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DeepSeek-R1 vs Kimi K2 0905
Comprehensive side-by-side LLM comparison
Kimi K2 0905 offers 262.1K more tokens in context window than DeepSeek-R1. Both models have similar pricing. 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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Moonshot AI
Kimi K2 was introduced as the second generation of Moonshot's language model family, designed to provide enhanced capabilities across language understanding and generation. Built with architectural improvements and expanded training, it represents a significant advancement in Moonshot's model offerings.
7 months newer
DeepSeek-R1
DeepSeek
2025-01-20
Kimi K2 0905
Moonshot AI
2025-09-05
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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 0905
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
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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Kimi K2 0905
Avg Score:0.0%
Providers:2