Google Labs vs Hy4 preview: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Google Labs and Hy4 preview — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Google Labs
Google's hub for discovering, trying, and learning about experimental AI tools, demos, and research from Google.
Key features
- Experiment Gallery: A curated collection of interactive AI experiments and demos that let users try prototype features in web-based experiences.
- Discoverability and Updates: Centralized listings and short descriptions that surface new research, tools, and technology updates from across Google's AI teams.
- Developer Links and Repositories: Directs users to associated code, GitHub repositories, or developer resources so engineers and researchers can inspect, reproduce, or extend experiments.
- Responsible AI Context: Presents information and guidance related to responsible use, safety considerations, and ethical context for showcased experiments.
- Hands-on Interaction: Web-accessible demos designed to let non-experts and practitioners interact with models and view outputs without local setup.
- Aggregation Across Teams: Brings together experiments from multiple Google groups and initiatives, making it easier to explore cross-team innovation in one place.
- Web-hosted experimental demos and interactive prototypes for exploring new ML capabilities
- Central discoverability portal linking to technical demos, documentation, and GitHub repositories
- Hands-on labs and codelabs covering Google Cloud integrations (Vertex AI, Dataplex, Cloud Storage, GKE)
- Educational lab content including step-by-step instructions, sample data, and code artifacts
- Links to GitHub projects and third-party apps (e.g., google-labs-jules, google-labs-code) for deeper integration or code access
- Some labs include infrastructure-as-code examples (Terraform) and command-line instructions for reproducibility
- Emphasis on responsible AI guidance and up-to-date experimental catalog
Best for
- Exploring New Capabilities: Try interactive demos to evaluate emerging Google AI features before adoption or integration into projects.
- Research Prototyping: Researchers review experiments and linked code to reproduce results, benchmark approaches, or spark new research directions.
- Developer Onboarding: Engineers follow linked repositories and resources to access sample code, reproduce experiments, and build integrations or prototypes.
- Teaching and Demonstration: Educators use web demos as classroom examples to illustrate modern AI techniques or to spark discussion about responsible AI.
- Product Discovery and Feedback: Product teams and early adopters interact with prototypes to provide feedback, inform product direction, or assess feasibility.
- Staying Informed: Practitioners and enthusiasts monitor Labs to keep up with Google's latest experiments, releases, and responsible AI guidance.
- Rapidly previewing and evaluating research prototypes and ML demos in a browser
- Learning and hands-on training via codelabs that demonstrate Google Cloud integrations
- Prototyping integrations that use Vertex AI, Cloud Storage, Dataplex, or GKE
- Exploring sample code and repos on GitHub to bootstrap production implementations
- Educators and learners using step-by-step labs to teach cloud and ML concepts
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.