Hy4 preview: In-Depth Evaluation of Tencent HunYuan's Open-Source Flagship Large Model

1. What is Hy4 preview

Hy4 preview is an open-source flagship large model launched by Tencent HunYuan, with a total parameter scale of 770B, 49B activated parameters, and a context length extended to 1M. The model was co-developed with experts from various fields within Tencent, including software engineering, gaming, finance, and security, and focuses on real-world productivity tasks such as code development, office analysis, game creation, and scientific research. It consistently ranks among the top open-source models in 203 blind tests of engineering tasks and has achieved significant breakthroughs in scientific scenarios such as molecular dynamics simulation, quantum device design, and century-old geometric problems.

Image source: Official article
Image source: official article

Technical positioning and domain: Hy4 preview belongs to the large-scale language model (LLM) domain and employs a MoE (Mixture of Experts) architecture. It is positioned as a general-purpose flagship model designed for real-world productivity scenarios. The model covers multiple vertical areas, including software engineering, office automation, game development, and scientific research, offering a differentiated approach from models that solely pursue high scores on public benchmark tests. It emphasizes usability and practical implementation in real engineering environments.

Development background: This model was developed by the Tencent HunYuan team, leveraging Tencent's long-term technical accumulation across business scenarios such as social media, gaming, finance, and cloud computing. During the development process, the team collaborated deeply with top experts from various fields within Tencent to build data systems and evaluation standards, ensuring that the model's capabilities align with actual business needs rather than just aiming for public rankings. This approach is highly distinctive within the open-source large model space.

Core value: Hy4 preview addresses the pain point of large models being "capable of conversation but difficult to implement" in real complex tasks, extending its capabilities to specific scenarios such as long-term code development, cross-file office analysis, and playable game prototype generation. At the same time, the model possesses autonomous reasoning optimization and experimental coordination capabilities, enabling its value to extend from the generation end to infrastructure operations and R&D process management, forming a comprehensive technical loop across the entire pipeline.

Technical features: The combination of 770B total parameters and 49B activated parameters in the MoE architecture maintains high inference efficiency while ensuring model capacity. The 1M context window supports the processing of ultra-long documents and large codebases. When paired with the Hyra agent, the model achieves a 2.0x speedup in molecular dynamics simulations, reduces the leakage rate in quantum device design from 48.2% to 4.8%, and advances the volume lower bound of the classic geometric problem, 3D Blaschke-Lebesgue, to 0.41104.

2. Key Features

• Software Engineering Capabilities: Significantly enhanced understanding, planning, debugging, and verification abilities for long-term development tasks. It can handle frontend development for large projects and proactively optimize visual aesthetics and interaction quality. The generated code can be directly executed and iteratively improved in real engineering environments, applicable to the entire workflow of requirement analysis, architecture design, and code maintenance for medium to large-scale software projects.

• Office Analysis Capabilities: Through high-quality data co-construction with financial domain experts, it significantly improves the understanding of complex office environments, supporting cross-file collaboration and multi-dimensional data analysis. It can complete the entire office workflow from raw information processing to document, spreadsheet, and presentation delivery. For example, it can automatically review 24 expense reports and cross-reference policy documents, reducing repetitive clerical work.

• Game Development Capabilities: Possesses the ability to generate a playable prototype with a single sentence, and can proficiently use mainstream game engines such as Unreal 5 and Unity. Developers can continuously refine complex game projects through multiple rounds of natural language interaction, covering the complete development pipeline from scene setup, gameplay logic, character animation blend trees, and audio systems, significantly reducing the time cost for game prototype validation.

• Scientific Research Capabilities: Demonstrates deep understanding, reasoning, and problem-solving abilities in scientific research scenarios. When paired with the Hyra agent, it achieves a 2.0x speedup in machine learning force field molecular dynamics simulations, reduces the leakage rate in low-temperature quantum transport device design from 48.2% to 4.8%, and makes substantial progress on a classical geometric problem that has remained unsolved for over a century. This showcases the model's comprehensive support for scientific research, from theoretical reasoning to experimental validation.

• Inference Optimization Capabilities: Can autonomously analyze performance bottlenecks in inference systems and conduct multi-round iterative optimizations around areas such as operator fusion and communication optimization. The end-to-end service throughput improves by 31.8% compared to the baseline. It consistently delivers stable performance gains across different context lengths and concurrency levels, verifying the model's ability to autonomously identify bottlenecks and continuously optimize infrastructure, providing efficiency guarantees for large-scale deployment.

• Experimental Coordination Capabilities: Manages multiple Codex Sessions in parallel like a researcher, and outperforms Codex's independent exploration in all 8 evaluations for small model post-training tasks. This capability reflects the model's comprehensive competence in judging directions, organizing experiments, and continuous iteration in complex R&D tasks, making it suitable for R&D scenarios requiring parallel exploration of multiple technical solutions.

3. How to Use

1. Open Source Download: Obtain model weights and code via HuggingFace, GitHub, ModelScope, or GitCode, supporting local deployment and secondary development. It is recommended to use an inference framework compatible with the MoE architecture, and to plan GPU memory and storage resources in advance based on the total parameter scale of 770B. For long-context scenarios, it is advisable to use an efficient KV Cache management strategy.

2. API Calling: Integrate with Tencent Cloud TokenHub or OpenRouter to directly call the model's API. The pricing is 6 yuan per million input tokens and 18 yuan per million output tokens. This method is suitable for business scenarios sensitive to deployment costs or requiring rapid integration, allowing full model capabilities without the need to build your own inference infrastructure.

3. Product Experience: Directly experience the office and programming capabilities of Hy4 preview within Tencent products such as WorkBuddy, CodeBuddy, Yuanbao, and ima. This method requires no technical configuration and is ideal for business teams to evaluate the model's real-world performance and verify its compatibility with existing tools.

4. Online Demo: Visit the online experience URL https://aistudio.tencent.com/ to interact with the model without any environment setup. This is suitable for quickly verifying model capabilities, conducting technical research, or performing comparative testing, and serves as the lowest barrier entry to understanding the model's performance.

5. Best Practices: When deploying locally, it is recommended to adjust the context length and concurrency parameters based on the actual task type. For tasks involving long document processing, attention should be paid to token consumption costs. In API calling scenarios, it is advisable to optimize calling efficiency through caching and batch processing strategies to reduce cost pressure from frequent interactions.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

Scenario Selection Recommendations: If the team's primary goal is to implement real productivity scenarios, especially requiring deep integration with office automation, game development, and research acceleration capabilities, Hy4 preview's expert-co-creation model and its strong product synergy with Tencent's ecosystem make it particularly suitable for business scenarios within the Tencent ecosystem and teams requiring ultra-long context processing capabilities. If the budget is limited and the focus is on general-purpose reasoning and code generation, DeepSeek-V4 Pro's API pricing is more competitive, making it ideal for large-scale calls and cost-sensitive projects.

Technical Selection Recommendations: For teams needing to handle large codebases or ultra-long documents with nearly 1M context length, the context window advantage of Hy4 preview is significant. For teams that already have their own inference infrastructure and mainly rely on third-party integration and self-deployment, DeepSeek-V4 Pro's open-sourced weights provide a more flexible solution. Research teams working on areas such as molecular dynamics or quantum device design can consider the Hy4 preview combined with the Hyra agent, as this solution has demonstrated quantified acceleration effects and can be a preferred technical route.

6. Editor's Summary

The release of Hy4 preview reflects Tencent Hun Yuan's clear strategic choice in its large model technology roadmap: rather than focusing solely on public benchmark scores, the model is aligned with real-world productivity scenarios through deep collaboration with internal business experts. Its technical foundation in the open-source model space includes a 770B total parameter and 49B active parameter Mixture-of-Experts (MoE) architecture, a 1M context window, and autonomous inference optimization capabilities. Additionally, specialized enhancements for four major scenarios—software engineering, office analysis, game development, and scientific research—equip the model with practical capabilities to transition from "general conversation" to "engineering implementation."

In terms of practical value, Hy4 preview's performance of 2.99/4.00 on average across 203 blind engineering tasks, along with quantifiable breakthroughs in research areas such as molecular dynamics simulation, quantum device design, and mathematical proof when paired with the Hyra agent, demonstrates that its capabilities extend beyond mere demonstrations. A 31.8% increase in inference throughput and enhanced experimental coordination capabilities further extend the model's value from the generation side to infrastructure operations and R&D process management, forming a relatively complete technical loop.

In terms of target users, this model is suitable for developers and business teams within the Tencent ecosystem, technical teams requiring ultra-long context processing capabilities, and researchers involved in computationally intensive scientific scenarios. For individual developers with limited budgets, the relatively high API pricing may present a certain barrier, but the open-source weights and local deployment options offer viable alternatives. In the future, as technical reports are refined and the community ecosystem expands, Hy4 preview's competitiveness within the first tier of open-source models is expected to be further solidified.

7. Application Scenarios

• Intelligent Code Development: Supports long-range project understanding, frontend visual aesthetics optimization, and debugging verification, and can be used in conjunction with CodeBuddy to complete the full development process from requirement analysis to executable code. Ideal for iterative maintenance of medium to large-scale software projects, effectively improving development efficiency and code quality.

• Complex Office Automation: Handles cross-file data analysis, financial audits, and policy comparisons, automatically generating documents, tables, and presentations. Already implemented in office products such as WorkBuddy, significantly reducing repetitive clerical tasks and enhancing process efficiency in industries like finance and auditing.

• Rapid Game Prototype Development: A single natural language requirement can generate a playable prototype, with proficient use of the Unity / Unreal 5 engines, supporting multi-round interactions to continuously refine levels, characters, and gameplay mechanics. Suitable for game teams to quickly validate core gameplay concepts during project initiation, reducing time and labor costs in prototype development.

• Accelerating Scientific Research: Collaborates with the Hyra agent to conduct molecular dynamics simulations, quantum device design, and mathematical theorem proving, autonomously managing experimental workflows and continuously iterating and optimizing research directions. Has achieved measurable efficiency improvements across multiple research areas, making it suitable for computationally intensive research teams.

• 3D Digital Content Generation: Generates interactive Three.js websites and digital twin scenarios based on precise parameters, supporting GLB model loading, procedural modeling, and multilingual responsive deployment. Applicable for virtual scene construction, digital twin application development, and interactive visualization projects.

8. FAQ

Q: What is the context length of Hy4 preview? What tasks is it suitable for?
A: The context length reaches 1M, placing it at the forefront among open-source models. It is suitable for tasks requiring long-range dependency modeling, such as analysis of large codebases, processing of ultra-long documents, and cross-file office data correlation. It can effectively handle ultra-long input scenarios that traditional models struggle to cover.

Q: How can I obtain the model weights of Hy4 preview?
A: You can access the model weights and code via HuggingFace, GitHub, ModelScope, or GitCode, supporting local deployment and secondary development. Specific download links are provided in the Project Links section at the end of this article. When deploying, hardware resources must be planned according to the total parameter size of 770B.

Q: What is the pricing for API calls? How can I integrate it?
A: The pricing is 6 yuan per million input tokens and 18 yuan per million output tokens. Integration can be done through Tencent Cloud TokenHub or OpenRouter. Compared to DeepSeek-V4 Pro's pricing, Hy4 preview has higher costs in scenarios involving long contexts and complex tasks. It is recommended to evaluate the ROI based on specific business needs.

Q: How to choose between Hy4 preview and DeepSeek-V4 Pro?
A: Hy4 preview excels in 1M context length, expert collaboration in real-world productivity scenarios, and product synergy within the Tencent ecosystem. DeepSeek-V4 Pro, on the other hand, offers lower API pricing and better general-purpose inference performance. Prioritize Hy4 preview when budget allows and deep scenario customization is required, while DeepSeek-V4 Pro is more suitable for cost-sensitive projects.

Q: How can the Hyra agent be used in research scenarios?
A: The Hyra agent manages the experimental process and iteration direction, while Hy4 preview provides the capability to understand and solve complex problems. In molecular dynamics simulations, it achieves a 2.0x speedup, and in quantum device design, it reduces the leakage rate from 48.2% to 4.8%. It is ideal for research teams requiring autonomous management of multi-round experiments.

Q: Does the model support optimization for Chinese scenarios?
A: As a model developed by the Tencent Hunyuan team, Hy4 preview has been optimized through expert collaboration in Chinese office, financial analysis, and code development scenarios. Chinese language capability is one of its core development directions. Specific Chinese evaluation data will be further disclosed in the official technical report.

9. Project Links

1. Product Official Website: https://hy.tencent.com/research/hy4-preview
2. GitHub Repository: https://github.com/Tencent-Hunyuan/Hy4-preview
3. HuggingFace Model Library: https://huggingface.co/tencent/Hy4-preview