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DeepSeek-R1 vs Gemma 3n E2B
Comprehensive side-by-side LLM comparison
Gemma 3n E2B 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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Gemma 3N E2B was created as a specialized efficient variant, designed for deployment in edge and mobile environments with strict resource constraints. Built with optimizations for edge computing, it brings Gemma capabilities to devices and applications where traditional models would be impractical.
5 months newer
DeepSeek-R1
DeepSeek
2025-01-20
Gemma 3n E2B
2025-06-26
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Performance Metrics
Context window and performance specifications
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Knowledge Cutoff
Training data recency comparison
Gemma 3n E2B
2024-06-01
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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Gemma 3n E2B
Avg Score:0.0%
Providers:0