JetBrains unified IntelliJ IDEA into a single distribution in December 2025, merging the previously separate Community and Ultimate editions. Under this new model, all Ultimate features require a subscription to unlock, while the IDE remains fully functional and free for both commercial and non-commercial projects without a subscription. This consolidation ensures that every developer works within an environment where the "IDE knows everything about your code," providing blazing-fast navigation and relevant suggestions in every context. I find this shift critical because it eliminates the friction of plugin management, placing mission-critical tools at your fingertips across a wide variety of supported languages and frameworks. The platform adheres to industry-leading security standards, including SOC 2 attestation, which protects organizational data and ensures compliance with global regulations. With 88 Fortune Global Top 100 companies using these products to deliver top-quality software, the baseline for enterprise adoption is high. You already know how to navigate an IDE, so focus on how the AI layer changes your daily workflow rather than the basic interface.
Multi-agent workflows and validation loops
The most significant trend is the transition from simple code completion to autonomous agent collaboration. Junie, an AI coding agent launched in January 2025, became generally available in April 2025, marking a clear departure from the AI Assistant’s focus on code completion. Unlike traditional assistants, Junie functions as an agent capable of working with multiple agents at once, either in one project or across several, without re-explaining state or codebase context. Developers can choose how each agent launches, in the GUI or the terminal, and decide what the IDE keeps front and center: your code or the agent session. I see real value in seeing which sessions are currently running, done, or waiting on you, as this visibility prevents bottlenecks in complex development cycles. You can comment on a line in the diff and send it back to the agent to act on, creating a tight feedback loop. Generating code quickly with AI is the easy part, but trusting the result is harder. IntelliJ IDEA closes the loop by allowing you to generate with AI and then validate immediately with a debugger, test runner, and built-in inspections. This approach means you ship with confidence, not just speed, ensuring nothing breaks before you merge the change. Debugging, testing, profiling, and database tools are all one click away, so you confirm that the generated code integrates cleanly with your existing application architecture.
Model flexibility and proprietary innovation
JetBrains leverages models from OpenAI and Google, alongside proprietary models developed in-house, such as the Mellum model released in April 2025. In 2024, the company integrated Google’s Gemini models into the AI Assistant, making it the first tool of its kind to combine functionality from OpenAI’s GPT-4o, Gemini, and JetBrains’ proprietary models. This collaboration leveraged Google Cloud’s Vertex AI to expand the pool of large language models available to users, with Gemini Pro 1.5 and Flash 1.5 adding a long context window, advanced reasoning, and high-volume, low-latency capabilities. Starting from version 2024.3, users could select their preferred LLM directly within the AI chat interface, giving developers control over which engine drives their tasks. Later in 2025, JetBrains integrated Gemini 3 Pro into both its AI Assistant and Junie, noting that the new model showed more than a 50% improvement over Gemini 2.5 Pro in solved benchmark tasks. For organizations using JetBrains AI Enterprise, teams can configure which LLM they use, while other users benefit from automatic selection of the most suitable model for each task. This flexibility addresses specific performance needs, though the reliance on external models raises questions about data sovereignty that remain unanswered. The integration of Gemini Code Assist as a plugin further supports real-time code completions, full-function generation, and natural-language-to-code conversion within the ecosystem. I recommend prioritizing the validation tools over raw generation speed, as the ability to inspect and debug AI output is what separates productive use from risky experimentation.
