title: Seed1.5-VL Technical Report
summary: We present Seed1.5-VL, a vision-language foundation model designed to advance
  general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed
  with a 532M-parameter vision encoder and a Mixture-of-Experts (MoE) LLM of 20B
  active parameters. Despite its relatively compact architecture, it delivers
  strong performance across a wide spectrum of public VLM benchmarks and internal
  evaluation suites, achieving the state-of-the-art performance on 38 out of 60
  public benchmarks. Moreover, in agent-centric tasks such as GUI control and
  gameplay, Seed1.5-VL outperforms leading multimodal systems, including OpenAI
  CUA and Claude 3.7. Beyond visual and video understanding, it also demonstrates
  strong reasoning abilities, making it particularly effective for multimodal
  reasoning challenges such as visual puzzles. We believe these capabilities will
  empower broader applications across diverse tasks. In this report, we mainly
  provide a comprehensive review of our experiences in building Seed1.5-VL across
  model design, data construction, and training at various stages, hoping that
  this report can inspire further research. Seed1.5-VL is now accessible at
  https://www.volcengine.com/ (Volcano Engine Model ID:
  doubao-1-5-thinking-vision-pro-250428)
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paper_title: Seed1.5-VL Technical Report
paper_summary: We present Seed1.5-VL, a vision-language foundation model designed to advance
  general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed
  with a 532M-parameter vision encoder and a Mixture-of-Experts (MoE) LLM of 20B
  active parameters. Despite its relatively compact architecture, it delivers
  strong performance across a wide spectrum of public VLM benchmarks and internal
  evaluation suites, achieving the state-of-the-art performance on 38 out of 60
  public benchmarks. Moreover, in agent-centric tasks such as GUI control and
  gameplay, Seed1.5-VL outperforms leading multimodal systems, including OpenAI
  CUA and Claude 3.7. Beyond visual and video understanding, it also demonstrates
  strong reasoning abilities, making it particularly effective for multimodal
  reasoning challenges such as visual puzzles. We believe these capabilities will
  empower broader applications across diverse tasks. In this report, we mainly
  provide a comprehensive review of our experiences in building Seed1.5-VL across
  model design, data construction, and training at various stages, hoping that
  this report can inspire further research. Seed1.5-VL is now accessible at
  https://www.volcengine.com/ (Volcano Engine Model ID:
  doubao-1-5-thinking-vision-pro-250428)
paper_ai_summary: Seed1.5-VL, a vision-language foundation model combining a vision encoder and a large MoE LLM, achieves state-of-the-art performance across various benchmarks and excels in multimodal reasoning tasks such as visual puzzles.
paper_ai_keywords: vision-language foundation model vision encoder Mixture-of-Experts (MoE) LLM multimodal understanding multimodal reasoning visual puzzles GUI control gameplay VLM benchmarks visual and video understanding