InclusionAI Ling 3.0

Ling 3.0 Flash Sante is a healthcare-specialized Mixture-of-Experts model built on Ling 3.0 Flash for medical reasoning, healthcare research, and tool-assisted workflows. It has 124B total parameters with 5.1B active, arranged in a 42-layer backbone with 2,560 hidden size, 32 attention heads, and 32 KV heads. Five Kimi Delta Attention layers alternate with one gated MLA layer, and each MoE layer routes tokens to 8 of 512 experts plus a shared expert. Supporting a 256K-token context, it runs in thinking mode by default with a Multi-Token Prediction layer.

Ling 3.0 Flash VL is a native multimodal Mixture-of-Experts model designed for visual reasoning, agentic workflows, and real-world task execution across images and videos. It features 124B total parameters with 5.5B activated per token, using a 42-layer hybrid backbone with 2,560 hidden size, 32 attention heads, and 32 KV heads. The architecture combines KDA and Gated MLA layers in a 5:1 ratio, with 512 experts activating 8 per token alongside shared experts. Its ViT encoder and two-layer projector integrate visual features, supporting up to 256K-token context for multimodal understanding, reasoning, and action.

Ling 3.0 Flash Fin is a finance-enhanced Mixture-of-Experts model designed for financial research, valuation, spreadsheet workflows, source-grounded analysis, and long-horizon agentic tasks. It features 124B total parameters with 5.1B activated per token, using a 42-layer architecture with 2,560 hidden size, 32 attention heads, and 32 KV heads. The architecture employs a hybrid KDA and attention design, 512 routed experts with 8 activated per token, and 1 shared expert, alongside a native Multi-Token Prediction layer. Supporting a 256K-token context window, it is optimized for multi-document financial reasoning, tool-intensive workflows, and professional research outputs.

Ling 3.0 Flash is a 124B-parameter native hybrid-reasoning Mixture-of-Experts model with only 5.1B active parameters, optimized for efficient long-context reasoning and production deployment. Its 42-layer architecture combines 35 Kimi Delta Attention (KDA) layers with 7 gated MLA layers in a 5:1 pattern, alongside 512 routed experts with 8 activated per token and 1 shared expert. Designed for complex agentic workflows, it incorporates training across 10,000+ interactive environments for coding, general, and deep-research agents, with support for context lengths up to 256K.

Ling 3.0 Tiny is a lightweight hybrid-linear Mixture-of-Experts model designed for efficient reasoning, coding, and agentic workflows on local and resource-constrained hardware. It features 7.9B total parameters with 1.3B activated, using a 24-layer architecture with a 1,536 hidden size and 16 attention heads. The model combines Kimi Delta Attention (KDA) and Multi-Head Latent Attention (MLA) in a 3:1 ratio, with 128 routed experts and 1 shared expert, activating 8 routed experts per token. Supporting a 131K-token context window, it is optimized for low-cost deployment while providing hybrid reasoning and efficient long-context processing for local and edge environments.