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Qwen/Qwen-Drive-1.0-4B Not documented context Input not listed Output not listed

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...

qwen/qwen3.7-flash 1M context $0.03/M input $0.13/M output

Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...

qwen/qwen3-vl-8b-instruct 131.072K context $0.117/M input $0.455/M output

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...

qwen/qwen3.6-27b 262.144K context $0.3/M input $2/M output

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...

qwen/qwen3.6-35b-a3b 262.144K context $0.1/M input $0.9/M output

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...

qwen/qwen3.5-9b 262.144K context $0.1/M input $0.15/M output

The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of...

qwen/qwen3.5-27b 262.144K context $0.195/M input $1.56/M output

The Qwen3.5 122B-A10B 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. In terms of...

qwen/qwen3.5-122b-a10b 262.144K context $0.26/M input $2.08/M output

The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...

qwen/qwen3.5-35b-a3b 256K context $0.312/M input $1.25/M output

The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of...

qwen/qwen3.5-plus-02-15 1M context $0.26/M input $1.56/M output

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...

qwen/qwen3-vl-30b-a3b-instruct 262.144K context $0.15/M input $0.6/M output

Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels...

qwen/qwen3-vl-30b-a3b-thinking 131.072K context $0.2/M input $2.4/M output

Qwen3.5 Plus (April 2026) is a large-scale multimodal language model from Alibaba. It accepts text, image, and video input and produces text output, with a 1M token context window. This...

qwen/qwen3.5-plus-20260420 1M context $0.3/M input $1.8/M output

The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the...

qwen/qwen3.5-flash-02-23 1M context $0.065/M input $0.26/M output

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...

qwen/qwen3.5-397b-a17b 262.144K context $0.55/M input $3.5/M output

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...

qwen/qwen3-vl-8b-thinking 131.072K context $0.18/M input $2.1/M output

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...

qwen/qwen3.6-plus 1M context $0.325/M input $1.95/M output

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/qwen3.8-max-0902 1M context $2/M input $6/M output

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...

qwen/qwen3.7-plus 1M context $0.32/M input $1.28/M output
Open weights

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Qwen/Qwen3.8-27B Not documented context Input not listed Output not listed

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...

qwen/qwen3.5-9b:batch 262.144K context $0.17/M input $0.25/M output
Open weights

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Qwen/Qwen-Image-Bench Not documented context Input not listed Output not listed
Open weights

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Qwen/Qwen3.5-397B-A17B Not documented context Input not listed Output not listed
Open weights

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Qwen/Qwen3.5-122B-A10B Not documented context Input not listed Output not listed
Open weights

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Qwen/Qwen3.5-27B Not documented context Input not listed Output not listed
Open weights

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Qwen/Qwen3.6-35B-A3B Not documented context Input not listed Output not listed