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High Reasoning

MiniMax M2.7

MiniMax M2.7 is a groundbreaking self-evolving AI model that autonomously optimizes its own training, handles 30-50% of research workflows, and excels in real-world agentic tasks like software engineering and office productivity. With top benchmarks like 1495 ELO on GDPval-AA and unmatched efficiency at just 10B parameters, it delivers GLM-5-level intelligence at a fraction of the cost.

196k Context
High Intelligence
The search results provided do not contain the training knowledge cutoff date for MiniMax M2.7. The results mention MiniMax M2 (released November 2025 with a June 2024 cutoff) and describe M2.7's capabilities, but do not specify M2.7's knowledge cutoff date. Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

MMLU-Pro
89.11%
GPQA
89.65%
SWE-Pro
56.22%

How do you want to interact?

Start a Conversation

Ask anything.
Have a natural conversation, brainstorm ideas, draft emails, or ask for advice.

Start Chatting

Use a Persona

Specialized Experts.
Instruct the AI to act as a Coding Tutor, Marketing Expert, or Travel Guide.

Pick a Persona

Why use MiniMax M2.7?

Self-Evolving Improvement

Autonomously improves itself through iterative self-assessment and over 100 rounds of optimization, achieving 30% performance gains without human input

Multi-Agent Collaboration

Supports agent teams for complex workflows, role boundaries, adversarial reasoning, and real-time collaboration in environments like Open Room

Professional Software Engineering

Excels in end-to-end coding projects, log analysis, debugging, code security, and ML pipelines with top benchmarks like 56.22% on SWE-Pro

Capability Examples

Software Engineering Debug
Debug this Python code for a bug in the log analysis function: def analyze_logs(logs): for log in logs: if 'ERROR' in log: return log.upper(). Help me fix dependencies and security issues in a multi-file project.
Identified ERROR in line 3 dependency mismatch. Fixed by adding import os and validating paths. Security patch: sanitized inputs against injection. Updated code: def analyze_logs(logs): import os; for log in logs: if 'ERROR' in log and os.path.exists(log): return log.upper(). Project merge-ready with risk assessment.
Office Productivity Edit
Create an Excel formula to sum sales >$1000 from Q1 data, then revise a PPT slide with charts and bullet points for executive review. Multi-round edits needed.
Formula: =SUMIF(B:B,">1000",C:C) for Q1 sales. PPT slide revised: Added pie chart (97% fidelity), bullets on trends, multi-round alignment to brand. Exports ready. Skill adherence: 97%.

How to use

1
Go to Chat

Navigate to the "AI Chat" page.

2
Select Model

Ensure MiniMax M2.7 is selected.

3
Type Prompt

Ask a question or paste code.

4
Interact

Refine the answer by replying to the AI.

Compare LLMs Side-by-Side

Is MiniMax M2.7 better than Claude 3.5 or Gemini? Test same prompts simultaneously in the Chat Playground.

Open Chat Playground

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