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DeepSeek-V3 vs Kimi K2 Base
Comprehensive side-by-side LLM comparison
Kimi K2 Base leads with 1.8% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-V3 was introduced as a major architectural advancement, developed with 671B mixture-of-experts parameters and trained on 14.8 trillion tokens. Built to be three times faster than V2 while maintaining open-source availability, it demonstrates competitive performance against frontier closed-source models and represents a significant leap in efficient large-scale model design.
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Moonshot AI
Kimi K2 Base was created as the foundation model in the K2 series, designed to serve as a starting point for fine-tuning and customization. Built to provide strong base capabilities for domain-specific applications, it enables developers to build specialized solutions on Moonshot's architecture.
6 months newer
DeepSeek-V3
DeepSeek
2024-12-25
Kimi K2 Base
Moonshot AI
2025-07-11
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Performance Metrics
Context window and performance specifications
Average performance across 6 common benchmarks
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DeepSeek-V3
Average Score:66.6%
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Kimi K2 Base
Average Score:68.4%(+1.8%)
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V3
1 providers
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
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Kimi K2 Base
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DeepSeek-V3
Avg Score:66.6%
Providers:1
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Kimi K2 Base
Avg Score:68.4%(+1.8%)
Providers:0