Hy4 preview vs TRAE SOLO: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and TRAE SOLO — 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.
T
TRAE SOLO
Trae / Trae-AI
SOLO is TRAE's autonomous coding mode that runs dedicated agent components (SOLO Code/Builder) inside the TRAE IDE to generate and modify code via natural language.
Key features
- SOLO Mode: An autonomous agent mode inside TRAE that runs dedicated components (SOLO Code, SOLO Coder, SOLO Builder) to generate, modify, and manage codebases via natural-language instructions.
- Downloadable Agent Components: SOLO exposes modular components (e.g., SOLO Code) that users can instantiate or download into their TRAE installation to enable isolated agent sessions.
- Natural-Language Coding: Accepts human prompts and system prompts (community or custom) to perform complex code generation, refactors, and multi-file changes across projects.
- Integration with TRAE Workflow: Works natively inside the TRAE IDE, leveraging TRAE memories, prompts, and existing workspace context to produce context-aware code edits and actions.
- Deployment & Tooling Hooks: Integrates with common developer tooling and deployment flows (users have reported Vercel workflow integrations and deployment-related operations) to automate end-to-end tasks.
- Subscription-Gated Access Control: SOLO features are accessed through TRAE's paid tier (TRAE PRO) and require users to enable/instantiate the SOLO modules within their account/environment.
- Community Prompts & Builders: Supports community-contributed prompts and a SOLO Builder concept for constructing system prompts or agent behaviors tailored to specific development tasks.
- Agent Session Management: Runs isolated sessions intended for single-agent workflows (Solo) to let the agent focus on a project or task without interfering with other IDE operations.
- Solo Mode (SOLO Code / SOLO Builder): autonomous single-agent coding workflows for scaffolding and building projects
- Natural-language code assistance integrated into the editor (conversational/code transform features)
- Integration with VS Code ecosystem (install hooks and extensions referenced for Trae) and a desktop Electron application
- Community prompts/memories system to store/share prompts and templates
- Companion agent repositories (trae-agent and related GitHub projects) for integrations and backend agent functionality
- Cross-component architecture: desktop app (Electron), VS Code extension hooks, and browser extension install points (Chrome / Edge button referenced)
- Project management and session persistence (issues indicate project/workspace handling, version/build metadata)
Best for
- Autonomous Feature Implementation: Provide a natural-language description of a new feature and have SOLO generate the code, update multiple files, and create tests across the repository.
- Large-Scale Refactoring: Instruct SOLO to refactor or modernize legacy code (rename symbols, update APIs, restructure modules) while leveraging workspace context and automated edits.
- Prompt-Driven Prototyping: Rapidly prototype components or microservices by describing desired behavior; SOLO generates runnable scaffolding and connects build/deploy steps.
- Automated Deployments & CI Tasks: Use SOLO to configure or trigger deployment flows (e.g., Vercel) and automation tasks from inside the TRAE IDE as part of a development-to-deploy workflow.
- Creating Custom Agent Workflows: Build and iterate custom SOLO Builder prompts and system prompts to tailor agent behavior for code reviews, security scans, or onboarding tasks.
- AI Pair-Programming Sessions: Run SOLO in an isolated session to act as a coding partner—implementing suggestions, generating alternative implementations, and producing test cases.
- Autonomous project scaffolding and builder workflows (generate a complete project or feature from prompts)
- Interactive natural-language code generation, refactoring, and completion within an IDE
- Creating and sharing community prompts, templates, and agent configurations (memories/agents)
- Embedding Trae capabilities into developer toolchains via VS Code integration or companion agent services
- Rapid prototyping and debugging with model-driven assistance and conversational context