Models

Qwen

Model · 7 items since May 2026

Part ofAlibaba

Releases

  1. Aug 3

    Qwen3.8 · Flash-Next, Omni-Flash · 27B / 2.4T · 95B active · open weights

  2. Jun 23

    Qwen-AgentWorld · 35B / 397B · 3B / 17B active · open weights

  3. May 20

    Qwen3.7-Max

Trends

Items

  1. Sep 18

    Alibaba's Qwen3.8-Omni-Flash

    Alibaba's first omni-modal model built for agentic use takes text, image, audio, and video with a 1M-token context, scores more than 25% above Qwen3.5-Omni-Plus across 29 benchmarks, and is API-only at $0.15/$0.47 per 1M tokens.

  2. Aug 26

    Alibaba's Qwen3.8-Flash-Next

    An explicit Qwen4-architecture preview pairing 125B parameters with a 51B N-gram "engram" lookup table at only 6B active per token, released with open weights on Hugging Face and ModelScope and priced roughly a third below DeepSeek-V4-Flash.

  3. Aug 15

    Alibaba's Qwen3.8-27B ties GPT-5.6 Luna

    The dense, natively multimodal 27B model (Apache 2.0, 262K context) that completes the Qwen3.8 open-weight rollout scored 52 on the Artificial Analysis Intelligence Index in independent testing, tying hosted GPT-5.6 Luna from a roughly 17GB file on one consumer GPU.

  4. Aug 13

    Alibaba releases Qwen3.8-2.4T-A95B open weights

    Alibaba shipped the weights it promised the week before, its first Max-class open release: a 2.4T-parameter (95B active) MoE with a 256K native context extensible to about 1M, on Hugging Face and ModelScope alongside an FP8 build.

  5. Aug 3

    Alibaba's Qwen3.8-Max reaches general availability

    The 2.4T-parameter (95B active) multimodal MoE with a 1M-token context went GA via API at $2/$6 per 1M tokens, and Alibaba committed to open weights for it and a smaller Qwen3.8-27B, its first open-weight Max-class model.

  6. Jun 23

    Alibaba's Qwen-AgentWorld world models

    The first open-weight language world models (35B-A3B and 397B-A17B) simulate agentic environments across seven domains via long chain-of-thought, serving as decoupled simulators that improve agentic RL beyond real-environment training, released with the AgentWorldBench evaluation.

  7. May 20

    Alibaba's Qwen3.7-Max

    An API-only model built for long-horizon autonomy ran 35 hours of unsupervised kernel optimization (1,158 tool calls) for a 10x speedup versus DeepSeek V4 Pro's 3.3x, and scores 80.4% on SWE-Verified and 92.4% on GPQA Diamond.