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DeepSeek-V2.5 vs Kimi K2 Base
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. DeepSeek-V2.5 is available on 3 providers. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-V2.5 was developed as an enhanced iteration of the DeepSeek-V2 architecture, designed to incorporate improvements in model quality and efficiency. Built to advance the DeepSeek foundation model series, it provides refined capabilities for general-purpose language understanding and generation tasks.
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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.
1 year newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Kimi K2 Base
Moonshot AI
2025-07-11
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Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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DeepSeek-V2.5
Average Score:83.4%(+0.0%)
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Kimi K2 Base
Average Score:83.4%
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V2.5
3 providers
DeepInfra
Throughput: 63 tok/s
Latency: 0.5ms
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
Hyperbolic
Throughput: 100 tok/s
Latency: 0.5ms
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DeepSeek-V2.5
Avg Score:83.4%(+0.0%)
Providers:3
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Kimi K2 Base
Avg Score:83.4%
Providers:0