Qwen releases AgentWorld-35B-A3B: a world-model and environment-simulation MoE for agents
Qwen has released Qwen-AgentWorld-35B-A3B on Hugging Face, a 35B-parameter MoE model (3B active) built on the Qwen3.5 MoE architecture. The model is tagged for world-model and environment-simulation use cases, suggesting it is designed to simulate environments for agent training or evaluation. It is paired with a dataset called AgentWorldBench, indicating an associated evaluation suite. Early engagement is minimal (0 downloads, 4 likes) but the model represents a notable direction in agent-environment modeling from a major open-weights lab.
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Qwen-AgentWorld: Language world models for general agent simulation and planning
Alibaba's Qwen team introduces Qwen-AgentWorld, a pair of language world models (35B-A3B and 397B-A17B) trained to simulate agentic environments across 7 domains using over 10M interaction trajectories. The models are trained via a three-stage pipeline (CPT, SFT, RL) and evaluated on AgentWorldBench, a new benchmark constructed from 5 frontier models across 9 established benchmarks. Beyond simulation, the work demonstrates two downstream use cases: using the world model as a decoupled RL training environment and as a warm-up for agent foundation models, both yielding gains over baselines.
Qwen releases Qwen3.5-35B-A3B-Base multimodal MoE model on Hugging Face
Qwen has released Qwen3.5-35B-A3B-Base, a 35B-parameter mixture-of-experts image-text-to-text base model on Hugging Face, activating approximately 3B parameters per forward pass. The model supports conversational use and is compatible with Azure deployment endpoints. With over 109K downloads, it represents a notable open-weights multimodal MoE release from the Qwen team.
Qwen releases Qwen3.5-35B-A3B multimodal MoE model on Hugging Face
Qwen has released Qwen3.5-35B-A3B, a 35B-parameter mixture-of-experts image-text-to-text model with approximately 3B active parameters, published on Hugging Face. The model supports conversational use and is compatible with Azure deployment endpoints. With over 2.8 million downloads and 1,400+ likes, it has seen substantial community uptake.
Qwen releases Qwen3.5-122B-A10B multimodal MoE model on Hugging Face
Qwen has released Qwen3.5-122B-A10B, a 122B-parameter mixture-of-experts image-text-to-text model with 10B active parameters, published on Hugging Face. The model supports conversational use and is compatible with Azure deployment endpoints. High download counts (840K) and likes (564) suggest rapid community uptake shortly after release.
Qwen releases Qwen3.6-35B-A3B multimodal MoE model on Hugging Face
Qwen published Qwen3.6-35B-A3B, a 35B-parameter mixture-of-experts image-text-to-text model with 3B active parameters, on Hugging Face. The model supports conversational use and is compatible with Azure deployment endpoints. With over 5.9 million downloads and 2,000 likes, it has seen substantial community uptake.
Qwen3 Release: Flagship 235B MoE and Full Model Family Announced
Alibaba's Qwen team has released Qwen3, a new family of large language models including the flagship Qwen3-235B-A22B mixture-of-experts model. The flagship model claims competitive benchmark performance against DeepSeek-R1, OpenAI o1/o3-mini, Grok-3, and Gemini-2.5-Pro on coding, math, and general capabilities. A smaller MoE variant, Qwen3-30B-A3B, reportedly outperforms QwQ-32B despite using only one-tenth the activated parameters, and the 4B model is said to match Qwen2.5's larger models. Models are available across Hugging Face, ModelScope, and Kaggle.
Qwen releases Qwen3.5-2B-Base multimodal model on Hugging Face
Qwen released Qwen3.5-2B-Base, a 2-billion parameter base model supporting image-text-to-text tasks, on Hugging Face. The model is tagged as conversational and endpoints-compatible, suggesting deployment readiness. With nearly 180K downloads, it has seen significant early adoption in the open-weights community.
Qwen1.5-MoE: Matching 7B Model Performance with 1/3 Activated Parameters
Alibaba's Qwen team releases Qwen1.5-MoE-A2.7B, a mixture-of-experts model with only 2.7 billion activated parameters that claims performance parity with 7B dense models such as Mistral 7B and Qwen1.5-7B. The model activates roughly one-third of its total parameters during inference, offering significant compute efficiency gains. This release follows growing industry interest in MoE architectures sparked by Mixtral, and the model is available on GitHub, HuggingFace, and ModelScope.