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MiniMax M2.7
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MinimaxMinimaxReleased 2026-03-18

MiniMax M2.7

197K context$0.300/M input$1.20/M output

MiniMax M2.7 is a large language model designed for autonomous productivity and continuous improvement. It incorporates advanced agentic capabilities through multi-agent collaboration, enabling it to plan, execute, and refine complex tasks in dynamic environments. The model is optimized for production-grade workflows such as live debugging, financial modeling, and document generation across various formats like Word, Excel, and PowerPoint. It achieves strong benchmark results, setting a new standard for multi-agent systems in real-world digital workflows.

What is MiniMax M2.7?

MiniMax M2.7 is an AI model from Minimax that Agent Mag tracks for pricing, context window, modalities, benchmarks, and API compatibility. Builders can use this page to compare MiniMax M2.7 against other models for agent workflows and production deployments.

Model ID

MiniMax M2.7 is a large language model designed for autonomous productivity and continuous improvement. It incorporates advanced agentic capabilities through multi-agent collaboration, enabling it to plan, execute, and refine complex tasks in dynamic environments. The model is optimized for production-grade workflows such as live debugging, financial modeling, and document generation across various formats like Word, Excel, and PowerPoint. It achieves strong benchmark results, setting a new standard for multi-agent systems in real-world digital workflows.

Architecture & Specifications
Tokenizer
Other
Released
2026-03-18
Modalities
Input
text
Output
text
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Strengths
  • Advanced agentic capabilities for multi-agent collaboration
  • Optimized for production-grade workflows like debugging and financial modeling
  • Strong benchmark performance in real-world tasks
  • Supports structured outputs and tool integration
Limitations
  • No information on training data size or sources
  • Parameter count not disclosed
  • Specific architecture details not provided
Recommended Use Cases
Live debugging and root cause analysis
Financial modeling and simulations
Document generation across Word, Excel, and PowerPoint
Multi-agent collaboration for complex task execution
Structured output generation for workflows

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Data enriched Apr 24, 2026. Pricing from OpenRouter API.