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Tencent's Hy4 Preview Open-Weight Model Beats Qwen3.8-Max and DeepSeek-V4 Pro on DeepSWE Benchmark

Tencent's newly open-sourced Hy4 preview model jumped to 8th place on the Code Arena WebDev leaderboard, up from 34th for its predecessor, while outscoring Qwen3.8-Max and DeepSeek-V4 Pro on the DeepSWE benchmark.

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Editor's Note ·

Correction:
The article's "What We Don't Know" section states that neither Hugging Face nor SCMP's coverage discloses Hy3's parameter count or context window. In fact, the cited SCMP article reports Hy3 had 295 billion parameters and a 256,000-token context window, versus Hy4 preview's 770 billion parameters and 1-million-token context length — a jump SCMP itself describes as a 2.6-fold parameter increase and a quadrupled context window.

Overview

Tencent has open-sourced Hy4 preview, a new flagship Mixture-of-Experts large language model from its Hunyuan-affiliated Hy Team, according to the model’s Hugging Face card. On the Code Arena WebDev leaderboard, Hy4 preview reached eighth place globally, up from 34th for Tencent’s previous-generation Hy3, according to the South China Morning Post.

What We Know

  • Architecture. Hy4 preview is a Mixture-of-Experts model with 770 billion total parameters, of which 49 billion are activated per token, spread across 78 layers with 256 routed experts plus one shared expert per Mixture-of-Experts layer, according to Hugging Face. The model activates the top eight routed experts per token alongside the shared expert, and supports a context length of 1 million tokens with a vocabulary size of 120,832, per the same source. The card also describes a Gated DeepSeek Sparse Attention mechanism paired with a cross-layer index-reuse component and an “identity Hyper-Connections” residual pathway meant to expand information flow between layers, per Hugging Face.
  • Licensing and availability. Hy4 preview is released under the Apache License 2.0 and is available on Hugging Face, ModelScope, GitCode, and CNB, according to its GitHub repository.
  • Leaderboard jump. On the Code Arena WebDev leaderboard, Hy4 preview placed eighth globally, behind Anthropic’s Claude Fable 5 but ahead of Alibaba’s Qwen 3.8-Flash-Next in ninth, while Hy3 had ranked 34th on the same benchmark, according to SCMP.
  • DeepSWE benchmark. Hy4 preview scored 64.3 on the DeepSWE benchmark, ahead of Alibaba’s Qwen-3.8 Max at 56.6 and DeepSeek-V4 Pro at 62.7, according to SCMP.
  • Internal evaluation. Tencent says it ran a blind side-by-side evaluation in which 163 internal experts rated model outputs on 203 engineering tasks, with Hy4 preview scoring slightly ahead of GLM 5.3 (2.99 versus 2.92 average, a 46.8% win rate) and of Kimi K3 (2.99 versus 2.94, a 51.2% win rate), according to Hugging Face. The card says the model was co-designed with Tencent’s own CodeBuddy and WorkBuddy products so that model gains would translate into real usage.
  • Analyst take. Goldman Sachs analysts said Tencent’s approach of drawing on its own product ecosystem to help train the model is notable for agentic workloads: “We view this closed-loop approach as particularly relevant for productivity and coding workloads, where real-world task trajectories, user interactions and evaluation signals drive model differentiation in the agentic AI era,” analysts led by Goldman’s head of Asia internet research, Ronald Keung, said, according to SCMP.
  • What comes next. Tencent said a full commercial iteration of the Hy4 series would launch later in 2026, according to SCMP. The Hugging Face card frames Hy4 preview itself as an early release, saying: “As with Hy3 preview, we would rather ship early and hear what breaks — that’s what made Hy3 substantially better, and it’s how we will get Hy4 right.”

What We Don’t Know

Neither Hugging Face nor SCMP’s coverage discloses Hy3’s own parameter count or context window, so the scale of the jump in raw model size between generations is not confirmed by any source used here — only the leaderboard rank change (34th to 8th) is established. Pricing and a firm release date for the promised “full commercial iteration” of Hy4 have not been announced. Independent, third-party verification of the DeepSWE and internal blind-evaluation figures beyond Tencent’s own disclosures and SCMP’s reporting is not yet available.

Analysis

Hy4 preview’s release adds to a run of large open-weight model launches from Chinese labs this year, following Alibaba’s Qwen3.8-Max and DeepSeek’s V4. Tencent’s framing — leaning on internal products like CodeBuddy and WorkBuddy to generate real task data for training, as flagged by Goldman Sachs’ research team — points to product-ecosystem data as a differentiator among labs racing on similar open-weight release strategies, rather than parameter count alone.