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DeepSeek-R1 vs Gemma 3n E4B
Comprehensive side-by-side LLM comparison
Gemma 3n E4B 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 E4B was developed as a slightly larger edge-optimized variant, designed to provide enhanced capabilities while still fitting within edge computing constraints. Built to serve applications that can accommodate more parameters for improved quality, it expands the range of edge deployment options.
5 months newer
DeepSeek-R1
DeepSeek
2025-01-20
Gemma 3n E4B
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 E4B
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 E4B
Avg Score:0.0%
Providers:0