Model · Image and text to text
Qwen
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. For more details, please refer to our blog post Qwen3.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty cells (--) indicate scores not yet available or not applicable. MathVision：our model’s score is…
Open weights
apache-2.0
403.4B parameters
262,144 tokens
transformers
A lossless repack of stepfun-ai/Step-5-Preview-NVFP4 (revision bc246cc2848e17b37f377eda8fd825c5b3018d1f), 17.1 GB smaller. The NVFP4 block scales of the routed experts are stored in b12x's compressed scale format (CSF), the lossless format introduced with nvidia/Qwen3.8-Flash-Next-FP4-Local. Every FP4 weight byte and every other tensor is identical to StepFun's checkpoint, and every scale decodes to StepFun's exact bytes, so this checkpoint computes exactly what Step-5-Preview-NVFP4 computes. Step 5 Preview is StepFun AI's model: a sparse mixture of experts with 600B total and 27B active parameters, text and image input, up to 1M tokens of context. Model card: stepfun-ai/Step-5-Preview.…
Open weights
other
338.5B parameters
1,048,576 tokens
Model · Image and text to text
Z.ai
Join our WeChat or Discord community. Check out the GLM-5.3-Flash blog and GLM-5 Technical report. Use GLM-5.3-Flash API services on Z.ai API Platform. We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear…
Open weights
mit
321.3B parameters
1,048,576 tokens
transformers
Join our WeChat or Discord community. Check out the GLM-5.3-Flash blog and GLM-5 Technical report. Use GLM-5.3-Flash API services on Z.ai API Platform. We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear…
Open weights
mit
321.3B parameters
1,048,576 tokens
transformers
Access requested at publisher
mit
550.9B parameters
exllamav3
Model · Image and text to text
Qwen
As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For…
Open weights
other
180B parameters
262,144 tokens
transformers
