Hy4 preview vs Project Genie: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and Project Genie — 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.
Project Genie
Google (Google Labs)
An experimental Google Labs project exploring generative assistant prototypes and interactive AI demos.
Key features
- Web-based Interactive Demo: A browser-hosted interface for trying prototype assistant behaviors and workflows, enabling live interaction and rapid observation of model output.
- Prototype Assistant Flows: Demonstrates conversational and task-planning flows to explore new assistant patterns, task breakdowns, and multi-step interactions for user testing.
- Feedback & Telemetry: Built to collect user feedback and usage signals to inform research decisions, iterate on designs, and identify failure modes.
- Responsible Deployment Controls: Includes mechanisms and UI elements focused on safety, privacy notices, and moderation/guardrails to evaluate real-world impacts during experiments.
- Rapid Iteration Platform: Supports fast updates to prompts, UI components, and integration points so researchers and engineers can test variations quickly.
- Discovery hub for experimental AI projects and demos
- Centralized listing and descriptions of emerging Google AI tools
- Emphasis on responsible exploration and public access to prototypes
- Links/backing to individual experiment pages for demos and details
- Public-facing explanations and promotional content rather than technical API docs
Best for
- Design validation: Let product teams test conversational assistant patterns and UI interactions with real users before investing in production development.
- Research experiments: Collect qualitative and quantitative feedback on new generative behaviors, safety mitigations, and model responses for academic or internal research.
- Prototype demonstrations: Showcase possible assistant features to stakeholders or partners using an interactive web demo rather than static mockups.
- Usability testing: Evaluate how users understand and interact with multi-step task planners, clarifying prompts, and suggested actions in a controlled environment.
- Safety evaluation: Trial moderation, privacy notices, and fallback behaviors to observe failure modes and tune guardrails prior to broader rollout.
- Discover and try early-stage Google AI experiments
- Track new tools and research prototypes from Google
- Demonstrate capabilities of experimental models to users and stakeholders
- Provide a public feedback channel for prototype improvement