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DeepSeek-V3.1 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-V3.1. Both models have similar pricing. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-V3.1, released by DeepSeek in August 2025, is a hybrid large language model with 671 billion total parameters (37 billion active) that unifies the capabilities of DeepSeek-V3 and DeepSeek-R1 in a single model. It features a 128K token context window and supports both direct generation and extended reasoning modes selectable via the chat template. DeepSeek-V3.1 targets general-purpose tasks, coding, and complex 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.
5 months newer
DeepSeek-V3.1
DeepSeek
2025-08-21
Minimax M 2.5
MiniMax
2026-02-13
Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-V3.1
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
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V3.1
1 providers
DeepSeek
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Minimax M 2.5
1 providers
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DeepSeek-V3.1
Avg Score:0.0%
Providers:1
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Minimax M 2.5
Avg Score:0.0%
Providers:1