Dim Notes vs Hy4 preview: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Dim Notes and Hy4 preview — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Dim Notes
The Dim (https://www.thedim.app/)
A minimal digital sanctuary for quickly capturing fleeting thoughts and ideas so you don't lose the moment.
Key features
- Quick capture of thoughts and ideas (stated in messaging)
- Minimalist/distraction‑free experience (implied by "sanctuary for your mind")
- Preserve fleeting ideas to prevent loss of inspiration
- Web‑forward presence (Open Graph type: website)
- Simple, focused messaging aimed at personal idea capture
Best for
- Capture fleeting thoughts and ideas before they are forgotten
- Personal journaling and reflection
- Brainstorming and preserving creative ideas
- Lightweight productivity for idea collection and review
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.