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DeepSeek-V3.2 vs Kimi K2.5
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Kimi K2.5 offers 128.2K more tokens in context window than DeepSeek-V3.2. DeepSeek-V3.2 is $6.13 cheaper per million tokens. Both models have their strengths depending on your specific coding needs.
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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 K2.5, released by Moonshot AI in January 2026, is an updated Mixture-of-Experts large language model with 1 trillion total parameters and 32 billion active parameters. It builds on Kimi K2 with improved coding performance across multiple languages and an expanded context window. Kimi K2.5 targets agentic development workflows, polyglot code generation, and open-source deployments requiring large-scale MoE reasoning.
1 month newer
DeepSeek-V3.2
DeepSeek
2025-12-01
Kimi K2.5
Moonshot AI
2026-01
Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-V3.2
Input:$0.27
Output:$1.10($6.13 cheaper)
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Kimi K2.5
Input:$1.50
Output:$6.00
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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DeepSeek-V3.2
Average Score:37.5%
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Kimi K2.5
Average Score:37.9%(+0.4%)
Performance comparison across key benchmark categories
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DeepSeek-V3.2
Coding37.5%
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Kimi K2.5
Coding37.9%(+0.4%)
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V3.2
1 providers
DeepSeek
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Kimi K2.5
1 providers
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DeepSeek-V3.2
Avg Score:37.5%
Providers:1
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Kimi K2.5
Avg Score:37.9%(+0.4%)
Providers:1