Hy4 preview vs TensorFlow: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and TensorFlow — 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.
TensorFlow
End-to-end open-source machine learning platform with a flexible ecosystem of tools, libraries, and deployment options for research and production.
Key features
- High-Level APIs: Provides Keras as an integrated high-level API for fast model prototyping, training, evaluation, and transfer learning with simple, composable building blocks.
- Low-Level Control: Exposes tensor operations and dataflow graph primitives for fine-grained custom model construction, custom gradients, and advanced research experiments.
- Distributed Training and Hardware Support: Supports multi-GPU and multi-node training, native TPU support, and strategies for data- and model-parallel training to scale large models.
- Deployment Tooling: Offers production deployment options including TensorFlow Serving for scalable model serving, TensorFlow Lite for mobile and embedded devices, and TensorFlow.js for browser and Node.js inference.
- Data Pipeline and Input APIs: tf.data and related utilities enable efficient, repeatable, parallelized data ingestion, preprocessing, and augmentation for large datasets.
- Visualization and Debugging: TensorBoard provides interactive visualizations for metrics, model graphs, profiling, and debugging to optimize training and performance.
- Model Optimization: Includes tooling for quantization, pruning, and model conversion to reduce size and latency for edge deployment and faster inference.
- Cross-language and Ecosystem Support: Official bindings and related projects (Python, C++, JavaScript, Java) plus extensive community libraries, prebuilt models, and tutorials across domains.
- Core low-level API for tensor operations and numerical computation using dataflow graphs
- High-level APIs including tf.keras and Layers for rapid model building
- tf.estimator abstractions for model training and deployment
- TensorBoard visualization toolkit for metrics, graphs and profiling
- Support for CPU and GPU acceleration and distributed training across machines
- TensorFlow.js for running and training models in the browser and Node.js
- Converters and tooling to import/export models and interoperate across runtimes
- tf.data and data pipeline APIs for efficient data loading and preprocessing
- Large ecosystem: official models, examples, tutorials, and community-contributed libraries (e.g., TensorFlow Probability, TensorFlowOnSpark)
Best for
- Research Prototyping: Rapidly design and iterate on novel neural architectures using eager execution and low-level ops, then scale experiments with distributed strategies.
- Large-scale Model Training: Train large deep learning models on multi-GPU or TPU clusters and use distributed training strategies to shorten time-to-train for production models.
- Production Model Serving: Serve real-time or batch inference in production using TensorFlow Serving integrated with monitoring and autoscaling infrastructures.
- Mobile and Edge Deployment: Convert and optimize trained models (quantization/pruning) for deployment on mobile and embedded devices with TensorFlow Lite to achieve low-latency inference.
- Browser and Node.js Inference: Run models client-side in web browsers or server-side in Node.js using TensorFlow.js to enable interactive ML experiences without server roundtrips.
- Data Pipeline Automation: Build end-to-end ML pipelines with tf.data and related ecosystem tools to preprocess, cache, and stream large datasets efficiently during training.
- Model Compression and Optimization: Apply model optimization techniques to reduce model size and latency for resource-constrained environments while maintaining accuracy.
- Training and evaluating deep learning models for computer vision, NLP, and speech
- Deploying ML models in production backends and servers with CPU/GPU acceleration
- Running and training models in the browser or Node.js via TensorFlow.js
- Distributed training and scaling of large models across clusters (e.g., integration with Spark)
- Experimentation and research using low-level ops or high-level Keras APIs with visualization via TensorBoard
- Educational tutorials, examples, and rapid prototyping of ML workflows