MiniMax M2.5 is an open-source frontier model designed for real-world productivity with state-of-the-art performance in coding, search, agentic tool-calling, and office work. It offers efficient execution at $1 per hour with 100 tokens per second, enabling economically viable scaling of long-horizon agents.
MiniMax M2.5 is an open-source frontier AI coding model designed for real-world productivity and belongs to the category of large language models specialized in coding, search, agentic tool-calling, and office work. This model is built for developers, AI engineers, and businesses that need state-of-the-art performance in automated code generation, complex reasoning, and multi-step agent tasks. Its core value lies in delivering enterprise-grade capabilities at a fraction of the cost, enabling economically viable scaling of long-horizon agents. With output speeds of 100 tokens per second and pricing as low as one-tenth that of comparable models, M2.5 makes advanced AI accessible for production environments. The model is available in both 100 TPS and 50 TPS versions to suit different throughput requirements.
Many organizations struggle with the high cost and latency of deploying large language models for real-time applications. Traditional models often require expensive infrastructure and yield slow response times, making them impractical for agentic workflows that demand rapid, iterative decision-making. Additionally, coding and office automation tasks typically need specialized models that can handle diverse formats like Word, PPT, and Excel, not just plain text. MiniMax M2.5 addresses these pain points by providing a high-throughput, low-latency solution that excels in both coding and office scenarios. Its efficient architecture reduces token waste and operational costs while maintaining cutting-edge accuracy, allowing teams to build and deploy AI agents without budget overruns.
The first major feature group focuses on coding compatibility and search and tool use capabilities. The model's coding performance is demonstrated through state-of-the-art results on benchmarks like Multi-SWE-Bench, where it achieved the best scores in the industry, earning it the label of open-source SOTA. This feature enables developers to generate complete applications, such as an e-commerce website for a premium modular cat tunnel system, from a single prompt with minimal iteration. The search and tool use capability allows M2.5 to call external APIs and retrieve information, making it ideal for agentic tasks that require interacting with databases, web services, or external knowledge bases. By combining reinforcement learning-optimized task decomposition with thinking token efficiency, the model autonomously breaks down complex problems into manageable steps, reducing both the time and computational resources needed to reach a solution.
Publisher
A
admin
Launch Date2026-02-17
Platformweb
Pricingpaid
Domain Authority
Ahrefs DR77/100