Seed1.5-VL Technical Report
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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Seed1.5-VL Technical Report
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)
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.
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