qwen3.8 Max (0803) is the August 3, 2026 checkpoint of Qwen3.8 Max, the flagship model in Alibaba's Qwen3.8 series and the general-availability successor to the Qwen3.8 Max Preview. It is...
Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be...
Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over...
Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default. It’s designed for hard multi-step problems; math proofs, code synthesis/debugging, logic, and agentic...
Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.
Qwen3.8 Flash is a multimodal reasoning model from Alibaba. It is suited for coding assistance, agentic workflows, visual understanding, document and codebase analysis, desktop interaction, chart analysis, and long-video analysis.
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out of 2.4 trillion total. It is...
Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.
Qwen3.6 Flash is a fast, efficient language model from Alibaba's Qwen 3.6 series. It supports text, image, and video input with a 1M token context window. Tiered pricing kicks in...
Qwen3.7 Flash is a vision-language reasoning model from Alibaba. It is suited for multimodal agents, visual coding, search, and computer interaction, with strengths in object recognition, spatial understanding, and real-world...
Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks,...
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated...
Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse mixture-of-experts architecture with approximately 1 trillion total parameters. It is optimized for agentic coding, tool use, and...
Qwen3.6 27B is a dense 27-billion-parameter language model from the Qwen Team at Alibaba, released in April 2026. It features hybrid multimodal capabilities — accepting text, image, and video inputs...
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...
| Model | Creator | Inputs | Context | Input | Output | Released | Compare |
|---|---|---|---|---|---|---|---|
| Qwen3.8 Max 0902alibaba/qwen3.8-max-0902 | 1M | $1.71 | $5.14 | 2026-09-02 | |||
| Qwen3.8 Flashalibaba/qwen3.8-flash | 1M | $0.15 | $0.47 | 2026-08-26 | |||
| Qwen3.8 Maxalibaba/qwen3.8-max | 1M | $2 | $6 | 2026-08-03 | |||
| Qwen3.8 Max Previewalibaba/qwen3.8-max-preview | 1M | $2 | $6 | 2026-07-19 | |||
| Qwen3.7 Flashalibaba/qwen3.7-flash | 1M | $0.028 | $0.113 | 2026-07-15 | |||
| Qwen3.7 Plusalibaba/qwen3.7-plus | 1M | $0.5 | $3 | 2026-06-02 | |||
| Qwen3.7 Maxalibaba/qwen3.7-max | 1M | $2.5 | $7.5 | 2026-05-21 | |||
| Qwen3.6 Flashalibaba/qwen3.6-flash | 1M | $0.188 | $1.125 | 2026-04-27 | |||
| Qwen3.6 Max Previewalibaba/qwen3.6-max-preview | 262.144K | $1.3 | $7.8 | 2026-04-20 | |||
| Qwen3.6 Plusalibaba/qwen3.6-plus | 1M | $0.5 | $3 | 2026-04-02 | |||
| Qwen3.5 Flashalibaba/qwen3.5-flash | 1M | $0.029 | $0.287 | 2026-02-23 | |||
| Qwen3.5 Plusalibaba/qwen3.5-plus | 1M | $0.4 | $2.4 | 2026-02-16 | |||
| Qwen3-VL Plusalibaba/qwen3-vl-plus | 262.144K | $0.2 | $1.6 | 2025-09-23 | |||
| Qwen3 Maxalibaba/qwen3-max | 262.144K | $1.2 | $6 | 2025-09-23 | |||
| Qwen3 Coder Flashalibaba/qwen3-coder-flash | 1M | $0.3 | $1.5 | 2025-07-28 | |||
| Qwen Flashalibaba/qwen-flash | 1M | $0.05 | $0.4 | 2025-07-28 | |||
| Qwen3 Coder Plusalibaba/qwen3-coder-plus | 1.04858M | $1 | $5 | 2025-07-23 | |||
| Qwen Turboalibaba/qwen-turbo | 1M | $0.05 | $0.2 | 2024-11-01 | |||
| Qwen Plusalibaba/qwen-plus | 1M | $0.4 | $1.2 | 2024-01-25 | |||
| Qwen: Qwen3.5 397B A17Bqwen/qwen3.5-397b-a17b | 262.144K | $0.55 | $3.5 | — | |||
| Qwen: Qwen3 Coder Nextqwen/qwen3-coder-next | 262.144K | $0.12 | $0.8 | — | |||
| Qwen: Qwen3 Coder Flashqwen/qwen3-coder-flash | 1M | $0.195 | $0.975 | — | |||
| Qwen: Qwen3 Next 80B A3B Instructqwen/qwen3-next-80b-a3b-instruct | 262.144K | $0.09 | $1.1 | — | |||
| Qwen: Qwen3 Coder 30B A3B Instructqwen/qwen3-coder-30b-a3b-instruct | 262.144K | $0.07 | $0.28 | — | |||
| Qwen: Qwen3 235B A22B Instruct 2507qwen/qwen3-235b-a22b-2507 | 262.144K | $0.22 | $0.88 | — | |||
| Qwen: Qwen3 Next 80B A3B Instruct (free)qwen/qwen3-next-80b-a3b-instruct:free | 262.144K | Free | Free | — | |||
| Qwen: Qwen3.8 Max (0902)qwen/qwen3.8-max-0902 | 1M | $2 | $6 | — | |||
| Qwen: Qwen Plus 0728 (thinking)qwen/qwen-plus-2025-07-28:thinking | 1M | $0.26 | $0.78 | — | |||
| Qwen: Qwen3.6 Plusqwen/qwen3.6-plus | 1M | $0.325 | $1.95 | — | |||
| Qwen: Qwen3.5-9B (batch)qwen/qwen3.5-9b:batch | 262.144K | $0.17 | $0.25 | — | |||
| Qwen: Qwen3 Coder 480B A35B (free)qwen/qwen3-coder:free | 262K | Free | Free | — | |||
| Qwen: Qwen3 Coder Plusqwen/qwen3-coder-plus | 1M | $0.65 | $3.25 | — | |||
| Qwen: Qwen3.7 Plusqwen/qwen3.7-plus | 1M | $0.32 | $1.28 | — | |||
| Qwen: Qwen3.8 2.4T A95B (batch)qwen/qwen3.8-2.4t-a95b:batch | 1.01M | $2 | $6 | — | |||
| Qwen: Qwen3 Maxqwen/qwen3-max | 262.144K | $0.78 | $3.9 | — | |||
| Qwen: Qwen3.8 Max (0803)qwen/qwen3.8-max | 1M | $2 | $6 | — | |||
| Qwen: Qwen3.8 27Bqwen/qwen3.8-27b | 1M | $0.42 | $3 | — | |||
| Qwen: Qwen3 Coder 480B A35Bqwen/qwen3-coder | 262.144K | $0.3 | $1 | — | |||
| Qwen: Qwen3 Next 80B A3B Thinkingqwen/qwen3-next-80b-a3b-thinking | 262.144K | $0.15 | $1.2 | — | |||
| Qwen: Qwen-Plusqwen/qwen-plus | 1M | $0.26 | $0.78 | — | |||
| Qwen: Qwen3.8 Flashqwen/qwen3.8-flash | 1M | $0.15 | $0.47 | — | |||
| Qwen: Qwen3.8 2.4T A95Bqwen/qwen3.8-2.4t-a95b | 1M | $2 | $6 | — | |||
| Qwen: Qwen Plus 0728qwen/qwen-plus-2025-07-28 | 1M | $0.26 | $0.78 | — | |||
| Qwen: Qwen3.6 Flashqwen/qwen3.6-flash | 1M | $0.188 | $1.125 | — | |||
| Qwen: Qwen3.7 Flashqwen/qwen3.7-flash | 1M | $0.03 | $0.13 | — | |||
| Qwen: Qwen3.7 Maxqwen/qwen3.7-max | 1M | $1.475 | $4.425 | — | |||
| Qwen: Qwen3.6 35B A3Bqwen/qwen3.6-35b-a3b | 262.144K | $0.1 | $0.9 | — | |||
| Qwen: Qwen3.6 Max Previewqwen/qwen3.6-max-preview | 262.144K | $1.027 | $6.162 | — | |||
| Qwen: Qwen3.6 27Bqwen/qwen3.6-27b | 262.144K | $0.3 | $2 | — | |||
| Qwen: Qwen3.5-9Bqwen/qwen3.5-9b | 262.144K | $0.1 | $0.15 | — |