Hy4 Official Release: A Deep Technical Review of Tencent’s 770B-Parameter Flagship Model

Tencent recently released the official version of Hy4, its latest large language model. The company claims the model has “joined the ranks of the strongest open-source models” for the first time. Yet third-party evaluation results paint a different picture: its composite intelligence index trails a competing flagship model, and its hallucination rate is labeled “high.” This gap between official claims and independent assessment has made the largest model in Tencent’s Hunyuan lineup a topic of heated discussion.

As a developer who has long followed the deployment of large models, I applied for API access as soon as it became available and ran a series of in-depth tests. This article examines the official Hy4 release from four angles: technical parameters, real-world performance, cost comparison, and practical boundaries. The goal is to help developers determine whether this update represents a genuine leap forward or is driven primarily by marketing.

1. Parameter Deep Dive: What a 770B-Parameter “Giant” Actually Delivers

The most obvious upgrade in the official Hy4 release is model size. Built on a Mixture of Experts (MoE) architecture, the total parameter count reaches 770B, roughly 2.6 times the size of the previous Hy3 series, which had fewer than 300B parameters. However, one key distinction matters: 770B is the total parameter count, while only 49B parameters are activated during each inference pass. This design has become the industry norm, using sparse activation to lower inference costs while preserving the knowledge capacity of a much larger model.

Compared with the preview version, the official release adds capabilities in two main areas:

• Visual understanding: The model has evolved from text-only to multimodal. It can interpret images, charts, and even hand-drawn sketches. This means developers can now describe image content in natural language or ask the model to generate code based on a screenshot.
• Computer Use: The model can interpret command-line and graphical interface information, enabling automated execution of simple cross-application tasks. For example, based on user instructions, it can open files, extract data, and generate reports. This provides a foundation for agent-based applications.

In addition, the 1-million-token context window (per API documentation) makes long-document parsing and large code repository analysis feasible. From a technical evolution perspective, the official Hy4 release is clearly attempting to move from a “conversational assistant” toward a “multimodal agent,” a shift that aligns with the current mainstream direction of large model competition.

2. Benchmark Disputes: Why Official Scores and Third-Party Results Diverge

Tencent highlights that Hy4 achieves state-of-the-art results on multiple benchmarks. However, third-party leaderboards show its composite intelligence index falling 11 points behind a competing flagship model, with a notably high hallucination rate. Such discrepancies are not uncommon in the large model space. They usually reflect differences in test sets and evaluation priorities:

• Official tests tend to focus on areas where the model performs well, and test data may overlap with training data, leading to inflated scores.
• Third-party evaluations place greater emphasis on open-ended question answering, logical reasoning, and factual consistency, imposing stricter requirements on generalization.

Looking at hallucination specifically, Hy4 performs reliably in code generation and document summarization, but it does tend to produce confident yet incorrect answers when handling open-domain commonsense questions. This means that if your application demands high factual accuracy, such as medical consultation or legal Q&A, you should implement a secondary verification step for model outputs.

Objective assessment: Hy4’s reasoning capability is in the first tier, but there is still room for improvement in the rigor of knowledge-based tasks. Developers should weigh the nature of their tasks when choosing a model. High-frequency, closed-ended tasks like information extraction and format conversion carry manageable risk. Low-frequency, open-ended question answering should be paired with retrieval-augmented generation (RAG) as a safeguard.

3. Hands-On Test One: Generating a 3D Game from a Single Prompt

To evaluate Hy4’s multimodal generation and instruction-following ability, I designed a demanding task: “Use a single sentence to generate a low-poly cartoon-style 3D Gold Miner game, including a mine shaft, miner, winch, hook, and other elements.”

Test setup: Hy4 official release, with reasoning intensity set to “high,” compared against a flagship model of the same class labeled Model X. Both models used default parameters, with no additional examples provided.

Results:

• Hy4: Generated complete, runnable code. The game scene contained all required elements, and the low-poly style was well executed. More importantly, the model automatically added physics collision detection and simple AI logic, such as automatic miner movement. This demonstrates an understanding of the underlying engineering problems implied by the prompt.
• Model X: Also produced runnable code, but the scene lacked interactive logic for the winch and hook. The code structure was also more chaotic and would require manual refactoring.

Comment: Hy4 performed impressively in breaking down a complex instruction and implementing it in code. It is particularly notable that the model interpreted “game” as “an application requiring interactive logic” rather than merely outputting a static HTML page. For rapid game prototyping and interactive story generation, Hy4 offers strong practical value.

4. Hands-On Test Two: Converting a Hand-Drawn Sketch to a Web Page

The second test focused on visual understanding, which is the most significant addition from the preview to the official release. I drew a website wireframe by hand, including a top navigation bar, a left sidebar, and a right content card area. I added an annotation: “Implement with flex layout; color theme should be dark tech style.”

Process: Hy4 correctly identified the intended layout and even understood the correspondence among the three labeled blocks in the sketch. The generated HTML/CSS code matched the wireframe with over 90% accuracy. It automatically applied a dark theme and made reasonable use of modern design elements such as rounded corners and shadows.

Comparison with similar capabilities: Compared with mainstream multimodal models, Hy4 shows strong tolerance for imprecise sketches. Even with curved lines and distorted proportions, it still accurately extracted key layout information. This likely stems from reinforced training on irregular visual inputs during its development.

Limitations: When a sketch contains more complex nested structures or overlapping elements, Hy4 occasionally misses some modules. Developers should keep sketch elements relatively independent where possible, or use textual notes to clarify key nodes.

5. Cost and Deployment: The Ecosystem Strategy Behind Competitive Pricing

Pricing is one of the strongest competitive advantages of the official Hy4 release. The API is priced at 6 CNY per million input tokens, 18 CNY per million output tokens, and 0.3 CNY per million tokens for cache hits. This is significantly lower than competing models of comparable scale.

For context, a flagship model in the same tier charges approximately 70 CNY per million output tokens, nearly four times Hy4’s price. For high-frequency use cases such as batch processing or AI customer support, this difference translates directly into meaningful cost savings. Tencent’s intent is clear: attract small and mid-sized developers through aggressive pricing and capture traffic at the agent application layer.

Supporting ecosystem: Regular users can select the Hy4 model directly in the graphical interface of tools such as WorkBuddy or CodeBuddy, with free daily quotas available during promotional periods. For developers, API access is straightforward and compatible with mainstream SDKs, enabling rapid integration into existing projects.

6. Target Users and Selection Guidance

Based on the tests and cost analysis above, the official Hy4 release is best suited to the following three groups:

1. Productivity tool developers: Those who need to build text processing or code generation applications quickly and cheaply, are cost-sensitive, and can tolerate a certain hallucination rate.
2. Multimodal application explorers: Those who need image understanding and diagram parsing capabilities but want to avoid the high costs of integrating multiple cloud services.
3. Teams rapidly validating agent prototypes: With its Computer Use capability, teams can test agent interaction logic at low investment.

Conversely, if your application involves highly sensitive domains such as finance or healthcare, or requires strong logical reasoning such as mathematical proof, it is advisable to wait for future iterations or add supplementary verification mechanisms.

7. Outlook and Conclusion

The official Hy4 release represents a milestone upgrade for Tencent’s Hunyuan family. Despite occasional weaknesses in third-party evaluations, its overall performance in code generation, visual understanding, and cost control gives it the potential to become a default option in many developers’ toolkits. As the open-source community continues to refine MoE architectures and agent frameworks mature, there is reason to expect Hy4 to narrow the gap with top closed-source models in factual accuracy in its next iteration.

Advice for developers: Do not fixate on leaderboard scores. Instead, integrate Hy4 into a real business scenario and run a two-week limited-traffic test. Focus on three metrics: instruction-following rate, invalid output rate, and context utilization efficiency. These indicators reflect model productivity far better than any single benchmark score.

There is no absolute “best” in large model selection, only the “best fit.” With its accessible pricing, Hy4 demonstrates that strong performance and low cost are not mutually exclusive. That may be its greatest contribution to the industry.