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DeepSeek-R1 vs Gemma 2 9B
Comprehensive side-by-side LLM comparison
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 2 9B was created as a more compact open-source model, designed to deliver capable performance with reduced computational requirements. Built with 9 billion parameters and instruction tuning, it serves applications where efficiency and accessibility are valued alongside the benefits of open-source availability.
6 months newer
Gemma 2 9B
2024-06-27
DeepSeek-R1
DeepSeek
2025-01-20
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Performance Metrics
Context window and performance specifications
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 2 9B
Avg Score:0.0%
Providers:0