What the 2025 Global Large‑Model Open‑Source Landscape Reveals About AI Trends

Release of the Global Large‑Model Open‑Source Ecosystem Report 2.0

On September 13, at the 2025 Inclusion·外滩大会 AI Open‑Source Insight Forum, Ant Open‑Source and Inclusion AI launched the updated Global Large‑Model Open‑Source Development Ecosystem Panorama and Trend Report , the second version since its May debut.

The report, built on data‑driven analysis of the entire GitHub platform using the OpenRank algorithm, objectively presents the current state and future direction of AI open‑source. It covers 22 technical domains and 114 of the most‑watched open‑source projects, divided into two major directions: AI Agent and AI Infra .

Key Findings and Statistics

More than 62% of the projects emerged after the “GPT moment” in October 2022, with an average age of only 30 months, reflecting the rapid iteration of the AI open‑source ecosystem.

Among roughly 360,000 global developers contributing to the panorama, 24% are from the United States, 18% from China, followed by India (8%), Germany (6%) and the United Kingdom (5%). Together, the US and China account for over 40% of core contributions.

Strategically, Chinese firms tend to open‑source model weights, while leading US companies favor closed‑source approaches.

"These projects are like digital building blocks; developers can freely combine them to create new applications," noted Wang Xu, Vice‑Chairman of the Ant Open‑Source Technical Committee.

Explosion of AI Coding Tools

The report highlights a dramatic rise in AI programming tools that automatically generate or modify code, accelerating a “developer efficiency revolution.” Tools are categorized as command‑line interfaces (e.g., Google’s Gemini CLI) and IDE plugins (e.g., Cline). The lightweight CLI tools are praised for flexibility, while IDE plugins focus on workflow integration.

New coding tools launched in 2025 attract over 30,000 stars on average; Gemini CLI, after only three months of open‑source, surpassed 60,000 stars, becoming one of the fastest‑growing projects.

Observations indicate model‑focused companies prefer CLI entry points, whereas teams emphasizing user experience opt for IDE integrations, jointly driving the efficiency “revolution.”

As AI assistants become indispensable, the report predicts programmers will delegate repetitive tasks to AI tools, concentrating on creative design and complex problem solving, potentially reshaping software development roles.

2025 Large‑Model Development Timeline Panorama

Ant Open‑Source also released a comprehensive timeline covering major large‑model releases from January 2025 to the present, including both open‑parameter and closed‑source models. The diagram annotates model parameters, modalities, and other key attributes, helping developers and the community understand the intense competition among vendors.

Key development directions identified are:

Clear divergence between open‑weight models in China and closed‑source models in the US.

Scaling of parameters under Mixture‑of‑Experts (MoE) architectures.

Enhanced reasoning capabilities via reinforcement learning.

Growth of multimodal models as the mainstream.

Emergence of dual evaluation methods combining subjective voting and objective metrics.