Hy
Hy is a 770B-parameter open-source language model with a 1M-token context window, deployed via Tencent Cloud or OpenRouter APIs. It is trained on long-context tasks including multi-file code review, document synthesis, game prototyping, and scientific research coordination. Agencies integrate Hy into internal workflows by calling the API from custom scripts, IDEs, or productivity tools like CodeBuddy and WorkBuddy. The model accepts entire codebases, policy documents, or research datasets in a single prompt, eliminating manual context-switching and enabling engineers, analysts, and researchers to maintain reasoning state across complex, multi-turn tasks.
Hy is an AI code tool, integrating with Tencent Cloud, OpenRouter, CodeBuddy, and WorkBuddy. InnovaAI scores it 3.6/10 for agency adoption, best for Software Engineer, Project Manager, and Operations Manager roles handling 5+ client meetings per week.
Agency Audit
Hy is a 770B-parameter open-source LLM with a 1M-token context window designed for long-horizon software engineering, document-heavy analysis, and scientific research. Agencies building custom software, managing complex R&D projects, or processing multi-file client deliverables benefit most from adopting it internally. The model integrates via Tencent Cloud and OpenRouter, works with GitHub and HuggingFace, and pairs with Tencent's CodeBuddy and WorkBuddy for production workflows. Best ROI emerges for AI development agencies, software engineering shops, and research-focused teams where engineers and strategists spend 5+ hours weekly on long-context tasks.
5recommended
100/mo
No paid plan published
Moderate
Illustrative scenario. Not a guarantee. Net capacity needs a verified paid base plan, and none is published for this service, so it is not modeled. Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Software Engineer handling multi-file code review and debugging
- Project Manager handling document synthesis and financial analysis
- Operations Manager handling R&D experiment coordination and result aggregation
- Your agency primarily delivers client services that do not involve long-context code generation, multi-file document analysis, or R&D coordination; Hy's 1M-token window and training data are optimized for those workflows and offer no advantage over smaller, cheaper models for short-context tasks.
- Your team lacks in-house API integration expertise or DevOps capacity to manage Tencent Cloud or OpenRouter deployments; adoption friction will outweigh productivity gains for 6+ months.
- Your current LLM stack already includes models with comparable context windows and you have no documented bottleneck in long-context reasoning or multi-file synthesis.
Internal Adoption Path
No paid plan published
100 hr/mo
5 seats × 20 hr each
$7,500/mo
modeled at $75/hr labor rate
No paid plan published
Illustrative scenario. Not a guarantee. No verified paid base plan is published for this service, so subscription cost and net capacity are not modeled. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of Hy
1M-token context window for multi-file code review
Accepts entire codebases, design systems, or documentation sets in a single prompt without truncation. Enables software engineers to debug across files and strategists to synthesize research without manual chunking or context loss.
Long-horizon software engineering reasoning
Understands planning, debugging, and verification across multi-turn development tasks. Helps engineers maintain state across refactoring sessions and reduces time spent re-explaining project scope to the model.
Multi-file document synthesis and artifact generation
Converts scattered emails, invoices, policies, and spreadsheets into coherent reports, tables, and presentations. Saves operations and finance staff hours on manual data consolidation and formatting.
Parallel R&D experiment coordination
Tracks multiple concurrent research runs, adjusts methodology based on results, and organizes findings across sessions. Enables research teams to iterate faster without manual experiment logging or result aggregation.
Game prototype generation with engine integration
Produces playable 3D prototypes with menus, animations, and parameter exposure in a single prompt. Reduces iteration cycles for agencies prototyping game mechanics or interactive experiences.
CodeBuddy and WorkBuddy co-design integration
Model improvements surface directly in Tencent's productivity tools, ensuring gains in reasoning translate to faster output in IDE and document workflows without additional configuration.
What Makes Hy Different
Unique advantages vs similar tools in this niche
1M-token context window
vs Models with shorter context limitsHandles long-horizon tasks like multi-file document analysis and large codebases in a single pass.
Open-source availability
vs Proprietary modelsOpen-sourced on GitHub and HuggingFace, allowing customization and self-hosting.
Strong performance in software engineering
vs Other open-source modelsOutperforms GLM 5.3 and Kimi K3 in blind side-by-side evaluations on engineering tasks.
Latest Updates
Recent releases and improvements for Hy
Introducing Hy4 preview
New2026-08-28Release of Hy4 preview, a 770B-parameter model with 49B active parameters and a 1M-token context window, with major gains in software engineering, office work, and scientific research. Open-sourced and available on Tencent Cloud and OpenRouter.
Value Equation
Outcome-likelihood-time-effort assessment for Hy
Limited agency channel
Hy scored below the agency-resellability threshold (agency_fit_score < 50). The Value Equation projects agency-side outcomes, which don't apply to tools without a clear resell pathway.
Contact HyPricing
Hy platform cost to your agency
Pay as you go
- No monthly subscription required
- Pay only for what you use — see per-unit rates below
- Cancel anytime, no contract lock-in
How usage-based pricing works
Hy charges per consumption unit (per 1m cached input tokens). Below are the component rates the vendor publishes. Each row is a separate charge: your total cost combines them based on your configuration and volume. Component rates range from $0.042 per 1m cached input tokens.
Final agency cost = (sum of selected component rates) × client usage volume. Confirm a usage estimate with each client before quoting.
Component Rates
Cost per unit: total depends on your configuration and volume
Add-ons
Optional extras priced on top of any main plan
No verified white-label program for Hy: client-facing delivery runs under the platform's native branding.
Market Intelligence
Offer + scale economics for Hy
Limited agency channel
Hy scored below the agency-resellability threshold (agency_fit_score < 50). It's a useful tool but not designed for white-labeled or retainer-based reselling, so we don't publish productized offer economics for it.
Contact HyInvestment Decision Framework
Strategic vetting analysis for Hy
Situational Fit
Fit depends on your client mix
Buy If
5Your software engineers spend 6+ hours per week debugging or refactoring multi-file codebases and currently rely on manual context-switching or smaller models that lose detail across files.
Your project managers or operations staff process 10+ reimbursement claims, policy reviews, or financial analyses per week that require cross-referencing multiple documents and spreadsheets.
Your strategists or researchers coordinate multiple parallel R&D experiments and need a model that can track experiment results, adjust direction, and organize findings across sessions.
Your design or frontend team builds interactive prototypes and currently hand-codes or uses limited code-generation tools that struggle with visual polish and multi-screen state management.
Your team runs internal knowledge-synthesis workflows where raw client data, research notes, or technical specs must be converted into polished decks, reports, or shared documents weekly.
Skip If
5Your agency primarily delivers client services that do not involve long-context code generation, multi-file document analysis, or R&D coordination; Hy's 1M-token window and training data are optimized for those workflows and offer no advantage over smaller, cheaper models for short-context tasks.
Your team lacks in-house API integration expertise or DevOps capacity to manage Tencent Cloud or OpenRouter deployments; adoption friction will outweigh productivity gains for 6+ months.
Your current LLM stack already includes models with comparable context windows and you have no documented bottleneck in long-context reasoning or multi-file synthesis.
Your security or compliance posture requires on-premise model hosting; Hy is available only via Tencent Cloud or OpenRouter and does not ship as a self-hosted binary.
Your team size is under 3 seats and your long-context workload is episodic rather than daily; per-token costs will exceed the value of occasional context-window relief.
Bottom Line
Hy is a 770B-parameter open-source LLM with a 1M-token context window designed for long-horizon software engineering, document-heavy analysis, and scientific research. Agencies building custom software, managing complex R&D projects, or processing multi-file client deliverables benefit most from adopting it internally. The model integrates via Tencent Cloud and OpenRouter, works with GitHub and HuggingFace, and pairs with Tencent's CodeBuddy and WorkBuddy for production workflows. Best ROI emerges for AI development agencies, software engineering shops, and research-focused teams where engineers and strategists spend 5+ hours weekly on long-context tasks.
Reality Check
Hy requires API integration and team familiarity with prompt engineering for complex tasks; it is not a drop-in replacement for existing IDE or document tools. Payback depends on your team's baseline context-window constraints: agencies already using smaller models or manual document synthesis see faster ROI than those with mature internal LLM workflows.
Moderate effort: standard configuration with some customization needed
Academy for Hy
Work through it in order: the course for this service first, then the modules behind it.
No Academy modules are published for this service yet. Browse the full Academy
Why this category matters
The commercial case before the tooling.
Core concepts
The mental model you need to price and scope the work.
- Scaffold, Don't SubstituteConcept
Scaffold, Don't Substitute is a framework for agencies adopting AI code tools: use them to generate scaffolding and handle maintenance, but never as a replacement for human architectural oversight. The strategic insight from the category description warns that over-reliance risks code quality inconsistency and vendor lock-in. For example, an agency might use Verdent to rapidly prototype a full-stack app from a natural language brief, then have senior engineers review and refactor the generated code before delivery. Similarly, Ripple can auto-fix consumer code when APIs break, but a human must verify the changes align with client contracts. This framework helps agencies capture speed advantages while protecting quality and client trust. It also aligns with recent market data showing that AI agent loops can run 100x cheaper via simulation, but accuracy tradeoffs demand human judgment for high-stakes tasks.
- Human Checkpoint RatioConcept
The Human Checkpoint Ratio is the proportion of AI-generated code that passes through human review before delivery. Agencies adopting AI code tools often see speed gains, but unchecked automation can introduce subtle bugs and architectural drift. The framework holds that the optimal ratio depends on task risk: scaffolding and boilerplate can run nearly autonomous, while core business logic and client-facing features demand human sign-off. For example, HumanLayer structures workflows with six phases, each requiring human checkpoints, ensuring alignment and early error catching. Similarly, Ripple automates API break fixes but relies on developers to review generated pull requests. Agencies should define explicit checkpoints per task type, balancing speed with quality. A 100x cost reduction in simulation-based agents, as reported by Marktechpost, suggests that high-volume, low-stakes tasks can tolerate lower ratios, freeing human oversight for critical paths.
- Maintenance Over BuildConcept
AI code tools shift agency value from greenfield builds to ongoing maintenance. Platforms like Ripple auto-fix breaking API changes across repos, while Verdent generates full-stack apps from prompts, making initial builds cheap and commoditized. The durable margin lies in keeping client systems healthy: dependency updates, security patches, and refactors. Agencies that sell maintenance retainers, not just launch fees, convert a one-off project into recurring revenue. A 100x cost reduction in agent loops, as reported in simulation research, makes automated upkeep affordable at scale. The framework: use AI for scaffolding and repairs, but anchor the commercial model on continuous care, where human oversight prevents the quality drift that pure automation introduces.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- AI Code Tools Rule: Scaffold Fast, Architect SlowEvaluation Rule
Use AI code tools for scaffolding and maintenance tasks, but keep human architectural oversight for production decisions.
- AI Code Tools Rule: When Delivery Speed Is the Bottleneck, Automate Maintenance Before Greenfield BuildsEvaluation Rule
Use AI code tools for scaffolding and maintenance automation first, and reserve human architects for greenfield design and final review.
- The Scaffolding-Only Trap: Why AI Code Tools Stall in Agency DeliveryFailure Pattern
- The Unreviewed Merge Trap: Why AI Code Tools Fail in Agency DeliveryFailure Pattern
8 modules selected for Hy
Frequently Asked Questions
Answers about pricing, setup, alternatives
Hy is a 770B-parameter open-source LLM optimized for tasks requiring long context and complex reasoning. It generates code for multi-file software engineering projects, analyzes and synthesizes documents across many files, creates playable game prototypes, and coordinates scientific research experiments. The 1M-token context window allows it to ingest entire codebases, policy documents, or research datasets without truncation, making it suited for agencies handling long-horizon development, document-heavy analysis, or R&D workflows.
Hy uses consumption-based pricing with no per-seat license. Input tokens cost $0.834 per 1M tokens; cached input tokens cost $0.042 per 1M tokens; output tokens cost $2.501 per 1M tokens. Costs scale with usage volume, not headcount. A team of 5 engineers running 10 long-context code-review sessions per week typically incurs $200-400 monthly depending on context size and output length.
Software engineers save time on multi-file code review and debugging by loading entire projects into a single prompt. Project managers and operations staff accelerate document synthesis and financial analysis by processing reimbursement claims, policy reviews, and budget reconciliation across multiple files. Research strategists and R&D leads coordinate parallel experiments and organize findings without manual session logging. Frontend designers and game developers iterate faster on interactive prototypes and visual refinement.
Conservative estimate is 4-8 hours per seat per week for teams running 5+ long-context workflows daily. A software engineer debugging a multi-file codebase saves 2-3 hours per session by avoiding manual context-switching and re-explanation. An operations staffer processing 10+ reimbursement claims saves 3-4 hours by automating cross-file policy lookup and arithmetic verification. Savings compound if multiple roles adopt simultaneously; teams with episodic long-context work see lower returns.
Hy is available via API on Tencent Cloud and OpenRouter. There is no native web chat interface; adoption requires either engineering time to build an internal wrapper or use of OpenRouter's multi-model routing dashboard. Teams without API integration capacity should budget 1-2 weeks for initial setup and testing before rolling out to non-technical staff.
Data sent to Tencent Cloud or OpenRouter is processed according to their respective privacy policies. Tencent does not retain prompts or outputs for model retraining unless you explicitly opt in. OpenRouter similarly does not use your data for training by default. Confirm data retention and deletion policies with your chosen provider before adopting, especially if handling client-sensitive code or documents.
Hy is open-source and available on HuggingFace and ModelScope, but Tencent does not publish a self-hosted binary or containerized deployment guide. Agencies requiring on-premise hosting would need to build their own inference stack using vLLM or similar frameworks, which requires significant DevOps effort. Most teams adopt via Tencent Cloud or OpenRouter for simplicity.
Hy is optimized for software engineering, document synthesis, and scientific research based on training data from Tencent experts in those domains. It offers a 1M-token context window at lower per-token cost than Claude or GPT-4, making it cost-effective for agencies running frequent long-context sessions. Trade-off: Hy is newer and less battle-tested in production; teams should pilot on non-critical workflows before full rollout.