Google Mixboard vs Hy4 preview: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Google Mixboard and Hy4 preview — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Google Mixboard
An experimental, AI-powered concept board for generating, exploring, and refining visual ideas and mood boards.
Key features
- Generative Mood Boards: Transforms natural-language prompts into visual concepts and mood boards, producing imagery, color suggestions, and layout ideas to kickstart design exploration.
- Idea Expansion: Automatically suggests variations and related concepts from initial inputs so users can broaden directions and discover unexpected design permutations.
- Iterative Refinement: Supports repeated prompting and modification to refine visuals and compositions, enabling a rapid feedback loop between intention and generated output.
- Visual Organization Canvas: Provides a flexible board-style workspace to arrange, compare, and juxtapose generated assets for clearer visual decision-making.
- Natural-Language Controls: Lets users guide generation and edits through conversational or prompt-based instructions, lowering the barrier for non-technical creators.
- Experimentation Focus: As a Google Labs experiment, Mixboard emphasizes rapid creative iteration and exploratory workflows rather than polished production tooling.
- Interactive concepting board interface for arranging and visualizing ideas
- Generative assistance to expand and iterate on concepts
- Tools to refine and structure ideas during ideation
- Visual organization for capturing variations and connections between concepts
Best for
- Brand Ideation: Quickly generate and iterate on visual directions—color palettes, imagery, and tone—for early-stage brand or campaign concepts.
- Mood-Board Creation: Assemble dynamic mood boards from text prompts to communicate aesthetic directions to teams or clients during pitches and reviews.
- Creative Brainstorming: Use AI-suggested variations to expand limited concepts into multiple distinct visual directions during team ideation sessions.
- Social Content Planning: Prototype visual themes and layouts for social media posts and short-form visual campaigns to test styles before production.
- Storyboarding and Concept Art: Produce rapid visual thumbnails and concept sketches to map out scenes, moods, and visual continuity during pre-production.
- Creative brainstorming and ideation sessions
- Product concept development and iteration
- Design and UX concept exploration
- Marketing concepting and campaign planning
- Collaborative team workshops for idea refinement
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.