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DeepSeek-R1 vs Kimi K2
Comprehensive side-by-side LLM comparison
Kimi K2 offers 128.2K more tokens in context window than DeepSeek-R1. Both models have similar pricing. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-R1, released by DeepSeek on January 20, 2025, is a large reasoning model with 671 billion total parameters (37 billion active in its MoE architecture) designed for extended chain-of-thought reasoning. It features a 128K token context window and demonstrated strong performance on mathematics, coding, and scientific reasoning benchmarks at its release. DeepSeek-R1 targets complex analytical tasks, competitive programming, and applications requiring deep deliberative reasoning under an open MIT license.
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Moonshot AI
Kimi K2, released by Moonshot AI on July 11, 2025, is an open-weight Mixture-of-Experts large language model with 1 trillion total parameters and 32 billion active parameters per inference. It features a 256K token context window (expanded from 128K in an September 2025 update) and demonstrated strong performance on agentic coding benchmarks. Kimi K2 targets software engineering agents, tool-use workflows, and open-source deployments under a modified MIT license.
5 months newer
DeepSeek-R1
DeepSeek
2025-01-20
Kimi K2
Moonshot AI
2025-07-11
Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-R1
Input:$0.55
Output:$2.19($0.36 cheaper)
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Kimi K2
Input:$0.60
Output:$2.50
Performance Metrics
Context window and performance specifications
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-R1
1 providers
DeepSeek
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Kimi K2
1 providers
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DeepSeek-R1
Avg Score:0.0%
Providers:1
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Kimi K2
Avg Score:0.0%
Providers:1