Hy4 preview vs LMArena: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and LMArena — 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.
LMArena
LMArena
Open platform for crowdsourced benchmarking and live leaderboards that ranks chatbots and LLMs using user votes and automated evaluations.
Key features
- Crowdsourced Pairwise Voting: Users can interact with multiple chatbots and cast pairwise votes; aggregated human preferences are used to compute model win-rates and power the live leaderboard.
- Bradley–Terry Ranking Engine: Uses the Bradley–Terry statistical model to convert pairwise user votes into continuous rankings and win-rate metrics for robust comparison between models.
- Arena-Hard-Auto Evaluation Suite: Provides an automated benchmark (Arena-Hard-Auto) with curated hard prompts, style-control features, and the ability to use GPT-4.1/Gemini judges for pre-deployment model assessment.
- Public Datasets and Preference Collections: Hosts multiple datasets (e.g., search-arena-24k, arena-human-preference-140k) and preference data on Hugging Face for training, evaluation, and replication of leaderboard results.
- Hugging Face Spaces & Model Repos: Maintains interactive leaderboards and example apps as Hugging Face Spaces and publishes model and dataset repositories for community use and reproducibility.
- FastChat Integration for Serving: Commonly integrated with FastChat to serve and evaluate chatbots in live comparisons and crowdsourced matches, enabling scalable interactive evaluations.
- Open Tooling & Scripts: Provides open-source scripts and configuration (e.g., config YAMLs, result display scripts) to run evaluations, add style attributes, and compute win rates under different judge configurations.
- Crowdsourced pairwise voting system driving live leaderboards (Bradley-Terry ranking)
- Public leaderboard and web chat interface (lmarena.ai) to try and compare models
- Arena-Hard-Auto: automated evaluation toolkit and benchmark with configurable judges (supports GPT-4.1/Gemini as judges)
- Integration with FastChat for training, serving, and evaluating chatbots
- Hugging Face presence: publishes datasets, benchmark suites, models, and Spaces (leaderboard Space)
- Open datasets for benchmarking (e.g., search-arena-24k, arena-hard datasets)
- Support for custom model evaluation via config YAML (model_list) and Python tooling (show_result.py, add_markdown_info.py)
- Model formats and training artifacts compatible with PyTorch/transformers (AutoTokenizer usage, model repo examples)
- Support for multi-modal evaluation and specialized arenas (e.g., VisionArena)
- Plugins/compatibility with external APIs (OpenAI API for GPT judges) and community model repos
Best for
- Pre-deployment Model Evaluation: Run Arena-Hard-Auto to estimate how a candidate model will perform on LMArena-style human preference comparisons before public release.
- Live Comparative Benchmarking: Publish a chatbot endpoint and compare it against other models on the live LMArena leaderboard to measure relative win rates from real user votes.
- Research on Human Preferences: Use the arena-human-preference datasets to study preference patterns, fine-tune models on preference data, or reproduce published leaderboard outcomes.
- Automated Stress Testing: Evaluate robustness and style-control behavior of models using Arena-Hard-Auto’s hard prompts and judge ensembles (GPT-4.1/Gemini) to surface failure modes.
- Dataset-driven Fine-tuning: Leverage LMArena-hosted datasets (search-arena-24k, others) to fine-tune conversational models for better performance on human-preference metrics.
- Community Benchmarking & Transparency: Host community challenges and transparent leaderboards via Hugging Face Spaces and GitHub repos to encourage reproducible, open comparisons.
- Evaluate and compare chatbot/LLM performance with real user votes and automated judges
- Pre-deployment validation: run Arena-Hard-Auto to estimate likely performance on the public leaderboard
- Publish research models, datasets, and leaderboards for community benchmarking and reproducibility
- Build and serve chatbots using FastChat integration and measure user preference on LMArena
- Run automated, configurable evaluations using ensemble judges (GPT-4.1, Gemini, etc.)