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.
MiniMaxChat
MiniMax M1 (Extended)
minimax-m1:extendedMiniMax M1 (Extended) by MiniMax.
- Context
- 128K tokens
- Endpoint
Pricing
Input$0.55 / 1M
Output$2.20 / 1M
Cache Write (5m)Not published
Cache Write (1h)Not published
Cache ReadNot published
Web Search$0 / 1M
Quick Start
Select an endpoint and copy a working example for this model.
Endpoint:
python
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"}}# )Supported Parameters
API docsCommon7 params
modelmessagesmax_tokenstemperaturetop_pstreamtoolsExtended4 params
reasoning_effortstream_optionsthinkingextra_bodyCursor IDE Model IDs
Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Decision guidance
Fit the request contract before production.
Check before production
- Confirm the production client uses one of the listed request surfaces: /v1/chat/completions, /v1/responses, /v1/messages.
- Estimate a representative request from the current Pay As You Go price fields instead of extrapolating from a tiny prompt.
- Check the observed-availability card and run your own timeout, retry, and fallback test before relying on the model.
Compare with Other Models
See how this model compares to others from the same provider.
MiniMax M2.5 (Lightning)
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.
- Context
- 128K
- Input
- $0.30/M
- Output
- $2.20/M
MiniMax M2.7
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.
- Context
- 204.8K
- Input
- $0.30/M
- Output
- $1.20/M
MiniMax M2.5
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.
- Context
- 204.8K
- Input
- $0.30/M
- Output
- $1.20/M
MiniMax M2.1
MiniMax-M2.1 is a lightweight, state-of-the-art model optimized for coding and agentic workflows, using just 10B activated parameters to deliver strong real-world performance with low latency and high cost efficiency. It improves on M2 with cleaner outputs and faster responses, leads in multilingual coding benchmarks (49.4% Multi-SWE-Bench, 72.5% SWE-Bench Multilingual), and serves as a versatile agent core for IDEs, coding tools, and general applications.
- Context
- 204.8K
- Input
- $0.225/M
- Output
- $0.90/M