Amytis vs. GPT-Rosalind and Claude Science

AI is changing how scientists work. Tools such as OpenAI’s GPT-Rosalind and Anthropic’s Claude Science are bringing increasingly powerful AI capabilities into the lab.

But choosing an AI tool for scientific research isn't just about choosing the smartest model: it's about understanding how AI can fit in with, and enhance, your research. 

In this blog post, we compare Amytis with GPT-Rosalind and Claude Science across four areas important to research:

• Freedom to choose the right AI model for each task

• Local AI models and control over research data

• Flexible workflows that can adapt to your research

• Integration with LIMS and ELNs

At a glance: Amytis vs. GPT-Rosalind vs. Claude Science

Comparison based on publicly described capabilities and Amytis's product positioning as of October 2026. Features and access arrangements may change. See the official OpenAI Rosalind overview and Claude Science overview.

1. Choose the right AI model for every research task

Scientific research is a collection of different tasks, each requiring different capabilities.

A researcher might use AI to review hundreds of papers, identify patterns in experimental data, analyse a protein sequence, write a Python script, or develop a hypothesis for the next experiment.

No single model is necessarily the best choice for all of these tasks.

GPT-Rosalind and Claude Science: Single model families

GPT-Rosalind is a specialised OpenAI model family developed for life sciences research, including biological reasoning, genomics, protein engineering and experimental planning. Rosalind Workbench combines this model with other scientific tools and plug-ins. 

Claude Science takes a slightly different approach, bringing Claude models together with specialist agents, scientific databases, computational tools and reproducible research artefacts.

While both are designed to support complex scientific work, you can only use Claude and ChatGPT models in Claude Science and Rosalind Workbench, respectively.

Amytis: Model choice

Amytis is model-agnostic. Rather than restricting users to one AI provider, Amytis lets you work with multiple model families and select the models that suit your needs.

The model families available include general-purpose frontier models such as ChatGPT, Claude, Grok, and DeepSeek, alongside specialist biological models such as ESMFold and BioProtein. This flexibility allows you to choose a different model for different tasks.

For example, you could use a frontier model to search for and synthesise scientific literature, a specialist protein model to explore sequence-related questions, and a locally hosted model to work with sensitive experimental data. All of this is accessible from a single workspace.

As AI models improve or more become available, these will be built into the workspace, meaning you can keep incorporating new capabilities without rebuilding your research workflow.

2. Keep sensitive research data private with local AI models

Scientific research often involves information that cannot be shared indiscriminately.

This might include unpublished experimental results, proprietary datasets, commercially sensitive intellectual property, or confidential clinical and patient-related information. Understanding what a model does and how it will process your data is an important first step before choosing any tool 

GPT-Rosalind and Claude Science: Frontier models that run externally

Both OpenAI (provider of GPT-Rosalind) and Anthropic (provider of Claude Science) are frontier models that run in the cloud or in externally hosted data centres. This means that every time you interact with one of the models in Rosalind Workbench or Claude Science, the information you input will be processed externally. How your data is handled and what visibility OpenAI or Anthropic have of this data depends on the type of user you are. 

Their privacy policies and data usage agreements can be found here: 

• OpenAI for GPT-Rosalind

  • Personal or individual accounts

  • Enterprise

• Anthropic for Claude Science 

  • Personal or individual accounts

  • Enterprise

It’s important to note that for both providers, you need to specifically opt-out if you don’t want your data (aka the information you use in ChatGPT or Claude) used for model-training purposes. 

For research with strict data sovereignty or intellectual property requirements, it’s important to read through all policies and T&Cs to make sure you’re comfortable with how your data will be handled. 

Amytis: Choose models that run on your own machine

Amytis supports locally hosted AI models: models that you download onto your machine and can be used to process information without sending your data to a cloud-based model provider. 

Local models are particularly useful when working with sensitive research information, operating under strict institutional data policies, or for highly specific and repeatable workflows.

Combined with the visual, knowledge graph nature of Amytis and a suite of cloud-based frontier models, having a selection of locally hosted models on your device means you have ultimate control over where your data is sent and how it’s processed. 

Local models do vary in capability, speed and hardware requirements. A smaller model running locally may not perform as well as a large frontier cloud model on every task. So, you can use local models where data control is the priority and the task is specific, or cloud models where data is not private and the task is broader.

3. Build research workflows that reflect how science actually works

Scientific research rarely follows a straight line. Yet many AI tools still centre the interaction on a conversation: ask a question, receive an answer, and continue down the same thread.

Imagine you're three weeks into a research project. You've discussed your experimental design, reviewed relevant papers and developed an initial analysis with an AI assistant.

You now want to change one part of the analysis.

In a chat-based workflow, the relevant information may be buried in a long conversation. You might need to revisit earlier messages, explain the context again, or start a new conversation and reconstruct the reasoning.

Long conversations also create context-management challenges. AI models have finite context windows, and adding more messages doesn't guarantee that every earlier detail will remain available or relevant.

GPT-Rosalind and Claude Science: Linear, chat-based interactions

Rosalind Workbench supports guided scientific workflows, while Claude Science offers interactive research sessions, specialist agents and reproducible outputs. Both are designed to help scientists work through complex, multi-step tasks.

However, the overall interface for both is similar to the general purpose ChatGPT and Claude apps: interactions happen through a linear chat. 

Amytis: A visual workspace built around interconnected workflows

Amytis uses flexible, visual knowledge graphs to structure research workflows.

Rather than treating the conversation as the project itself, researchers can represent their work as interconnected steps, information and processes. They can organise complex workflows, inspect individual components and modify parts of the project without having to restart the entire interaction.

This makes it easier to develop a research process iteratively. It also provides a way to manage context: instead of relying on an ever-growing conversation, the workflow can organise information around the relevant steps and tasks.

You remain in control of how the project is structured, what gets changed and how the workflow develops.

4. Bring AI, LIMS and electronic lab notebooks into one workspace

AI is most useful when it fits naturally into the research process.

But in many labs, research activities are spread across multiple systems: an electronic lab notebook (ELN) for recording experiments, a laboratory information management system (LIMS) for managing samples and laboratory operations, separate tools for data analysis, and an AI assistant for questions and interpretation.

Moving between these systems creates friction. It can also make it harder to connect an AI-generated insight to the experimental context in which it matters.

GPT-Rosalind and Claude Science: A separate tool with integrations available

Rosalind Workbench and Claude Science are primarily AI tools. Both advertise connector or plug-in functionality with major ELN and LIMs providers, but the tools remain disparate. 

Amytis: Integrate AI with the work of the laboratory

Amytis is designed to bring research workflows and lab management together in one environment.

It can house LIMS functionality, protocols and project plans alongside AI-assisted analysis and other research activities.

This means lab processes don't have to sit entirely outside the AI workspace. You can organise experimental work, manage relevant information and build AI-assisted workflows around the research you are conducting.

So, which AI research workspace should you choose?

Amytis, GPT-Rosalind and Claude Science are all aimed at making AI more useful for scientific research, but they approach the problem differently.

GPT-Rosalind is worth considering if your priority is specialised AI reasoning for life sciences, supported by scientific tools and guided research workflows.

Claude Science is worth considering if you want a scientific workbench built around Claude, with computational tools, specialist agents, access to scientific databases and reproducible research outputs.

Amytis is ideal if you want greater control over your overall research environment: the models you use, where those models run and how your workflows are structured. You also get access to a workspace that is functional without AI: you can continue to use the workspace to track your lab inventory, protocols and projects.

Its four central differentiators are:

• Model choice: combine different AI model families and specialist biological models instead of committing your workflow to one provider.

• Local AI: run locally hosted models when keeping inference on your own machine is important.

• Flexible knowledge graphs: structure and modify interconnected research workflows without relying exclusively on linear conversations.

• Native LIMS and ELN functionality: bring laboratory processes, protocols and project planning into the same environment as AI-assisted research.

The right choice ultimately depends on your research, your infrastructure and the way your team works.

If you want help deciding, get in touch with our team and we can help you explore the right option for your research.