Hy4 preview vs OpenChatKit: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and OpenChatKit — 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.
OpenChatKit
Together Computer
OpenChatKit is an open-source kit providing instruction-tuned chat models, a moderation model, and an extensible retrieval system for custom, up-to-date chatbots.
Key features
- Instruction-Tuned Chat Models: Provides pretrained instruction-tuned language models (including a 20B-parameter GPT-NeoXT-derived chat model) optimized for conversational tasks and assistant-style behavior.
- Moderation Model: Ships a dedicated moderation model intended to filter or classify unsafe content, enabling safer deployments of chat applications.
- Extensible Retrieval System: Integrated retrieval components allow the chatbot to query external or custom repositories (documents, wikis, news) so responses can include up-to-date or domain-specific information.
- Pretrained Weights and Conversion Tools: Includes pretrained checkpoints and tooling/scripts to convert model weights (e.g., to Hugging Face formats) and prepare models for various inference stacks.
- Inference and Deployment Examples: Code and references for running high-performance inference using techniques such as DeepSpeed and multi-GPU deployments, with community examples for SageMaker and other platforms.
- Training and Fine-Tuning Pipelines: Provides training recipes, data organization, and scripts for continuing training or instruction-tuning on custom datasets (e.g., OIG-43M based training pipeline).
- Repository Tooling and Utilities: Contains utilities for dataset handling, evaluation, retrieval indexing, and developer-friendly examples to accelerate building and extending chat systems.
- Permissive Licensing and Community Development: Distributed under Apache 2.0 to encourage reuse, modification, and community contributions across research and production use cases.
- Instruction-tuned language models (including GPT-NeoXT-Chat-Base-20B and a 20B chat model variant)
- 6B-parameter moderation model for content filtering
- Extensible retrieval system for retrieval-augmented generation and inclusion of up-to-date or custom content
- Training and inference code and utilities (training/, inference/ directories) for fine-tuning and serving
- Tools and scripts for converting model weights to Hugging Face formats
- Support for DeepSpeed model-parallel techniques to deploy across multiple GPUs
- Examples and community-contributed integrations (e.g., SageMaker large model inference container example)
- Open-source license (Apache 2.0) with repository, docs, and data folders included
Best for
- Custom Knowledge Chatbots: Build a domain-specific assistant by indexing company docs, knowledge bases, or news feeds into the retrieval system so the model can answer questions with up-to-date, contextual information.
- On-Premise Self-Hosting: Deploy the provided pretrained models on private infrastructure using DeepSpeed or multi-GPU setups to run self-hosted conversational services without third-party APIs.
- Instruction Tuning and Fine-Tuning: Use the training pipelines and OIG-43M-based recipes to further fine-tune models for specialized tasks (customer support, medical triage, legal Q&A) with custom instruction datasets.
- Content Moderation Pipelines: Integrate the included moderation model to screen generated outputs and user inputs to reduce unsafe, biased, or disallowed content in production chat applications.
- Research and Reproducibility: Use the open code, data references, and conversion tools for research experiments, benchmark comparisons, or to reproduce large-model training and evaluation workflows.
- Cloud Deployment Examples: Follow community examples and guides to deploy OpenChatKit models on cloud platforms (e.g., AWS SageMaker) using model-parallel inference techniques for scalable serving.
- Model Conversion and Integration: Convert pretrained weights to popular formats (Hugging Face) and integrate models into existing ML stacks, chat frontends, or orchestration systems.
- Building general-purpose or specialized chatbots and conversational agents
- Retrieval-augmented assistants that combine custom knowledge sources (Wikipedia, news, corpora) with LLM responses
- Deploying large chat models across multiple GPUs or cloud inference containers (e.g., SageMaker with DeepSpeed)
- Research and development workflows: fine-tuning, evaluating, and converting models for downstream use
- Content moderation pipelines using the included moderation model