Seed 2.0 Lite: ByteDance's Cost-Efficient Enterprise Multimodal Model

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Key Highlights

• Surpasses Previous Generation: Overall performance exceeds Seed 1.8 with significant improvements in visual reasoning, instruction following, and tool calling
• Multimodal Input: Supports image, video, and text input, covering document/chart analysis and video understanding scenarios
• Flexible Vision Tiers: Three visual input quality options (low / high / xhigh) to balance cost and fidelity
• Strong Benchmarks: AIME 2025 at 93.0, MMLU-Pro 87.7 (surpasses Pro), SWE-Bench Verified 73.5%
• Cost-Efficient Deployment: ~1/5 the cost of Pro, ideal for high-QPS and broad-coverage production scenarios

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Background

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Detailed Analysis

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Core Features

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Performance Highlights

• Near-flagship math reasoning: AIME 2025 at 93.0, only 3 points below Pro (96.0)
• Surpasses Pro in knowledge tasks: MMLU-Pro at 87.7 vs Pro’s 87.0, proving Lite is fully capable for knowledge-understanding workloads
• Major visual reasoning improvement: MathVision jumps from 81.3 (Seed 1.8) to 86.4
• Video understanding: VideoMME at 87.7, VideoReasonBench at 64.2, supporting spatiotemporal video analysis

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Multimodal Capabilities

• Information extraction from mixed text-and-image content
• Document and chart analysis covering most common scenarios
• Visual grounding capabilities

• Spatiotemporal video understanding and motion perception
• Video captioning
• Video reasoning and analysis

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Technical Specifications

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Practical Applications

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Recommended Use Cases

1. Unstructured Information Processing: Document parsing, receipt recognition, contract analysis
2. Text Content Creation: Marketing copy, product descriptions, content summarization
3. Search and Recommendation: Semantic understanding, intent recognition, content ranking
4. Data Analysis: Report interpretation, chart understanding, trend analysis
5. Video Content Understanding: Video captioning, content moderation, clip analysis
6. Agent Workflows: Multi-step instruction execution, tool calling, function calling

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Code Examples

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Text Conversation

from openai import OpenAI

client = OpenAI(
    api_key="your-apiyi-key",
    base_url="https://api.apiyi.com/v1"
)

response = client.chat.completions.create(
    model="ByteDance-Seed-2.0-lite",
    messages=[
        {
            "role": "user",
            "content": "Analyze the following quarterly data and provide key trends and recommendations..."
        }
    ],
    max_tokens=4096
)

print(response.choices[0].message.content)

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Image Understanding

response = client.chat.completions.create(
    model="ByteDance-Seed-2.0-lite",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Analyze the key data and trends in this chart"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/chart.png",
                        "detail": "high"
                    }
                }
            ]
        }
    ],
    max_tokens=4096
)

print(response.choices[0].message.content)

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Tool Calling

response = client.chat.completions.create(
    model="ByteDance-Seed-2.0-lite",
    messages=[
        {"role": "user", "content": "What's the weather like in Beijing today?"}
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get weather information for a specified city",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city": {"type": "string", "description": "City name"}
                    },
                    "required": ["city"]
                }
            }
        }
    ]
)

print(response.choices[0].message)

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Best Practices

1. Choose the Right Vision Tier:
   • Use default high for standard document analysis
   • Use xhigh for dense text or complex charts
   • Use low for simple classification to save costs
2. Leverage Multimodal Capabilities:
   • Mixed image-text input improves information extraction
   • Video understanding supports spatiotemporal analysis for content moderation
3. Large-Scale Production Deployment:
   • Lite costs ~1/5 of Pro — prioritize for high-QPS scenarios
   • For knowledge understanding tasks (MMLU-Pro), Lite can fully replace Pro

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Pricing and Availability

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Seed 2.0 Series Comparison

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Deposit Bonus

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Available Models

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How to Access

• Website: apiyi.com
• API Endpoint: https://api.apiyi.com/v1
• OpenAI-compatible format
• Works with all OpenAI SDKs

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Summary and Recommendations

• Best Value: ~1/5 the cost of Pro, surpasses Pro in some benchmarks (MMLU-Pro)
• Full Multimodal: Image, video, and text input with flexible vision quality tiers
• Production-Ready: Long-context processing, multi-source fusion, high-fidelity structured outputs
• Strong Agent Capabilities: Instruction following at 94.0 (COLLIE), major tool calling improvements

1. Daily production tasks: Lite is the default choice, balancing capability and cost
2. High-accuracy needs: Consider upgrading to Pro for tasks like SWE-Bench
3. High-concurrency lightweight scenarios: Consider Mini for lowest cost
4. Vision-intensive scenarios: Use xhigh vision tier for best accuracy

• Enterprise users deploying AI capabilities at scale
• Production workflows requiring multimodal document/video analysis
• Developers building agent workflows
• High-QPS applications seeking the best cost-performance ratio