Jin 2.0: The Next Generation of Multimodal AI

Two years ago, Jin unified our AI capabilities into a single multimodal architecture. Today we are releasing Jin 2.0, a major upgrade that expands capabilities, improves performance, and unlocks new applications.

What's New

Jin 2.0 represents a fundamental advancement across every dimension:

Expanded Context

Capability	Jin 1.0	Jin 2.0
Context window	8K tokens	128K tokens
Image resolution	512x512	4096x4096
Audio duration	30 seconds	10 minutes
Video support	None	5 minutes

Long-context enables new applications: entire product catalogs in context, full conversation history, complete document analysis.

New Modalities

Jin 2.0 processes:

• Text: Enhanced reasoning and generation
• Images: Higher resolution, better understanding
• Audio: Speech, music, ambient sound
• Video: Temporal understanding across frames
• 3D: Point clouds, meshes, spatial data
• Code: Execution and debugging

Improved Reasoning

Chain-of-thought reasoning built into the model:

User: "Which of these products would be best for a small apartment?"
[Attaches images of 3 sofas]

Jin 2.0:
"Let me analyze each option for small apartment suitability:

1. First sofa: L-shaped sectional, approximately 120" total length
   - Would dominate a small room
   - No clear path to modular reconfiguration

2. Second sofa: Compact loveseat, approximately 60" width
   - Appropriate scale for small spaces
   - Clean lines won't visually clutter

3. Third sofa: Sleeper sofa, approximately 80" width
   - Good for multi-purpose small spaces
   - However, requires clearance for opening

Recommendation: The loveseat (#2) is best for a small apartment
prioritizing visual space. The sleeper (#3) is best if you
occasionally host guests. Avoid the sectional (#1)."

Tool Use

Jin 2.0 can use external tools:

const response = await jin.query({
  prompt: "What's the current inventory for SKU-12345?",
  tools: [
    inventoryAPI,
    orderAPI,
    shippingAPI
  ]
});

// Jin decides which tools to call, when, and how to synthesize results

Architecture Evolution

Mixture of Experts

Jin 2.0 uses a sparse MoE architecture:

Input
  ↓
┌─────────────────────────────────┐
│         Router Network          │
│  "Which experts for this input?"│
└─────────┬───────────────────────┘
          ↓
    ┌─────┼─────┬─────┬─────┐
    ↓     ↓     ↓     ↓     ↓
┌─────┐┌─────┐┌─────┐┌─────┐┌─────┐
│Exp 1││Exp 2││Exp 3││ ... ││Exp N│
└──┬──┘└──┬──┘└──┬──┘       └──┬──┘
   │      │      │             │
   └──────┴──────┴─────────────┘
          ↓
    Combined Output

Benefits:

• 8x more parameters with same compute
• Specialized experts for different tasks
• Dynamic routing based on input

Native Multimodality

All modalities processed in unified space from the start:

┌─────────────────────────────────────┐
│     Unified Token Space             │
│                                     │
│  [TEXT] [IMG] [IMG] [AUDIO] [TEXT]  │
│    ↕     ↕     ↕      ↕      ↕      │
│  Cross-attention across all tokens  │
│                                     │
└─────────────────────────────────────┘

No more separate encoders merged late. True multimodal understanding.

Memory and Retrieval

Built-in retrieval augmentation:

const jin = new Jin({
  memory: {
    type: 'hybrid',
    shortTerm: 'conversation',
    longTerm: 'vector_store',
    retrieval: 'automatic'
  }
});

// Jin automatically retrieves relevant context
const response = await jin.query({
  prompt: "What did the customer say about sizing in their last order?"
  // Jin retrieves from long-term memory automatically
});

Performance

Speed

Metric	Jin 1.0	Jin 2.0	Improvement
Time to first token	450ms	120ms	73% faster
Tokens per second	45	180	4x faster
Image processing	800ms	200ms	75% faster

Quality

Benchmarks on commerce tasks:

Task	Jin 1.0	Jin 2.0	Improvement
Product understanding	84%	93%	+9%
Customer intent	79%	91%	+12%
Recommendation relevance	71%	86%	+15%
Support resolution	67%	84%	+17%

API Changes

New Capabilities

// Video understanding
const analysis = await jin.analyze({
  video: productDemoVideo,
  prompt: "Summarize the key features demonstrated"
});

// Tool use
const result = await jin.query({
  prompt: "Check inventory and create a restock order if needed",
  tools: [inventoryTool, orderTool],
  autoExecute: true
});

// Long context
const summary = await jin.query({
  documents: entireProductCatalog, // 100K+ tokens
  prompt: "Which products would appeal to eco-conscious millennials?"
});

Backward Compatibility

Jin 1.0 API calls work unchanged. New capabilities opt-in.

Deployment Options

Cloud

Available now on Hanzo Cloud:

const jin = new Jin({
  apiKey: 'xxx',
  model: 'jin-2.0'
});

Self-Hosted

Jin 2.0 available for on-premises deployment:

• Minimum: 8x H100 GPUs
• Recommended: 16x H100 GPUs
• Quantized: 4x H100 GPUs (reduced quality)

Edge

Jin 2.0 Lite for edge deployment:

• Distilled model
• Runs on single GPU
• 90% of capability at 10% of cost

Pricing

Jin 2.0 pricing:

• Input: $0.50 per million tokens
• Output: $1.50 per million tokens
• Images: $0.01 per image
• Video: $0.05 per minute

Volume discounts available.

Migration

Recommended Path

1. Test Jin 2.0 with existing prompts
2. Enable new capabilities incrementally
3. Update prompts to leverage improvements
4. Monitor quality and adjust

Timeline

• Jin 1.0 supported until December 2025
• Migration tools available now
• Premium support for enterprise migrations

What's Next

Jin 2.0 is the foundation for:

• Real-time video understanding
• Autonomous agent capabilities
• Personalized model adaptation
• Cross-lingual commerce

The future of commerce AI starts today.

---

Zach Kelling is the founder of Hanzo Industries.