Use this model when
- The workload fits the text or chat tasks shown in this model's current catalog record.
- The input fits within the published 128K-token context record, with output and system overhead budgeted separately.
minimax-m1:extendedMiniMax M1 (Extended) by MiniMax.
Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="minimax-m1:extended", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="minimax-m1:extended",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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MiniMax-M2.5-Lightning is the high-speed variant of the M2.5 series, optimized for low latency, real-time responsiveness, and high-frequency workloads. It retains the core planning and execution strengths of M2.5 while further improving inference efficiency and response speed, making it ideal for interactive applications, rapid coding assistance, and workflow automation. With enhanced cost efficiency and reduced latency, M2.5-Lightning is particularly well suited for high-throughput, always-on deployments and production environments where speed and scalability are critical.
MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. It incorporates advanced multi-agent collaboration, enabling the model to plan, execute, and iteratively refine complex tasks across dynamic environments. Built for production-grade workflows, M2.7 supports tasks such as live debugging, root cause analysis, financial modeling, and full document generation across Word, Excel, and PowerPoint. With strong benchmark performance—including 56.2% on SWE-Pro, 57.0% on Terminal Bench 2, and 1495 ELO on GDPval-AA—it sets a new standard for multi-agent systems in real-world digital workflows.
MiniMax-M2.5 is a state-of-the-art large language model designed for real-world productivity and digital work environments. Building on the coding strengths of M2.1, it expands into general office workflows, demonstrating strong capability in generating and operating Word, Excel, and PowerPoint files, switching context across software environments, and collaborating effectively with both human users and agent systems. Trained on diverse real-world working scenarios, M2.5 combines strong planning ability with improved token efficiency, enabling more effective task execution. With strong benchmark performance—including 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp—it is well suited for productivity automation, coding workflows, and agent-driven knowledge work.
Initialized observational baseline with no recorded failures