Seed1.5-VL: ByteDance's Vision-Language Foundation Model Achieving 38 SOTA Benchmarks

In the rapidly advancing field of vision-language models, a new heavyweight has emerged from an unexpected corner. Seed1.5-VL, developed by ByteDance’s Seed team, has achieved state-of-the-art results on an astonishing 38 out of 60 public benchmarks, spanning image understanding, video comprehension, document parsing, and multi-image reasoning.

Built on a 20-billion parameter Mixture-of-Experts (MoE) architecture with approximately 2 billion activated parameters per token, Seed1.5-VL represents a careful balancing act between raw capability and computational efficiency. It outperforms models with far larger parameter counts while maintaining inference speeds suitable for real-world applications.

The model’s benchmark sweep is remarkable not just for the number of wins, but for the breadth of categories it dominates. From OCR and chart understanding to multi-image reasoning and video comprehension, Seed1.5-VL demonstrates that ByteDance’s research team has achieved something genuinely comprehensive in the multimodal space.

What Is the Architecture Behind Seed1.5-VL?

Seed1.5-VL’s architecture is a masterclass in modern multimodal design, combining several proven techniques into a cohesive system.

The dual visual encoder design is particularly innovative. SigLIP provides broad visual understanding – recognizing objects, scenes, and overall composition. ViTDet adds fine-grained detail, enabling the model to read small text, distinguish subtle visual differences, and understand low-level visual features that typical VLMs miss.

graph TD
    A[Input Image] --> B[SigLIP Encoder]
    A --> C[ViTDet Encoder]
    B --> D[Visual Feature Fusion]
    C --> D
    D --> E[Q-Former Projection]
    F[Input Text] --> G[Text Embedding]
    E --> H[MoE LLM Backbone]
    G --> H
    H --> I[Expert Router]
    I --> J[Expert 1: Visual Reasoning]
    I --> K[Expert 2: Text Understanding]
    I --> L[Expert 3: Multi-Image Comparison]
    I --> M[Expert N: ...]
    J --> N[Output Generation]
    K --> N
    L --> N
    M --> N

How Does Seed1.5-VL Perform Across Benchmark Categories?

The breadth of Seed1.5-VL’s benchmark performance is its most impressive characteristic. The following table shows its performance across major evaluation categories.

The ChartQA score of 90.0% is particularly noteworthy – it demonstrates that Seed1.5-VL can not only see charts but truly understand them, extracting accurate data points and relationships from complex visualizations.

How Does Seed1.5-VL Handle Video Understanding?

Video understanding presents unique challenges for VLMs: the model must maintain temporal coherence across frames, track object movement, and understand actions that unfold over time.

sequenceDiagram
    Participant V as Video Input
    Participant S as Sampler
    Participant E as Visual Encoders
    Participant M as MoE LLM
    Participant O as Output
    
    V->>S: Extract key frames
    S->>E: Send sampled frames
    E->>M: Per-frame visual tokens
    M->>M: Temporal attention across frames
    M->>M: Object tracking across time
    M->>O: Generate video description
    M->>O: Answer temporal questions

Seed1.5-VL processes video by sampling key frames, encoding each through the dual visual encoder pipeline, and then allowing the MoE language backbone to reason across the temporal dimension. This approach achieves a 69.3 overall score on the Video-MME benchmark, placing it among the top video understanding models regardless of parameter count.

How Do Seed1.5-VL Model Variants Compare?

ByteDance released multiple model configurations to accommodate different deployment scenarios.

The 20B MoE variant is the flagship, using its 2B active parameters per token to achieve results that sometimes rival models with 10x the activated parameter count. The “Plus” variant incorporates additional training data and extended fine-tuning for maximum benchmark performance.

What Are the Practical Applications of Seed1.5-VL?

Seed1.5-VL’s diverse capabilities translate into concrete applications across multiple industries.

How Can You Deploy Seed1.5-VL?

The model is available for local deployment through the official GitHub repository.

git clone https://github.com/ByteDance-Seed/Seed1.5-VL
cd Seed1.5-VL
pip install -r requirements.txt

# Run inference
python demo.py --model-path Seed1.5-VL-20B

For production deployments, ByteDance has also provided optimized inference code using vLLM and TensorRT-LLM backends, enabling efficient serving at scale. The Hugging Face integration allows straightforward model loading with the standard Transformers API.

FAQ

What is Seed1.5-VL? Seed1.5-VL is ByteDance’s vision-language foundation model featuring a 20B parameter Mixture-of-Experts (MoE) architecture. It achieves state-of-the-art results on 38 out of 60 public benchmarks spanning image understanding, video understanding, document parsing, and multi-image reasoning tasks.

What is the architecture of Seed1.5-VL? Seed1.5-VL uses a 20B parameter MoE (Mixture-of-Experts) architecture with approximately 2B activated parameters per token. It employs a dual visual encoder design combining SigLIP for general visual features and ViTDet for fine-grained detail, connected to an LLM backbone via a Q-Former projector.

How does Seed1.5-VL perform on benchmarks? Seed1.5-VL achieves SOTA on 38 of 60 public benchmarks, outperforming models of comparable and even larger sizes. On specific tasks it scores 90.0% on ChartQA, 88.1% on OCRBench, 87.5 on MMBench-EN, and 85.2% on MMBench-CN. For video understanding, it scores 69.3 overall on Video-MME.

What makes Seed1.5-VL different from other VLM models? Seed1.5-VL differentiates itself through several architectural innovations: dual visual encoders that preserve fine-grained visual details, resolution upscaling that dynamically increases input resolution, a native multi-image training pipeline, and a highly efficient MoE architecture that activates only ~2B of 20B parameters per token.

Is Seed1.5-VL open source and how can I access it? Yes, Seed1.5-VL is open source. The model weights, inference code, and evaluation scripts are available on GitHub under the ByteDance-Seed organization. The model can be deployed using the Hugging Face Transformers library or the official inference codebase.

Further Reading

• Seed1.5-VL GitHub Repository – Official source code, model weights, and documentation
• Seed1.5-VL Technical Report (arXiv) – Research paper detailing architecture and benchmarks
• Seed1.5-VL on Hugging Face – Model weights and inference examples
• ByteDance Seed Team Blog – Research blog and additional model releases