Hy4 preview vs TheySaid 3.0: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and TheySaid 3.0 — 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.
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TheySaid 3.0
TheySaid
Turn single-question surveys into real-time conversational surveys to boost engagement and surface richer insights.
Key features
- Conversational Survey Conversion: Transforms single-question surveys into AI-driven multi-turn conversations that probe respondents with contextual follow-ups to gather richer qualitative data.
- Real-time Engagement: Dynamically adapts follow-up prompts based on answers to keep respondents engaged and reduce drop-off during the survey experience.
- Automated Insight Extraction: Aggregates and summarizes responses, surfaces recurring themes and sentiment, and highlights actionable findings for faster analysis.
- Intelligent Question Generation: Generates clarifying and targeted follow-up questions tailored to each respondent’s answers to obtain deeper context and reasons.
- Response Analytics Dashboard: Provides aggregated views, filters, and breakdowns (e.g., sentiment and themes) to help teams interpret results quickly.
- Export & Integration: Enables exporting response data and integrating survey outputs with downstream analytics or research workflows for further analysis.
- Conversational AI Surveys that adapt follow-up questions based on responses
- AI Interviews to automate in-depth user interviews
- AI Pulses and Polls for single-question feedback and follow-ups
- Question recommendation and auto-generation from website content
- Embedding and delivery via existing channels
- AI-driven summarization of responses and insights
- Convert single-question surveys into interactive, AI-driven conversations
- Real-time capture of conversational survey responses
- Smart conversational survey design to boost respondent engagement
- Automated analysis and extraction of insights from conversational responses
- Web-based survey creation and results dashboard
Best for
- Converting NPS/CSAT single-question prompts into conversational flows to collect reasons, suggestions, and context behind scores for better actionability.
- Market research that requires scalable qualitative feedback by turning short surveys into richer interviews to surface customer needs and motivations.
- Customer support and feedback collection that triages issues via guided conversation and captures exact user language and sentiment for product teams.
- Employee pulse and HR surveys that solicit candid explanations and suggestions while maintaining higher completion rates through conversational engagement.
- Product discovery and usability testing to gather in-depth user reactions, feature requests, and pain points from compact, conversational surveys.
- Collecting product and UX feedback via conversational surveys
- Automating user interviews to surface deeper insights
- Running NPS/CSAT/CES pulses with follow-up probing
- Embedding surveys across websites and apps to increase engagement
- Conducting user tests and polls with AI follow-ups to understand reasons
- Customer feedback collection with richer qualitative responses
- Market research using conversational probes to uncover insights
- Product feedback and user experience surveys
- NPS and satisfaction measurement with follow-up conversational context
- Employee engagement and pulse surveys