The rapid evolution of artificial intelligence has led to a proliferation of large language models (LLMs), each with its own strengths, and weaknesses. While ChatGPT, Gemini, and Claude have all emerged as leading contenders, users are increasingly exploring the nuances of each platform, particularly when it comes to complex tasks like agent orchestration and personalized AI experiences. A growing number of individuals and developers are now looking at ways to transfer their customized AI setups – known as GPTs in ChatGPT, Gems in Gemini, and Skills in Claude – between these platforms. This is driven by a desire to leverage the specific capabilities of each model and avoid vendor lock-in.
Recent data indicates a shift in user preference. According to an analysis published in January 2026, ChatGPT’s market share has decreased from 86.7% to 64.5% in the past year, signaling a growing openness to alternatives. While ChatGPT remains a dominant force, users are recognizing that Gemini and Claude offer distinct advantages in certain areas. This trend is prompting a search for interoperability – the ability to seamlessly move customized AI agents between different LLMs.
Understanding the Ecosystem: GPTs, Gems, and Skills
Each of the major AI platforms offers a way to create customized AI agents tailored to specific tasks. OpenAI’s ChatGPT allows users to build GPTs, which are essentially custom versions of ChatGPT designed for a particular purpose. Google’s Gemini offers Gems, providing similar functionality. Anthropic’s Claude, meanwhile, utilizes Skills, which are designed to extend Claude’s capabilities and allow it to perform specialized tasks. The core concept behind all three is the same: to empower users to create AI assistants that are finely tuned to their individual needs.
The ability to migrate these customizations is becoming increasingly important. Users invest time and effort in crafting prompts, defining behaviors, and providing knowledge bases for their GPTs, Gems, and Skills. Losing this investment when switching platforms is a significant deterrent. The question of how to convert these personalized agents from one platform to another is gaining traction within the AI community.
Why Migrate? The Strengths of Each Platform
The decision to migrate often hinges on the specific strengths of each LLM. As of early 2026, Claude consistently demonstrates superior instruction-following capabilities. According to recent testing, Claude excels at adhering to detailed prompts, even those that are lengthy and complex. This is particularly valuable for tasks requiring precise outputs, such as proofreading and editing, where Claude accurately highlights deletions and insertions as instructed, unlike some other models. One example highlighted in a recent report showed Claude correctly formatting edits with strikethrough and blue text, while ChatGPT struggled with the same task.
Gemini 3, shines in the realm of audio and video analysis. Its ability to process and understand multimedia content surpasses that of ChatGPT and, in many cases, Claude. This makes Gemini a compelling choice for applications involving speech recognition, image understanding, and video summarization. Google has also recently introduced features allowing users to import chat histories and memories from other AI applications into Gemini, further enhancing its appeal for those seeking a unified AI experience. Google’s recent move to allow importing chat histories aims to simplify the transition process for new users.
ChatGPT, despite losing some market share, remains a versatile and widely accessible option. Its broad knowledge base and ease of use continue to make it a popular choice for a wide range of applications. However, users are increasingly recognizing that specialized tasks may be better suited to the more focused capabilities of Claude or Gemini.
The Challenge of Conversion: A Lack of Direct Tools
Currently, there isn’t a one-click solution to automatically convert GPTs, Gems, and Skills between platforms. The underlying architectures and prompt engineering techniques differ significantly, making direct translation difficult. The process typically involves manually recreating the desired functionality on the target platform. This requires a deep understanding of each platform’s specific features and limitations.
The core of any customized AI agent lies in its prompts and knowledge base. Migrating these elements requires careful consideration. Prompts may need to be rephrased to align with the target LLM’s preferred style and syntax. Knowledge bases may need to be reformatted or restructured to ensure compatibility. The specific features available on each platform – such as function calling or external API integrations – may require adjustments to the agent’s design.
Strategies for Migration: A Manual Approach
While automated tools are lacking, several strategies can facilitate the migration process. The most common approach involves a manual recreation of the agent’s functionality on the target platform. This entails:
- Deconstructing the Original: Carefully analyze the original GPT, Gem, or Skill to understand its core purpose, key prompts, and knowledge base.
- Replicating the Logic: Recreate the agent’s logic and behavior on the target platform using its native tools and features.
- Testing and Refinement: Thoroughly test the migrated agent to ensure it performs as expected. Refine the prompts and knowledge base as needed to optimize its performance.
Another helpful technique is to focus on the underlying principles of the agent’s design. Instead of attempting a literal translation of prompts, concentrate on capturing the agent’s intended behavior and recreating it using the target platform’s unique capabilities. This approach can often lead to more effective and robust agents.
Leveraging Shared Principles of Prompt Engineering
Despite the differences between platforms, some fundamental principles of prompt engineering remain consistent. Techniques such as providing clear instructions, specifying the desired output format, and using examples can improve the performance of any LLM. By focusing on these core principles, users can increase the likelihood of successfully migrating their customized AI agents.
The Future of AI Interoperability
The current lack of seamless migration tools highlights a growing need for greater interoperability within the AI ecosystem. As the number of LLMs continues to expand, users will increasingly demand the ability to move their customized agents between platforms without losing valuable time and effort. The development of standardized formats for representing AI agents and their knowledge bases could significantly simplify the migration process.
Several initiatives are underway to address this challenge. Open-source projects are exploring the creation of common frameworks for building and deploying AI agents. Industry collaborations are also exploring the potential for developing standardized APIs that would allow different LLMs to communicate and share data. While these efforts are still in their early stages, they represent a promising step towards a more open and interoperable AI landscape.
The ability to easily migrate between platforms will not only benefit individual users but also foster innovation and competition within the AI industry. By reducing vendor lock-in, it will empower users to choose the best tools for their specific needs and encourage developers to create more versatile and adaptable AI agents.
As the AI landscape continues to evolve, the demand for seamless migration tools will only grow stronger. The future of AI likely hinges on the ability to create a more interconnected and interoperable ecosystem, where users can freely move their customized agents between platforms and leverage the unique strengths of each LLM. For now, a manual, thoughtful approach remains the most reliable path for those seeking to transfer their AI customizations.
Retain an eye on developments from Anthropic, Google, and OpenAI as they continue to refine their platforms and potentially introduce new features to facilitate migration. The next few months will likely see further advancements in this area, offering users more options for managing their AI agents across different ecosystems.