Comet vs Hy4 preview: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Comet and Hy4 preview — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Comet

Freemium

End-to-end model evaluation platform for AI developers, offering LLM evaluation, experiment tracking, and production monitoring.

End-to-End Model Evaluation: Provides a unified workflow to evaluate models from research to production, aggregating metrics, test datasets, and evaluation artifacts to make comparisons and audits straightforward.
LLM Evaluation Suite: Offers specialized evaluation tooling and metrics tailored for large language models, enabling targeted tests, generation scoring, and quality assessments across LLM variants and prompts.
Experiment Tracking: Records runs with hyperparameters, datasets, code versions, metrics, and artifacts so experiments are reproducible and searchable across teams.
Production Monitoring: Continuously monitors deployed models for performance drift, regressions, and anomalous behavior, enabling alerts and rapid rollback or retraining decisions.
Comparative Visualizations: Visual dashboards and side-by-side comparisons to identify best-performing experiments, track trends over time, and surface regressions between model versions.
Collaboration and Reporting: Centralized repository of experiments and evaluation results to share findings, generate reports, and align stakeholders on model readiness and risks.
End-to-end model evaluation across development and production
Best-in-class LLM evaluation capabilities
Experiment tracking for runs, parameters, and results
Production monitoring for model performance and regressions
Benchmarking and comparison of model versions
Centralized metrics, logging, and dashboards for models

Benchmarking LLM Variants: Run systematic evaluations of multiple LLM checkpoints and prompt strategies to identify the best-performing model for a production use case.
Reproducible Experimentation: Track hyperparameters, datasets, code commits, and outputs to reproduce research runs and validate results across team members or CI pipelines.
Production Performance Monitoring: Detect model drift or sudden drops in key metrics in production and trigger alerts or automated mitigation workflows.
Regression Detection Before Release: Compare candidate model versions against a production baseline using recorded evaluations and visual diffs to prevent degradation.
Compliance and Audit Reporting: Maintain a searchable history of evaluations, datasets, and model artifacts to satisfy auditing, documentation, or regulatory requirements.
Cross-Team Collaboration: Share evaluation dashboards and experiment histories between data scientists, ML engineers, and product teams to accelerate model iteration and decision-making.
Comparing LLM and other model variants using standardized evaluations
Tracking experiments, hyperparameters, and results during model development
Monitoring deployed models to detect performance degradation and data drift
Benchmarking models and producing reproducible evaluation reports
Operationalizing model evaluation workflows for teams

Tencent

Free

Tencent's open-weight Hy4 preview, a 770B-parameter Mixture-of-Experts model with 49B active parameters and a 1M-token context window.

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.

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.