Hy4 preview vs Otto by Audos.com: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and Otto by Audos.com — 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.
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Otto by Audos.com
surajshetty3416 / Otto (Frappe app)
A Frappe application library that adds LLM capabilities (sessions, model management, queries) to Frappe apps.
Key features
- Frappe Integration: Implements LLM capabilities as a Frappe application backed by DocTypes so model sessions and metadata are stored and managed in the Frappe framework.
- Typed Library Interfaces: Exposes strongly typed modules (otto.lib.types and otto.llm.types) to build custom LLM features with clear type definitions and developer ergonomics.
- Session Management: Provides session-based interaction support (OttoSession) allowing multi-turn conversations and continued context across requests while warning against directly coupling to internal DocType internals.
- Quick One-off Queries: Offers utilities to perform single-shot operations such as document summarization or ad-hoc queries within a Frappe app.
- Model Discovery & Creation: Includes tooling to discover available models and create new model configurations from within the application environment.
- Otto Execution Workflows: Integrates with application-level execution flows so generated outputs and LLM interactions can be incorporated into business processes and custom features.
- Exposes core LLM functionality as a library for Frappe apps
- Session management (OttoSession backed by DocType)
- Model management and discovery
- Typed API definitions via otto.lib.types and otto.llm.types
- Examples for one-off queries, session-based interactions, tool usage, and model creation
- Integrated with Frappe DocTypes (not a standalone package)
- Used internally for Otto Execution and application-level features
- Repository documentation (README) with installation notes and usage examples
Best for
- Document Summarization: Use Otto to add a one-click document summarization feature inside a Frappe app to generate concise summaries from uploaded documents.
- Conversational Assistants: Build session-based chat assistants within ERP/CRM workflows that maintain context across interactions using OttoSession.
- In-App Model Selection: Allow administrators to discover, configure, and switch between available LLM models for different app features (e.g., billing, support, knowledge base).
- Workflow Automation: Embed LLM-driven execution steps into existing Frappe workflows to generate content, draft responses, or extract structured data from text.
- Custom LLM Features: Developers create bespoke LLM-powered capabilities (e.g., guided form-filling, smart search, or code generation helpers) using the typed otto.lib interfaces.
- Tool Integration: Combine Otto's LLM outputs with other Frappe DocTypes and business logic to automate tasks like ticket triage or knowledge base population.
- Add conversational or session-based LLM features to Frappe applications
- Build custom LLM-backed app features (summarization, generation, Q&A) inside Frappe
- Create and manage LLM sessions and models from within a Frappe app
- Instrument application-level execution flows that call LLMs (Otto Execution)
- Prototype tool-usage patterns and model discovery workflows in Frappe