Alibaba Qwen 3.5

Qwen3.5 397B A17B is a multimodal Mixture-of-Experts (MoE) large language model optimized for long-context reasoning, vision-language understanding, and scalable agentic workflows. It contains 397B total parameters with 17B active parameters, activating 10 routed experts plus 1 shared expert from 512 experts per token. The model employs a 60-layer hybrid architecture combining Gated DeltaNet and full attention layers, with 32 attention heads and a 4,096 hidden size. Supporting a 262K token context window, extendable to ~1M, it integrates a 27-layer vision encoder for unified multimodal processing.

Qwen3.5 122B A10B is a multimodal Mixture-of-Experts (MoE) large language model optimized for long-context reasoning, vision-language understanding, and scalable agentic workflows. It contains 122B total parameters with 10B active parameters, activating 8 routed experts plus 1 shared expert from 256 experts per token. The model employs a 48-layer hybrid architecture combining Gated DeltaNet and full attention layers, with 32 attention heads and a 3,072 hidden size. It supports a 262K token context window, extendable to ~1M, and integrates a 27-layer vision encoder for unified multimodal processing.

Qwen3.5 35B A3B is a multimodal Mixture-of-Experts (MoE) large language model designed for efficient long-context reasoning, multimodal understanding, and scalable deployment. The model contains 35B total parameters with ~3B active parameters, activating 8 routed experts plus 1 shared expert from 256 experts per token. It features a 40-layer hybrid architecture combining Gated DeltaNet and full attention layers, with 16 attention heads and a 2,048 hidden size. The model supports a 262K token context window, extendable to ~1M, and integrates a 27-layer vision encoder for unified multimodal processing.

Qwen3.5 27B is a large multimodal causal language model built with a 27B parameter architecture. It uses a 64-layer transformer with 24 attention heads, 4 KV heads, and a 5,120 hidden size, paired with a 17,408 intermediate dimension. The model supports a native 262K token context window, extendable to ~1M tokens, and integrates a 27-layer vision encoder with 1,152 hidden size. It combines Gated DeltaNet and attention layers, enabling efficient long-context reasoning, strong multimodal performance, and broad multilingual support across 200+ languages.

Qwen3.5 9B is a multimodal causal language model built with a 9B parameter architecture. It uses a 32-layer transformer with 16 attention heads, 4 KV heads, and a 4,096 hidden size, paired with a 12,288 intermediate dimension. The model supports a native 262K token context window, extendable to ~1M tokens, and includes a 27-layer vision encoder with 1,152 hidden size. It combines Gated DeltaNet and attention layers, delivering efficient long-context reasoning, strong multimodal performance, and broad multilingual coverage across 200+ languages.

Qwen3.5 4B is a multimodal causal language model built with a 4B parameter architecture. It uses a 32-layer transformer with 16 attention heads, 4 KV heads, and a 2,560 hidden size, paired with a 9,216 intermediate dimension. The model supports a native 262K token context window, extendable beyond 1M tokens, and integrates a 24-layer vision encoder with 1,024 hidden size. It combines Gated DeltaNet and attention layers, enabling efficient long-context reasoning, strong multimodal understanding, and broad multilingual capability across 200+ languages.

Qwen3.5 2B is a compact multimodal causal language model built with a 2B parameter architecture. It uses a 24-layer transformer with 8 attention heads, 2 KV heads, and a 2,048 hidden size, paired with a 6,144 intermediate dimension. The model supports up to a 262K token context window and integrates a vision encoder with 24 layers and 1,024 hidden size. It combines Gated DeltaNet and attention layers, enabling efficient long-context reasoning, multimodal understanding, and strong multilingual performance across 200+ languages.

Qwen3.5 0.8B is a compact multimodal language model combining a causal transformer with a vision encoder for efficient cross-modal reasoning. It features a 0.8B parameter architecture with 24 layers, 1024 hidden size, 8 attention heads (2 KV heads), and integrates Gated DeltaNet linear attention with periodic full attention layers. The model supports a 262K token context window and uses multi-token prediction (MTP). It is designed for efficient multimodal understanding, strong reasoning, and broad multilingual support across 200+ languages with low-latency inference.