LightweightByteDance

Seed 1.6 Flash

Seed 1.6 Flash is ByteDance's lightweight multimodal model supporting text, image, and video processing with a 262K token context window.

Context 262K
Tier Lightweight
Modalities text, image, video
Input from
$0.075 / 1M tokens
across 1 provider
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API Pricing

ProviderInput / 1MOutput / 1MUpdated
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OpenRouter
$0.075$0.3004/14/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
ByteDance
Family
Seed
Tier
Lightweight
Context Window
262K
Modalities
Text, Image, Video

Capabilities

Tool Calling
No
Open Source
No

Strengths & Limitations

  • Supports three modalities: text, image, and video processing
  • Large 262K token context window for extensive multimodal content
  • Lightweight tier optimized for speed and efficiency
  • Native video understanding capability
  • Developed by ByteDance with expertise in multimedia applications
  • Suitable for high-volume processing workflows
  • Cross-modal analysis within single inference calls
  • No tool calling or function execution support
  • Proprietary model with weights not publicly available
  • Lightweight tier may have reduced reasoning capabilities compared to flagship models
  • Limited technical documentation and benchmark scores available
  • Newer model family with less established track record

Key Features

•262K token context window
•Text input and generation
•Image analysis and understanding
•Video processing capabilities
•Multimodal content analysis
•Streaming response support
•Batch processing optimization
•Cross-modal reasoning

About Seed 1.6 Flash

Seed 1.6 Flash is ByteDance's lightweight multimodal model in the Seed family, designed for efficient processing across text, image, and video inputs. As a lightweight tier model, it balances capability with speed and cost-effectiveness for multimodal applications. The model features a 262,144 token context window and native support for three modalities: text, image, and video processing. This multimodal capability allows it to analyze and understand content across different media types within a single inference call, making it suitable for applications requiring cross-modal understanding. Seed 1.6 Flash is positioned for high-volume multimodal applications where speed and cost efficiency are priorities over maximum capability. Its lightweight design makes it practical for content moderation, media analysis, and automated processing workflows that need to handle mixed media inputs at scale.

Common Use Cases

Seed 1.6 Flash is well-suited for content moderation platforms, social media analysis, automated video summarization, and multimedia content classification. Its lightweight design makes it ideal for high-volume applications like processing user-generated content, automated thumbnail generation, video highlight extraction, and cross-platform media analysis. The combination of video understanding and large context window enables it to process longer video content or analyze multiple media files together, making it practical for media companies, content platforms, and automated moderation systems that need efficient multimodal processing.

Frequently Asked Questions

How much does Seed 1.6 Flash cost per million tokens?

Seed 1.6 Flash pricing varies by provider and pricing type (standard vs batch). Check the pricing table above for current rates across all providers.

What is Seed 1.6 Flash best used for?

Seed 1.6 Flash excels at high-volume multimodal applications including content moderation, video analysis, automated media processing, and cross-modal content understanding. Its lightweight design and native video support make it ideal for platforms processing large amounts of user-generated multimedia content.

Does Seed 1.6 Flash support video analysis?

Yes, Seed 1.6 Flash natively supports video processing as one of its three supported modalities, along with text and images. This allows it to analyze video content, extract information, and perform cross-modal reasoning between video, image, and text inputs within its 262K token context window.