Cohere Command R+ vs Hy4 preview: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Cohere Command R+ and Hy4 preview — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Cohere Command R+
Cohere
104B-parameter LLM optimized for long-context conversational tasks, RAG, grounded generation, and multi-step tool use (open research release).
Key features
- Large-Scale Model: 104 billion parameter research checkpoint offering high-capacity reasoning and generation for complex tasks.
- Very Long Context: Supports extremely long context windows (~128K tokens / 131,072) to handle long documents, multi-turn conversations, and extended RAG inputs.
- Retrieval-Augmented Generation: Built-in support for RAG workflows — takes conversation plus retrieved document snippets and generates grounded, citation-aware responses.
- Grounded Generation Modes: Multiple answer modes including a "fast" citation mode that emits answers with grounding spans to reduce token usage while trading off some grounding accuracy.
- Single- and Multi-Step Tool Use: Native prompt templates and workflow support for both single-step tool calls and multi-step tool orchestration, enabling complex pipelines that combine multiple tools across steps.
- Multilingual Training and Evaluation: Trained on 23 languages and explicitly evaluated across 10 languages (e.g., English, French, Spanish, German, Portuguese, Japanese, Korean, Arabic, Chinese).
- Open Research Release & Integrations: Available as an open-weights research release on Hugging Face (c4ai-command-r-plus-08-2024) with hosted demo spaces and guidance for use with transformers and prompt templates.
- Large-parameter family (research release includes a 104B-parameter variant)
- Long context windows (documented up to 128K tokens / 131072 in some listings)
- Optimized for conversational templates and chat-style prompts
- Retrieval-Augmented Generation (RAG) with grounding spans and citation modes
- Single-step and multi-step tool use templates for orchestrating external tools/APIs
- Multilingual generation (trained on 23 languages; evaluated in 10 languages)
- Open-weights research release available on Hugging Face (Cohere Labs) with Transformer integration
- Hostable via Cohere hosted Chat API (reference: https://docs.cohere.com/reference/chat)
- Examples and integration guidance for use with FAISS, Hugging Face transformers (>=4.39.1), and local inference
Best for
- Document Q&A with RAG: Build systems that retrieve relevant document snippets (e.g., from a vector store) and produce citation-aware answers over long documents or corpora.
- Conversational Agents that Call Tools: Implement chat agents that call external APIs, databases, or tools in single-step or multi-step workflows to complete tasks like booking, data retrieval, or automation.
- Multi-step Automation Pipelines: Orchestrate sequences of tool invocations (e.g., search → extract → transform → submit) where the model plans and executes multiple steps to complete complex operations.
- Long-form Summarization and Analysis: Summarize, synthesize, or analyze very long documents, meeting transcripts, or multi-document corpora using the extended context window.
- Multilingual Support and Cross-Language Tasks: Provide question answering, summarization, or conversational support across numerous languages with evaluated performance in major languages.
- Research and Experimentation: Use the open-weights release to experiment with grounded generation techniques, citation modes, prompt templates, and custom tool-use strategies in research or prototype builds.
- Long-form document question-answering and summarization using RAG over large corpora
- Multi-step automation and agents that call and combine external tools/APIs
- Conversational assistants requiring grounded answers with source citations
- Document analysis pipelines (PDFs, knowledge bases) combined with semantic search like FAISS
- Multilingual customer support and knowledge retrieval across large contexts
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.