DeepSeek VL2
Multimodal
Zero-eval
#1MMT-Bench
#1MME
#3MMBench-V1.1
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by DeepSeek
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About
DeepSeek VL2 is a multimodal language model developed by DeepSeek. It achieves strong performance with an average score of 70.9% across 14 benchmarks. It excels particularly in DocVQA (93.3%), ChartQA (86.0%), TextVQA (84.2%). It supports a 259K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2024, it represents DeepSeek's latest advancement in AI technology.
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Pricing Range
Input (per 1M)$9.50 -$9.50
Output (per 1M)$4800.00 -$4800.00
Providers1
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Timeline
AnnouncedDec 13, 2024
ReleasedDec 13, 2024
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Specifications
Capabilities
Multimodal
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License & Family
License
deepseek
Performance Overview
Performance metrics and category breakdown
Overall Performance
14 benchmarks
Average Score
70.9%
Best Score
93.3%
High Performers (80%+)
5Performance Metrics
Max Context Window
258.6KAvg Throughput
22.0 tok/sAvg Latency
1ms+
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All Benchmark Results for DeepSeek VL2
Complete list of benchmark scores with detailed information
| DocVQA | multimodal | 0.93 | 93.3% | Self-reported | |
| ChartQA | multimodal | 0.86 | 86.0% | Self-reported | |
| TextVQA | multimodal | 0.84 | 84.2% | Self-reported | |
| AI2D | multimodal | 0.81 | 81.4% | Self-reported | |
| OCRBench | multimodal | 0.81 | 81.1% | Self-reported | |
| MMBench | multimodal | 0.80 | 79.6% | Self-reported | |
| MMBench-V1.1 | multimodal | 0.79 | 79.2% | Self-reported | |
| InfoVQA | multimodal | 0.78 | 78.1% | Self-reported | |
| RealWorldQA | multimodal | 0.68 | 68.4% | Self-reported | |
| MMT-Bench | multimodal | 0.64 | 63.6% | Self-reported |
Showing 1 to 10 of 14 benchmarks