Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception...
Models
Every model in the catalog with source-linked pricing, context limits, provider availability, and published benchmark results.
Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math....
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table...
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 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 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-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...
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-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...
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 Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.
Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking. The model is designed specifically for “thinking mode,” where internal reasoning traces are separated...
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...
Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, 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-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-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...
Qwen3-235B-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, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique...
Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between "thinking" mode for math,...
Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...
Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...
Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...
Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...
Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...
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...
Qwen3.8 Max is the flagship model in Alibaba's Qwen3.8 series, the general-availability successor to the Qwen3.8 Max Preview. It is a multimodal reasoning model intended for complex reasoning, visual understanding,...
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...
No provider description is available for this model yet.
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...
Qwen3.8 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...
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...
No provider description is available for this model yet.
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,...
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...
No provider description is available for this model yet.
No provider description is available for this model yet.
Qwen3.8 Omni Flash is an omni-modal reasoning model from Alibaba, the first Qwen model built around agentic capabilities with native audio-video understanding. It is suited for audio-video analysis and summarization,...
Qwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point. It accepts text, image, and video...
No provider description is available for this model yet.
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...
| Model | Creator | Inputs | Context | Input | Output | Released | Compare |
|---|---|---|---|---|---|---|---|
| Qwen: Qwen3 VL 30B A3B Instructqwen/qwen3-vl-30b-a3b-instruct | 262.144K | $0.15 | $0.6 | — | |||
| Qwen: Qwen3 VL 235B A22B Thinkingqwen/qwen3-vl-235b-a22b-thinking | 131.072K | $0.4 | $4 | — | |||
| Qwen: Qwen3 VL 235B A22B Instructqwen/qwen3-vl-235b-a22b-instruct | 131.072K | $0.21 | $1.9 | — | |||
| Qwen: Qwen3 Maxqwen/qwen3-max | 262.144K | $0.78 | $3.9 | — | |||
| Qwen: Qwen3 Coder Plusqwen/qwen3-coder-plus | 1M | $0.65 | $3.25 | — | |||
| Qwen: Qwen3 Coder Flashqwen/qwen3-coder-flash | 1M | $0.195 | $0.975 | — | |||
| Qwen: Qwen3 Next 80B A3B Thinkingqwen/qwen3-next-80b-a3b-thinking | 262.144K | $0.15 | $1.2 | — | |||
| Qwen: Qwen3 Next 80B A3B Instruct (free)qwen/qwen3-next-80b-a3b-instruct:free | 262.144K | Free | Free | — | |||
| Qwen: Qwen3 Next 80B A3B Instructqwen/qwen3-next-80b-a3b-instruct | 262.144K | $0.09 | $1.1 | — | |||
| Qwen: Qwen Plus 0728 (thinking)qwen/qwen-plus-2025-07-28:thinking | 1M | $0.26 | $0.78 | — | |||
| Qwen: Qwen Plus 0728qwen/qwen-plus-2025-07-28 | 1M | $0.26 | $0.78 | — | |||
| Qwen: Qwen3 30B A3B Thinking 2507qwen/qwen3-30b-a3b-thinking-2507 | 81.92K | $0.2 | $2.4 | — | |||
| Qwen: Qwen3 Coder 30B A3B Instructqwen/qwen3-coder-30b-a3b-instruct | 262.144K | $0.07 | $0.28 | — | |||
| Qwen: Qwen3 30B A3B Instruct 2507qwen/qwen3-30b-a3b-instruct-2507 | 262.144K | $0.09 | $0.3 | — | |||
| Qwen: Qwen3 235B A22B Thinking 2507qwen/qwen3-235b-a22b-thinking-2507 | 128K | $0.45 | $3.5 | — | |||
| Qwen: Qwen3 Coder 480B A35B (free)qwen/qwen3-coder:free | 262K | Free | Free | — | |||
| Qwen: Qwen3 Coder 480B A35Bqwen/qwen3-coder | 262.144K | $0.3 | $1 | — | |||
| Qwen: Qwen3 235B A22B Instruct 2507qwen/qwen3-235b-a22b-2507 | 262.144K | $0.09 | $0.55 | — | |||
| Qwen: Qwen3 30B A3Bqwen/qwen3-30b-a3b | 40.96K | $0.12 | $0.5 | — | |||
| Qwen: Qwen3 8Bqwen/qwen3-8b | 131.072K | $0.117 | $0.455 | — | |||
| Qwen: Qwen3 14Bqwen/qwen3-14b | 40.96K | $0.12 | $0.24 | — | |||
| Qwen: Qwen3 32Bqwen/qwen3-32b | 40.96K | $0.08 | $0.28 | — | |||
| Qwen: Qwen3 235B A22Bqwen/qwen3-235b-a22b | 131.072K | $0.455 | $1.82 | — | |||
| Qwen: Qwen2.5 VL 72B Instructqwen/qwen2.5-vl-72b-instruct | 128K | $0.8 | $1 | — | |||
| Qwen: Qwen-Plusqwen/qwen-plus | 1M | $0.26 | $0.78 | — | |||
| Qwen2.5 Coder 32B Instructqwen/qwen-2.5-coder-32b-instruct | 32.768K | $0.66 | $1 | — | |||
| Qwen: Qwen2.5 7B Instructqwen/qwen-2.5-7b-instruct | 32.768K | $0.1 | $0.2 | — | |||
| Qwen2.5 72B Instructqwen/qwen-2.5-72b-instruct | 32.768K | $0.36 | $0.4 | — | |||
| Qwen: Qwen3.7 Flashqwen/qwen3.7-flash | 1M | $0.03 | $0.13 | — | |||
| Qwen: Qwen3.8 Maxqwen/qwen3.8-max | 1M | $2 | $6 | — | |||
| Qwen: Qwen3.8 2.4T A95Bqwen/qwen3.8-2.4t-a95b | 1.01M | $2 | $6 | — | |||
| Qwen/Qwen3.8-2.4T-A95BQwen/Qwen3.8-2.4T-A95B | Not documented | — | — | — | |||
| Qwen: Qwen3.8 27Bqwen/qwen3.8-27b | 262.144K | $0.2 | $2.5 | — | |||
| Qwen: Qwen3.8 Flashqwen/qwen3.8-flash | 1M | $0.15 | $0.47 | — | |||
| Qwen: Qwen3.8 2.4T A95B (batch)qwen/qwen3.8-2.4t-a95b:batch | 1.01M | $2 | $6 | — | |||
| Qwen: Qwen3.5-9B (batch)qwen/qwen3.5-9b:batch | 262.144K | $0.17 | $0.25 | — | |||
| Qwen/Qwen3.8-Flash-NextQwen/Qwen3.8-Flash-Next | Not documented | — | — | — | |||
| Qwen: Qwen3.8 Max (0902)qwen/qwen3.8-max-0902 | 1M | $2 | $6 | — | |||
| Qwen: Qwen3.8 27B (free)qwen/qwen3.8-27b:free | 262.144K | Free | Free | — | |||
| Qwen/Qwen-Image-2.1-PE-T2IQwen/Qwen-Image-2.1-PE-T2I | Not documented | — | — | — | |||
| Qwen/Qwen-Image-2.1Qwen/Qwen-Image-2.1 | Not documented | — | — | — | |||
| Qwen: Qwen3.8 Omni Flashqwen/qwen3.8-omni-flash | 1M | $0.15 | $0.47 | — | |||
| Qwen: Qwen3.8 Max Primeqwen/qwen3.8-max-prime | 1M | $4 | $12 | — | |||
| Qwen/Qwen-Image-2.1-TurboQwen/Qwen-Image-2.1-Turbo | Not documented | — | — | — | |||
| Qwen: Qwen3.7 Plus (batch)qwen/qwen3.7-plus:batch | 262.144K | $0.23 | $0.92 | — |