On September 14, 2026, SiliconFlow announced through its official account that Hy4 preview had launched on its platform. This model was developed by Tencent's Hunyuan team, with a total of about 770 billion parameters, about 49 billion activated per token, supporting contexts up to 1 million tokens, and is open under the Apache 2.0 license. It should be noted that Hy4 preview was not released on that day; A new point in this news is that third-party inference platforms provide a direct access point, making it easier for developers who do not plan to deploy large model weights themselves to trial and evaluate.
What changes the new API entry points
The 770 billion total parameter model demands high standards for video memory, parallel scheduling, and inference operations. The self-built environment requires not only multi-card resources but also weight loading, service scaling, and concurrency isolation. Platform hosting converts these tasks into API calls, allowing teams to first verify code generation, complex analysis, and long documentation with a small number of real tasks before deciding whether to invest in self-built resources. However, SiliconFlow's announcement did not simultaneously provide price, limits, and performance commitments covering all scenarios; before migration, it should still refer to the real-time model list and service terms in the console.
Highlights of the Hy4 preview architecture
- Sparse Activation: The model has 78 layers, 256 routing experts, and 1 shared expert, with 8 routing experts selected each time, aiming to balance large parameter capacity with single-run computational load.
- Long Context: The official model annotates the context of 1 million tokens and uses mechanisms such as Gated DSA and IndexCache to reduce the burden of long sequence processing.
- Open Licensing: Apache 2.0 is beneficial for research, secondary development, and enterprise evaluation, but actual use still requires checking the complete license documents and applicable regulations that come with the model warehouse.
Which teams are most worth trying first?
Teams that need to understand large codebases, retrieve multiple materials, generate research reports, or build complex agent workflows can include them in the candidate pool. During trials, do not just submit short prompts; cover real long contextual inputs, tool calls, structured output, and continuous multi-turn tasks. 1 million tokens are usable contextual configurations and do not automatically guarantee the model will accurately find key information at any length, nor does it mean all inputs have the same attention quality.
Four checks before access
- Confirm the platform's current model identification, regional availability, concurrency limits, and output limits.
- Use desensitized samples to test data retention, log permissions, and enterprise compliance requirements.
- It also records first token delay, full response time, failure rate, and the cost of each successful task.
- Prepare a similar model as a control to evaluate answer accuracy and manual review time, rather than just comparing nominal parameters.
For most teams, the biggest significance of this launch is lowering the threshold for "verify before procurement." It does not eliminate the cost and governance issues of large models, but instead adds a lighter managed testing path for Hy4 preview, moving from a self-deploying option that requires strong infrastructure capabilities.
