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DeepSeek-V3.2 vs Minimax M 2.5
Comprehensive side-by-side LLM comparison
Minimax M 2.5 leads with 2.1% higher average benchmark score. Minimax M 2.5 offers 68.1K more tokens in context window than DeepSeek-V3.2. Both models have similar pricing. 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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MiniMax
MiniMax M2.5 is a large language model from MiniMax extensively trained with reinforcement learning across hundreds of thousands of complex real-world environments. It targets agentic tool use, coding automation, and office productivity tasks, with strong results on software engineering and web browsing benchmarks. M2.5 represents the next generation of MiniMax's M-series models optimized for production agentic workloads.
2 months newer
DeepSeek-V3.2
DeepSeek
2025-12-01
Minimax M 2.5
MiniMax
2026-02-13
Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-V3.2
Input:$0.27
Output:$1.10($0.13 cheaper)
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Minimax M 2.5
Input:$0.30
Output:$1.20
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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Minimax M 2.5
Average Score:39.6%(+2.1%)
Performance comparison across key benchmark categories
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DeepSeek-V3.2
Coding37.5%
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Minimax M 2.5
Coding39.6%(+2.1%)
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V3.2
1 providers
DeepSeek
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Minimax M 2.5
1 providers
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DeepSeek-V3.2
Avg Score:37.5%
Providers:1
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Minimax M 2.5
Avg Score:39.6%(+2.1%)
Providers:1