In the field of artificial intelligence, parameter scale was once considered the absolute king of performance. However, a new open-source project is challenging this perception. MiroThinker1.5, a research agent model with only 3 billion parameters, achieves performance comparable to models with trillions of parameters through its efficient interactive scaling mechanism, while reducing inference costs to 1/20 of traditional models. This not only marks a milestone in AI's transition from "brute force computing" to "intelligent density," but also injects strong momentum into the open-source community. Below is an in-depth analysis of this hot topic by AIbase.

 Breakthrough Performance: Small Parameters, Big Intelligence

MiroThinker1.5, with its "lightweight" 3 billion parameters, surpasses benchmark models like Kimi-K2-Thinking that have up to one trillion parameters. In the Chinese browsing benchmark test (BrowseComp), it performed exceptionally well, defeating many larger-scale competitors. The key lies in its "Interactive Scaling" technology, which emphasizes "Agentic Density" rather than just the number of parameters. By seeking evidence in real time, iteratively refining, and verifying hypotheses, it achieves cutting-edge agent search capabilities. Each query's inference cost is only $0.07, equivalent to 1/20 of traditional 1T models, significantly lowering the barrier for AI applications and driving the evolution from "static reasoning" to "native intelligence."

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 Core Technological Innovation: Multi-turn Reasoning and Self-correction

The core of this model lies in its multi-turn reasoning mechanism for memory management. It can retain the context of the last five conversations while cleaning up irrelevant historical information, ensuring information integrity and low resource consumption. This design gives the model the ability of "self-directed inquiry," supporting up to 400 tool interactions per task, forming a recursive, hierarchical reasoning chain. MiroThinker1.5 has features such as automated information search, multi-step thinking and self-correction, long-term memory and context understanding, tool calling and execution, and research-level information integration and evaluation. These characteristics allow it to handle complex tasks effortlessly, avoiding the problem of "hindsight leakage" in traditional models.

 Practical Application Testing: From Historical Analysis to Deep Reports

In practical tests, MiroThinker1.5 has shown amazing potential. For example, when given the task "review each knowledge revolution in human history (such as writing, printing, electricity, internet), analyze the uniqueness and potential risks of the AI revolution, and how it will change the direction of civilization evolution," the model conducted dozens of rounds of reasoning and searching, including multiple verifications and re-searches. It covered various domestic and foreign sources and literature, finally generating a complete report. This proves its strong capability in understanding problems, planning steps, and using tools to solve issues. User feedback indicates that the model performs especially well in exploring sensitive topics or capturing real-time information, far exceeding the "learning on the spot" mode of traditional search engines.

 Industry Insights: Open Source Redefining the AI Landscape

The release of MiroThinker1.5 is seen as a "DeepSeek moment" in the AI industry. It challenges traditional scaling laws, shifting from parameter-intensive memory to method-driven investigation. Through open-source form (available on GitHub and Hugging Face), it not only reduces training and inference costs but also provides efficient tools for small and medium-sized developers and researchers. Experts point out that when the entire industry invests heavily to improve 1% accuracy, this interactive growth will become the key to the Agentic Era. In the future, AI competition will focus on "intelligent density" rather than parameter scale, which may disrupt the old order and promote more efficient logical construction.

AIbase believes that the emergence of MiroThinker1.5 signals the transformation of AI from the "Giant Era" to the "Intelligent Era." It not only brings hope to the open-source community but also reminds practitioners: true innovation lies in methods, not scale. AIbase will continue to track developments in this field. Please stay tuned for more cutting-edge reports.

Project Address: https://github.com/MiroMindAI/MiroThinker