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,...
Models
Every model in the catalog with source-linked pricing, context limits, provider availability, and published benchmark results.
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...
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...
Qwen3.6 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...
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...
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...
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...
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...
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...
Qwen3.5-9B 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 | Score | Inputs | Context | Input | Output | Released | Compare |
|---|---|---|---|---|---|---|---|---|
| Qwen: Qwen3.8 Max (0902)qwen/qwen3.8-max-0902 | 71.8 | 1M | $2 | $6 | — | |||
| Qwen: Qwen3.8 Max (0803)qwen/qwen3.8-max | 68.9 | 1M | $2 | $6 | — | |||
| Qwen: Qwen3.8 27Bqwen/qwen3.8-27b | 68.1 | 1M | $0.42 | $3 | — | |||
| Qwen: Qwen3.6 Plusqwen/qwen3.6-plus | 54.5 | 1M | $0.325 | $1.95 | — | |||
| Qwen: Qwen3.6 27Bqwen/qwen3.6-27b | 53.7 | 262.144K | $0.3 | $2 | — | |||
| Qwen: Qwen3.5 397B A17Bqwen/qwen3.5-397b-a17b | 48.2 | 262.144K | $0.55 | $3.5 | — | |||
| Qwen: Qwen3.5-122B-A10Bqwen/qwen3.5-122b-a10b | 45.7 | 262.144K | $0.26 | $2.08 | — | |||
| Qwen: Qwen3.6 35B A3Bqwen/qwen3.6-35b-a3b | 41.9 | 262.144K | $0.1 | $0.9 | — | |||
| Qwen: Qwen3.5-35B-A3Bqwen/qwen3.5-35b-a3b | 37.0 | 256K | $0.312 | $1.25 | — | |||
| Qwen: Qwen3.5-9B (batch)qwen/qwen3.5-9b:batch | 28.7 | 262.144K | $0.17 | $0.25 | — | |||
| Qwen: Qwen3.5-9Bqwen/qwen3.5-9b | 28.7 | 262.144K | $0.1 | $0.15 | — |