Hy4 Preview

Architectural Overview & Capability Profile

Hy4 Preview is developed by Tencent as an advanced foundation model engineered for production enterprise applications, reasoning workflows, and autonomous problem-solving. On the comprehensive RankLLMs composite evaluation index, Hy4 Preview registers an overall benchmark rating of 55.2 / 100.

Operating under a Open Weights license structure, Hy4 Preview is distributed as an open-weights architecture capable of local deployment, private VPC inference, and custom fine-tuning.

Benchmark Performance & Real-World Utility

In standardized laboratory and real-world evaluation suites, Hy4 Preview demonstrates calibrated proficiency across key performance vectors:

• SWE-bench Verified Software Engineering (77%): Measures multi-file GitHub issue resolution. The model demonstrates practical capability in localizing source code bugs, synthesizing functional patches, and passing rigorous integration test matrices. For evaluation methodology, consult our SWE-bench Pro guide.

• GPQA Diamond Scientific Reasoning (58.5%): Assesses graduate-level domain problem solving across physics, chemistry, and biology, minimizing hallucinations in multi-step deductive arguments. See our Humanity’s Last Exam benchmark analysis.

• MATH-500 Competition Mathematics (54.8%): Evaluates formal mathematical problem solving and symbolic derivation, providing foundational logic for algorithmic computing.

• Terminal-Bench Execution (85.4%): Reflects proficiency in interactive command-line environments, shell tool use, and automated DevOps scripting.

Inference Latency & Cost Architecture

• Token Context Window: 1.0M tokens, allowing large documents, multi-file codebases, and long conversation memories to be processed in a single context window.
• Inference Speed: Reference throughput clocks at approximately 60 tokens/second, enabling responsive streaming in developer tools and interactive interfaces.
• Commercial Pricing: Zero per-token inference fee when self-hosted on owned GPU infrastructure (e.g. vLLM or Ollama), with marginal costs limited strictly to hardware compute.

Strengths, Limitations & Ideal Workflows

Primary Strengths

• 770B MoE architecture activating 49B parameters per token with 1.0M context window
• Exceptional coding accuracy on DeepSWE (64.3) and SWE Atlas Refactoring (53.3)
• Strong agent tool calling on Toolathlon (74.1) and APEX-Agents (37.1)
• Permissive Apache 2.0 open-weight release

Known Considerations

• Requires distributed multi-node GPU infrastructure for full FP8/BF16 cluster serving

Recommended Use Cases

Open-weights enterprise agents, large-scale code refactoring, and 1M context document reasoning.

Related Comparisons & Leaderboard Context

• Compare with alternatives in the Model Comparison Engine
• Compare global rankings on the AI Model Leaderboard
• View complete platform index in our HTML Sitemap & Directory
• Explore top coding models in our Best LLM for Coding Guide