+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
DeepSeek VL2 vs Gemma 2 27B
Comprehensive side-by-side LLM comparison
Gemma 2 27B leads with 3.8% higher average benchmark score. DeepSeek VL2 supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
+
+
+
+
DeepSeek
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.
+
+
+
+
Gemma 2 27B is a language model developed by Google. It achieves strong performance with an average score of 69.1% across 16 benchmarks. It excels particularly in ARC-E (88.6%), HellaSwag (86.4%), BoolQ (84.8%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Google's latest advancement in AI technology.
5 months newer
Gemma 2 27B
2024-06-27
DeepSeek VL2
DeepSeek
2024-12-13
Performance Metrics
Context window and performance specifications
Average performance across 30 common benchmarks
+
+
+
+
DeepSeek VL2
Average Score:33.1%
+
+
+
+
Gemma 2 27B
Average Score:36.9%(+3.8%)
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
DeepSeek VL2
1 providers
Replicate
Throughput: 22 tok/s
Latency: 0.5ms
+
+
+
+
Gemma 2 27B
+
+
+
+
DeepSeek VL2
Avg Score:33.1%
Providers:1
+
+
+
+
Gemma 2 27B
Avg Score:36.9%(+3.8%)
Providers:0