deepseek-vl2
About deepseek-vl2
DeepSeek-VL2 is a mixed-expert (MoE) vision-language model developed based on DeepSeekMoE-27B, employing a sparse-activated MoE architecture to achieve superior performance with only 4.5B active parameters. The model excels in various tasks including visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Compared to existing open-source dense models and MoE-based models, it demonstrates competitive or state-of-the-art performance using the same or fewer active parameters.
Explore how DeepSeek-VL2's advanced vision-language capabilities solve complex, real-world problems across various industries.
Intelligent Document Processing
Automate data extraction and analysis from diverse documents like invoices, contracts, and reports, leveraging OCR and visual understanding.
Use Case Example:
"Automatically extracts key figures from scanned financial statements and populates a database, reducing manual data entry by 80% for an accounting firm."
Visual Content Analysis
Identify and categorize objects, scenes, or inappropriate content within images and videos for moderation, search, or analytics.
Use Case Example:
"Flags prohibited items or sensitive content in user-uploaded e-commerce product images, ensuring compliance with platform guidelines and brand safety."
Automated Image Captioning
Generate detailed, context-aware descriptions for images, enhancing accessibility for visually impaired users and improving content SEO.
Use Case Example:
"Provides a rich textual description for a complex medical MRI scan, explaining findings to a doctor or patient, or generating alt-text for web images."
E-commerce Product Enrichment
Automatically tag product images with attributes, brands, and categories for improved search, recommendations, and inventory management.
Use Case Example:
"Analyzes a clothing item's image to identify its style, color, material, and brand from a logo, populating product metadata for an online catalog system."
Metadata
Specification
State
Deprecated
Architecture
Calibrated
No
Mixture of Experts
Yes
Total Parameters
27B
Activated Parameters
4.5B
Reasoning
No
Precision
FP8
Context length
4K
Max Tokens
4K
Compare with Other Models
See how this model stacks up against others.
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Input:
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Input:
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Max output:
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Input:
$
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Total Context:
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Max output:
164K
Input:
$
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/ M Tokens
Output:
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Total Context:
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Max output:
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Input:
$
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Total Context:
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Max output:
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Input:
$
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/ M Tokens
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2.18
/ M Tokens
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Release on: Jan 20, 2025
Total Context:
131K
Max output:
131K
Input:
$
0.18
/ M Tokens
Output:
$
0.18
/ M Tokens
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Release on: Jan 20, 2025
Total Context:
131K
Max output:
131K
Input:
$
0.1
/ M Tokens
Output:
$
0.1
/ M Tokens
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Release on: Jan 20, 2025
Total Context:
33K
Max output:
16K
Input:
$
0.05
/ M Tokens
Output:
$
0.05
/ M Tokens
