Tencent's Hy3 Beats Flagship AI Models Without a Trillion Parameters

Tencent just shipped Hy3, the full release of its 295B Mixture-of-Experts language model, and it is making a pointed argument: you do not need a trillion-parameter model to get flagship-level results. The model is open-weight under Apache 2.0, available on Hugging Face right now, and free to call via API on OpenRouter for the next two weeks.

From preview to production

Hy3 is a 295B-parameter MoE model with 21B active parameters and a 3.8B MTP layer. Following the Hy3 Preview launch in late April, the team gathered feedback from 50+ product teams, fixed various issues in task execution and interaction, and improved both the quality and scale of the post-training pipeline. The full Hy3 release is the result of that iteration cycle.

Building on the Preview, Tencent improved post-training data quality and diversity while scaling up RL training. Hy3 shows solid gains across reasoning, agentic workflows, and long-context tasks, with performance close to leading flagship models both domestic and international.

The architecture doing the heavy lifting

MoE (Mixture-of-Experts) means the model has 295B total parameters but only activates a small fraction per token. Top-8 out of 192 experts are activated, routing routine queries to fast pattern-matching experts and complex problems to deeper reasoning chains. This is how Hy3 keeps compute costs low while maintaining quality.

Hy3 breaks the assumption that all experts cost the same compute when active. Some experts are wider and handle general token routing across domains; others are narrower and specialize in reasoning-dense subproblems like multi-step math or tool call sequencing. The router learns to send tokens to appropriately sized experts rather than uniformly sized ones, so 21B active parameters produce output quality normally associated with 40-70B active parameter models.