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DeepSeek-V3.2 vs Kimi K2
Comprehensive side-by-side LLM comparison
DeepSeek-V3.2 leads with 16.1% higher average benchmark score. Kimi K2 offers 128.2K more tokens in context window than DeepSeek-V3.2. DeepSeek-V3.2 is $1.73 cheaper per million tokens. Overall, DeepSeek-V3.2 is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-V3.2, released by DeepSeek on December 1, 2025, is a large language model with 685 billion total parameters featuring integrated thinking in tool-use and support for both reasoning and direct generation modes. It features a 128K token context window and introduced large-scale agent training across 1,800+ environments. DeepSeek-V3.2 targets agentic workflows, complex instruction following, and coding tasks 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.
4 months newer
Kimi K2
Moonshot AI
2025-07-11
DeepSeek-V3.2
DeepSeek
2025-12-01
Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-V3.2
Input:$0.27
Output:$1.10($1.73 cheaper)
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Kimi K2
Input:$0.60
Output:$2.50
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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DeepSeek-V3.2
Average Score:80.4%(+16.1%)
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Kimi K2
Average Score:64.3%
Performance comparison across key benchmark categories
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DeepSeek-V3.2
Agents80.4%(+16.1%)
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Kimi K2
Agents64.3%
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V3.2
1 providers
DeepSeek
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Kimi K2
1 providers
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DeepSeek-V3.2
Avg Score:80.4%(+16.1%)
Providers:1
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Kimi K2
Avg Score:64.3%
Providers:1