Tencent Hy3 Preview: 295B Open-Source MoE Developer Guide 2026

Tencent just open-sourced a 295-billion-parameter model that went from cold start to production in under three months — and it posted a 74.4% score on SWE-bench Verified, the highest any previous Tencent model has ever achieved. Hy3 Preview, released on April 23, 2026, is the first output of a complete rebuild of Tencent’s Hunyuan pretraining and reinforcement learning infrastructure. The team is led by Yao Shunyu, a former OpenAI researcher who joined Tencent’s AI division in early 2026.

The key numbers: 295B total parameters with only 21B active (Mixture-of-Experts architecture), a 256,000-token context window, and a hybrid fast-slow thinking design. On SWE-bench Verified, it scores 74.4% — a 40% relative improvement over Hy2’s 53%. It drives agentic workflows of up to 495 steps. And it was already deployed inside WeChat, QQ, and Yuanbao before the public announcement landed.

This guide covers the architecture, every major benchmark, how to run it locally with vLLM or SGLang, how to call it via the Tencent Cloud API, and what it means for developers choosing between open models and frontier API subscriptions in mid-2026.

The Rebuild Story

Hy3 Preview is not a fine-tune of a previous Hunyuan checkpoint. It is the first product of a ground-up reconstruction of Tencent’s AI stack, started in late January 2026 and completed in roughly 84 days. That timeline — under three months from cold start to open-source public release — is unusual for a model at this scale.

The compression was possible partly because Tencent deployed Hy3 inside production products before announcing it publicly. Yuanbao (Tencent’s AI assistant), CodeBuddy (their developer copilot), WorkBuddy, and Tencent Docs all ran Hy3 in live production traffic before the weights were posted to Hugging Face. Real user traffic at Tencent’s scale surfaces failure modes that synthetic benchmarks and internal red-teaming miss. By the time Hy3 hit Hugging Face, it had already been debugged at WeChat-scale concurrency.

Yao Shunyu’s background at OpenAI focused on agentic evaluation and interactivity. That background shaped Hy3’s design priorities directly: rather than optimizing purely for single-question benchmark scores, the team treated real-world multi-step agent task completion as a first-class target. That decision shows up in the results.

Architecture: What Makes 295B Act Like 21B

Mixture-of-Experts

Hy3 Preview uses a Mixture-of-Experts (MoE) architecture. Each forward pass activates only 21B out of 295B total parameters — roughly 7%. The remaining parameters belong to specialized expert layers that are selectively routed to based on the input token. From a deployment perspective, the active memory and compute footprint resembles a 21B dense model, not a 295B dense model.

In practice: a 21B dense model in BF16 requires approximately 42 GB of VRAM. With 4-bit quantization (AWQ or GPTQ), Hy3’s effective footprint drops to roughly 148 GB — which fits on two H100 80GB GPUs with room remaining for KV cache. Running a 295B-class model on two datacenter GPUs was not realistic six months ago.

Hybrid Fast-Slow Thinking

Hy3 Preview supports two inference modes, selected at runtime via a system prompt parameter:

• Non-Thinking mode: Fast, direct responses. Behaves like a standard instruction-following model. Appropriate for retrieval, summarization, classification, and tasks where speed matters more than reasoning depth.
• Thinking mode: Extended chain-of-thought reasoning, similar to o1-style models. The model generates an internal reasoning trace before producing its final answer. Recommended for math, coding, and complex agentic tasks where correctness outweighs latency.

Both modes share identical weights. The thinking budget can be capped numerically, giving you cost control without disabling the mode entirely. A higher budget allows the model to spend more tokens on internal reasoning before committing to an answer — up to 32,768 tokens for the most demanding tasks.

Context Window

Hy3 Preview supports up to 256,000 tokens. This is not the 1M-token context that DeepSeek V4-Pro ships with, but 256K covers the vast majority of real-world agentic use cases: a full medium-sized codebase, a 200-page PDF, or dozens of rounds of a multi-tool agent conversation all fit comfortably. According to Tencent’s internal RULER benchmark testing, the model maintains consistent performance without degradation across the full 256K window — a bar that many models with larger advertised context windows fail to meet cleanly in practice.