Claude 4 vs Hy4 preview: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Claude 4 and Hy4 preview — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Claude 4
Anthropic
Claude 4 is Anthropic's next-generation family of large models delivering more reliable, interpretable assistance for complex work, learning, and coding.
Key features
- Interpretable Outputs: Produces explanations and stepwise reasoning to make model decisions more transparent and easier to audit for correctness and safety.
- Improved Reliability: Enhanced instruction-following and reduced hallucinations compared to prior generations, designed for complex multi-step tasks across domains.
- Model Family Variants: Offered as multiple specialized variants (e.g., Sonnet for agentic and general tasks, Opus for coding) enabling selection of models optimized for coding, agents, or general assistance.
- Developer Platform Integration: First-class support on the Claude Developer Platform with API access, quickstarts, and SDKs to embed Claude models into apps, agents, and workflows.
- Large Context and Multi-Stage Reasoning: Engineered to handle extended context and interleaved/thinking-style prompting patterns to manage longer documents and multi-step reasoning processes.
- Agent & Tooling Support: Designed to work with agent frameworks, tool integrations, and products like Claude Code to interact with codebases, execute tasks, and manage git workflows via natural language.
- High‑capability natural language reasoning and multi‑step task completion
- Improved interpretability and reliability for critical workflows
- Accessible via the Claude Developer Platform and Claude API with API key access
- Integrates with developer tooling: Claude Code CLI (npm package), quickstarts, SDKs and cookbooks
- Support for agentic coding workflows, git automation, and codebase understanding (Claude Code)
- Used in Anthropic apps (mobile iOS app) and third‑party integrations (e.g., GitHub Copilot support)
- Examples, recipes, and reference implementations available in public repositories (claude-quickstarts, claude-cookbooks)
Best for
- Long-form research synthesis: Analyze and summarize large document sets, extracting insights, sources, and stepwise justifications for informed decision-making.
- Developer assistance and code generation: Review, debug, and generate complex code across languages using Opus-optimized variants and Claude Code integrations to operate on repositories.
- Agentic automation: Power multi-step agents that call tools, manage context windows, and delegate subagents for specialized subtasks in customer support or data workflows.
- Enterprise knowledge workflows: Integrate Claude into internal tools to index, query, and reason over company documents, policies, and project artifacts with interpretable outputs.
- Educational tutoring and learning: Provide step-by-step explanations, problem solving, and personalized learning assistance across subjects with reliable reasoning traces.
- Document analysis and synthesis: Extract structured data, generate executive summaries, and produce action items from lengthy reports, contracts, or meeting transcripts.
- Developer tooling: code generation, debugging, and automated git workflows via Claude Code
- Knowledge work: research summarization, document analysis, and project organization
- Agentic applications: building autonomous assistants and task automation agents
- Customer support: automated responses, triage, and assisted agent workflows
- Content workflows: document parsing (PDFs), moderation filters, and prompt/evaluation automation
- Mobile productivity: on‑device assistant features and visual analysis in apps
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.