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DeepSeek-R1 vs Minimax M 2.5
Comprehensive side-by-side LLM comparison
Minimax M 2.5 offers 68.1K more tokens in context window than DeepSeek-R1. Minimax M 2.5 is $1.24 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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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.
1 year newer
DeepSeek-R1
DeepSeek
2025-01-20
Minimax M 2.5
MiniMax
2026-02-13
Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-R1
Input:$0.55
Output:$2.19
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Minimax M 2.5
Input:$0.30
Output:$1.20($1.24 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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Minimax M 2.5
1 providers
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DeepSeek-R1
Avg Score:0.0%
Providers:1
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Minimax M 2.5
Avg Score:0.0%
Providers:1