deepseek vs Hy4 preview: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of deepseek and Hy4 preview — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
deepseek
DeepSeek
Open-source family of large language and multimodal models (DeepSeek-V3, R1, VL, Coder) focused on efficient MoE scaling and RL-driven reasoning.
Key features
- Mixture-of-Experts Architecture: Uses MoE designs (DeepSeekMoE) with Multi-head Latent Attention (MLA) to activate a subset of parameters per token, enabling very large total parameter counts while controlling inference cost and memory.
- Massive Pretraining: V3 was pretrained on a reported 14.8 trillion diverse tokens with a multi-token prediction objective, providing strong general-language capabilities before downstream tuning.
- Reinforcement-Learning Driven Reasoning: DeepSeek-R1 and R1-Zero investigate reinforcement learning (including RL without supervised warm-up) to elicit emergent chain-of-thought, self-verification, reflection, and long-form reasoning behaviors.
- Multimodal Understanding (DeepSeek-VL): A vision-language model designed for real-world multimodal inputs, able to process logical diagrams, web pages, formulas, scientific literature, natural images and embodied scenarios.
- Code and Long-Context Specialization: DeepSeek-Coder-V2 extends code support to hundreds of programming languages, increases context windows (examples up to 128K), and optimizes for code generation and math reasoning tasks.
- Open Releases and Reproducibility: Models, weights, and research artifacts are published on GitHub and Hugging Face; community reproductions and distillations (e.g., open-r1 reproduction) exist to validate reported benchmarks.
- MoE architectures (DeepSeekMoE) supporting high total parameter counts with smaller activated parameters per token (e.g., V3: 671B total, 37B activated)
- Multi-head Latent Attention (MLA) for efficient inference
- Auxiliary-loss-free load-balancing strategy and multi-token prediction training objective
- Reinforcement learning-centric training (DeepSeek-R1 and R1-Zero) enabling long chain-of-thought, reflection, and self-verification behaviors
- Vision-Language model (DeepSeek-VL) for multimodal understanding: diagrams, webpages, formulas, scientific literature, natural images
- Code-specialized models (DeepSeek-Coder-V2) with expanded language support (86→338 languages) and extended context up to 128K tokens
- Public model checkpoints and downloads (Hugging Face repositories and GitHub), with Transformer docs available for integration
- Cross-platform desktop client (DeepSeek Desktop) providing native UI, localStorage and cookie support
- Published resource/compute metrics (e.g., V3 pretraining on ~14.8T tokens, ~2.664M H800 GPU hours for pretraining)
Best for
- Research Benchmarking: Evaluate new RL techniques and MoE scaling strategies by reproducing and extending DeepSeek training regimes and reported results on math and reasoning benchmarks.
- High-Performance Text Generation: Deploy DeepSeek-V3 variants for large-scale text generation tasks that benefit from strong pretraining and efficient MoE inference.
- Advanced Reasoning Tasks: Use DeepSeek-R1 models for complex chain-of-thought problems, multi-step math, code reasoning, and tasks benefiting from self-verification/reflection capabilities.
- Multimodal Document Understanding: Apply DeepSeek-VL to analyze and extract structured information from diagrams, formulas, web page screenshots, and scientific PDFs.
- Code Generation and Review: Use DeepSeek-Coder-V2 for generating, completing, and reasoning about code across hundreds of languages and very long context windows (large codebases, multi-file contexts).
- Open-Source Model Integration: Integrate publicly released DeepSeek checkpoints into custom pipelines, fine-tune for domain-specific tasks, or run community distillations for lighter-weight deployments.
- Long-form reasoning and chain-of-thought problem solving in math, code, and reasoning benchmarks
- Code generation, completion, and analysis across hundreds of programming languages with large context windows
- Multimodal understanding tasks: document parsing (web pages, diagrams, formulas), scientific literature comprehension, and natural image interpretation
- Research and fine-tuning workflows using downloadable checkpoints (Hugging Face / GitHub)
- Desktop-based interactions via DeepSeek Desktop for local, native access to models and assistant features
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.