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DeepSeek-R1 vs Qwen2-VL-72B-Instruct
Comprehensive side-by-side LLM comparison
Qwen2-VL-72B-Instruct supports multimodal inputs. DeepSeek-R1 is available on 5 providers. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-R1 was developed as a reasoning-focused language model, designed to combine chain-of-thought reasoning with reinforcement learning techniques. Built to excel at complex problem-solving through trial-and-error learning and deliberate analytical processes, it demonstrates the power of efficient training methods in open-source model development.
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Alibaba Cloud / Qwen Team
Qwen2-VL 72B was developed as a large vision-language model, designed to handle multimodal tasks combining visual and textual understanding. Built with 72 billion parameters for integrated vision and language processing, it enables applications requiring sophisticated analysis of images alongside text.
4 months newer
Qwen2-VL-72B-Instruct
Alibaba Cloud / Qwen Team
2024-08-29
DeepSeek-R1
DeepSeek
2025-01-20
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Performance Metrics
Context window and performance specifications
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Knowledge Cutoff
Training data recency comparison
Qwen2-VL-72B-Instruct
2023-06-30
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-R1
5 providers
DeepInfra
Throughput: 0.9 tok/s
Latency: 0.3ms
DeepSeek
Throughput: 9 tok/s
Latency: 0.3ms
Fireworks
Throughput: 2.1 tok/s
Latency: 0.3ms
Together
Throughput: 4 tok/s
Latency: 0.6ms
ZeroEval
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DeepSeek-R1
Avg Score:0.0%
Providers:5
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Qwen2-VL-72B-Instruct
Avg Score:0.0%
Providers:0