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DeepSeek-R1 vs DeepSeek-V3.2
Comprehensive side-by-side LLM comparison
DeepSeek-V3.2 is $1.37 cheaper per million tokens. 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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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.
10 months newer
DeepSeek-R1
DeepSeek
2025-01-20
DeepSeek-V3.2
DeepSeek
2025-12-01
Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-R1
Input:$0.55
Output:$2.19
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DeepSeek-V3.2
Input:$0.27
Output:$1.10($1.37 cheaper)
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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DeepSeek-V3.2
1 providers
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DeepSeek-R1
Avg Score:0.0%
Providers:1
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DeepSeek-V3.2
Avg Score:0.0%
Providers:1