AI ToolAI Code Tools

Hy

Hy is a 770B-parameter open-source language model with a 1M-token context window, deployed via Tencent Cloud or OpenRouter APIs.

Hy is an AI code tool, integrating with Tencent Cloud, OpenRouter, CodeBuddy and WorkBuddy. InnovaAI rates it 3.6 of 10 for agency adoption, best for Software Engineer, Project Manager and Operations Manager roles.

Situational Fit3.6/10

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.

Situational FitNo WLUsage Based
Seats

5recommended

Est. Hours Saved

100/mo

Net Capacity

No paid plan published

Friction

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.

Situational Fit
Fit36
Visit Hy
Best For Your Team
  • 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
Not Ideal If
  • 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

Team Subscription

No paid plan published

Time Saved Monthly

100 hr/mo

5 seats × 20 hr each

Value of Reclaimed Time

$7,500/mo

modeled at $75/hr labor rate

Net Capacity

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 limits

Handles long-horizon tasks like multi-file document analysis and large codebases in a single pass.

Open-source availability

vs Proprietary models

Open-sourced on GitHub and HuggingFace, allowing customization and self-hosting.

Strong performance in software engineering

vs Other open-source models

Outperforms 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

New2026-08-28

Release 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 Hy

Pricing

Hy platform cost to your agency

Pay as you go

Custom
  • 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

Per 1M cached input tokens
$0.042/ 1M cached input tokens
Per 1M input tokens
$0.834/ 1M input tokens

Add-ons

Optional extras priced on top of any main plan

Add-on: 1M output tokens
$2.50

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 Hy

Investment Decision Framework

Strategic vetting analysis for Hy

Vetting Verdict

Situational Fit

Fit depends on your client mix

Agency Fit(white-label + resell pathway)
36/100
0255075100
Resell Friction(WL + mode + complexity)
85/100
0255075100

Buy If

5
OPERATIONAL FIT

Your 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.

OPERATIONAL FIT

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.

OPERATIONAL FIT

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.

OPERATIONAL FIT

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.

OPERATIONAL FIT

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

5
CAUTION

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.

CAUTION

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.

CAUTION

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.

CAUTION

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.

CAUTION

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

Trade-offs & Gotchas

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.

Implementation Reality

Moderate effort: standard configuration with some customization needed

Effort: 4/10Time: 4/10

Academy for Hy

Work through it in order: the course for this service first, then the modules behind it.

Course for this service

Hy Agency Implementation, Long-Context AI for Code & Document Delivery

Learn to architect Hy into client workflows for multi-file code review, document synthesis, and R&D coordination. This course teaches agencies how to structure prompts around Hy's 1M-token context window, build retainer pricing models around cached tokens, and deliver faster turnaround on complex engineering and operations projects.

Open the course

Core concepts

The mental model you need to price and scope the work.

  1. Scaffold Versus Steward SplitConcept

    AI code tools compress the cost of producing a first working version, but they do not compress the cost of owning it. The Scaffold Versus Steward Split separates two jobs agencies have historically billed as one: scaffolding (generating a full-stack app, wiring auth and payments, standing up a prototype) and stewardship (architecture decisions, review, and keeping consumer code in sync when contracts change). Scaffolding is where the speed advantage lives. Stewardship is where retainer value and liability live. Verdent illustrates the first half, turning a plain-English description into a working product with authentication, payments, and an admin dashboard. Ripple illustrates the second, detecting a breaking API change and opening pull requests across every affected repository. Agencies that price both halves at the same rate give away the margin on scaffolding and undercharge for stewardship. Split the two in your statements of work, and staff them differently.

  2. Agent Output Liability LineConcept

    The Agent Output Liability Line is the boundary between code an agency can ship under its own name and code that still needs a human signature. Agentic tools now generate full-stack applications from prompts and auto-fix breaking API changes across repositories, which moves the liability question from 'did we write it' to 'who reviewed it.' Agencies that blur this line absorb client risk without pricing it. A practical test: if an agent opens a pull request at 2am and no human reads the diff before merge, that code sits above the line and belongs in a maintenance retainer, not a fixed-scope build. Tools like Agent Monitor and Agent Desk exist specifically to make that review boundary visible by surfacing per-file risk levels and session histories. The framework matters because delivery speed without a defined liability line converts margin into unpaid warranty work.

  3. Verification Debt CompoundingConcept

    Verification Debt Compounding treats every unreviewed agent-generated commit as a small loan against future delivery capacity. The interest rate is set by how far the code drifts from the architecture a human would have chosen. Agencies that ship agent output straight to client repos accumulate this debt quietly, then pay it back in emergency debugging during a retainer sprint. The category description warns that over-reliance risks code quality inconsistency, and the operational evidence supports it: Agent Desk launched as an open-source macOS app specifically so teams can browse and review sessions across Claude Code, Cursor, Codex, and GitHub Copilot, which only matters if review is a scheduled step rather than an afterthought. A 10 MB in-browser model from GenClass now flags silent CSS and race-condition bugs that never throw errors, the exact class of defect that passes agent self-tests and lands in production. Budget review time as a line item, not a contingency.

13 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.