cto.new vs Hy4 preview: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of cto.new and Hy4 preview — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
cto.new
Unknown (cto.new)
A cto.new landing/shortcut page that links to a Product Hunt listing for CTO-focused resources.
Key features
- Provides a web landing URL at cto.new/product-hunt that links to a Product Hunt listing
- Acts as a short/vanity URL for CTO-focused resources or product listing
- Simple static landing behavior (redirect or informational page) based on available content
- No public API or integration details present in the provided content
- No SDKs, plugins, or platform-specific installers documented
Best for
- Sharing a concise link to a Product Hunt listing or CTO-focused product
- Providing a landing page for CTO resources or a curated product announcement
- Marketing/promotional link distribution for CTO-targeted content
- Bookmark or quick-access URL for CTO community resources
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.