Hy4 preview vs Google Vertex AI: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and Google Vertex AI — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Hy4 preview
Tencent
Tencent's open-weight Hy4 preview, a 770B-parameter Mixture-of-Experts model with 49B active parameters and a 1M-token context window.
Key features
- 770B Mixture-of-Experts Architecture: Holds 770 billion total parameters while activating only 49 billion per token, so capacity scales without proportional inference cost.
- 1M-Token Context Window: Accepts inputs exceeding one million tokens, allowing whole codebases, long document sets or extended agent traces in a single prompt.
- Apache 2.0 Open Weights: Released under a permissive licence that allows commercial use, modification and redistribution with no separate agreement.
- Productivity Task Focus: Tuned for real-world coding, office work and scientific research rather than narrow benchmark optimisation.
- Multi-Product Availability: Accessible globally through Tencent's WorkBuddy, CodeBuddy, Yuanbao and ima applications in addition to the raw weights.
- API Access via TokenHub and OpenRouter: Can be called through Tencent Cloud TokenHub or OpenRouter for teams that prefer hosted inference over self-hosting.
Best for
- Whole-Repository Code Work: Load an entire codebase into the million-token context to reason about refactors and cross-file dependencies at once.
- Long-Horizon Agent Tasks: Drive multi-step agent workflows where the full history of tool calls and intermediate results must stay in context.
- Self-Hosted Deployment: Run a frontier-scale open-weight model on private infrastructure where data cannot leave the organisation.
- Scientific Literature Analysis: Ingest large collections of papers or experimental logs and synthesise findings without chunking the input.
- Office Document Processing: Summarise, draft and restructure long reports, contracts and spreadsheets in enterprise workflows.
- Commercial Fine-Tuning: Adapt the weights for a proprietary product under the Apache 2.0 licence without negotiating a model licence.
Google Vertex AI
Enterprise-ready, fully-managed, unified AI development platform for building, deploying, and managing ML and generative models.
Key features
- Unified Development Studio: Vertex AI Studio provides a single web-based workbench for prototyping, training, evaluating, and deploying models, simplifying workflows across teams and reducing context switching.
- Model Garden & Foundation Models: Access to a curated set of first‑party, open‑source, and third‑party foundation models (160+ models, e.g., Gemini, Gemma, Llama 3, Claude 3) for text, vision, audio, and multimodal use cases, enabling rapid experimentation and fine-tuning.
- Managed Datasets and Feature Store: Fully managed dataset types (tabular, text, image, video) and an online Feature Store for consistent feature serving, metadata management, and integration with training and serving pipelines.
- Agent Builder and Conversational Tools: Tools to create multi-step agents and conversational applications that combine LLMs with tool integrations, function calling and orchestration for production assistants and workflows.
- Vertex Pipelines & MLOps: End-to-end CI/CD and workflow orchestration for ML with pipelines, model registry, deployment targets (online and batch), automatic scaling, and model monitoring for drift and performance.
- SDKs, Samples and Notebooks: Official Python SDK (googleapis/python-aiplatform), extensive sample repositories and starter notebooks (vertex-ai-samples) to accelerate development, reproducible experiments, and automation.
- Integrations with Google Data Services: Native integration with BigQuery, Cloud Storage, Data Labeling, and other Google Cloud services for data ingestion, labeling, large-scale training, and operational analytics.
- Managed Serving & Monitoring: Fully managed online and batch prediction endpoints with autoscaling, logging, monitoring, and tools for explainability and model performance tracking in production.
- Unified platform covering dataset management, training, evaluation, deployment, and monitoring
- Vertex AI Studio: web-based development and model management UI
- Agent Builder: tooling for building conversational agents and agent orchestration
- Access to 160+ foundation models and curated Model Garden (first-party, open-source, third-party)
- AutoML workflows for no-code/low-code model training alongside support for custom training code
- Managed datasets for tabular, text, image, and video data with import and labeling support
- Python SDK (google-cloud-aiplatform) with GAPIC-backed auto-generated client libraries
- Support for experiments, notebooks, and sample repositories for reproducible development
- Model serving and online/ batch prediction with feature store and online serving integration
- Infrastructure-as-code and provisioning support (e.g., Terraform modules for Vertex AI resources)
- Integration with Google Cloud services (GCS, BigQuery, IAM, monitoring) and CI/CD pipelines
Best for
- Productionizing ML Models: Train, validate, register and deploy supervised or custom models at scale using pipelines, model registry, autoscaled endpoints, and monitoring for continuous delivery.
- Building Generative Applications: Use foundation models from the Model Garden and Agent Builder to create chatbots, content generation systems, summarization services, and multimodal assistants.
- Enterprise Search and Knowledge Retrieval: Implement enterprise search solutions over website and internal data using Vertex AI Search and integrated generative models for retrieval-augmented generation.
- Computer Vision and Video ML: Create managed image and video datasets, run training and inference pipelines for object detection, classification, and video analysis with scalable serving.
- MLOps and Compliance Workflows: Implement reproducible pipelines, model monitoring, drift detection, explainability, and governance to meet enterprise compliance and operational requirements.
- Data Scientist Workbench and Experimentation: Use the SDK, sample notebooks, and model garden to iterate rapidly on experiments, fine-tune foundation models, and benchmark custom models with cloud compute.
- Build and deploy generative AI applications using foundation models hosted in Vertex AI
- Train AutoML or custom ML models on managed datasets for production prediction workloads
- Implement MLOps workflows: experiments, model registry, continuous training and deployment
- Create conversational agents and agent orchestration using Agent Builder
- Host and serve models with online feature serving via Feature Store for low-latency inference
- Integrate Vertex AI with infrastructure-as-code (Terraform) to provision reproducible environments
- Run notebooks and sample pipelines for prototyping, research, and production migration