Best AI Tools for Research

Gemma is Google DeepMind's family of open-source large language models, serving as lightweight versions of Gemini. Available models include Gemma 1 (February 2024), Gemma 2 (June 2024), and Gemma 3 (March 2026) with variants like PaliGemma for vision-language tasks and MedGemma for medical applications. Available in multiple sizes (2B, 7B, and larger variants). Designed for research, education, and commercial applications with permissive licensing. Trained on similar data and methods as Gemini models but optimized for open-source deployment. Available through Hugging Face, Kaggle, and Google Cloud Vertex AI.

Perplexity uses frontier LLMs to browse the web in real-time and provide cited, accurate answers to complex queries. Its 'Pages' feature allows for the instant creation of research reports.

SAM3D v2 leverages Meta's latest Segment Anything technology to reconstruct 3D geometry from single or multiple images with extreme precision. It is the industry standard for research-grade 3D reconstruction.

Generates 3D objects from text prompts or images using OpenAI's Shap-E model, a conditional generative model for 3D assets. Produces high-quality 3D meshes, point clouds, and neural radiance fields (NeRFs) from natural language descriptions. Supports both text-to-3D and image-to-3D workflows, generating detailed 3D models with realistic geometry and textures suitable for game assets, product visualization, and 3D printing applications. Open-source model with comprehensive documentation and active community support, making it ideal for research, prototyping, and educational use.

Generates high-quality 3D NeRF (Neural Radiance Field) representations from text prompts using score distillation sampling, a technique that leverages pre-trained 2D diffusion models for 3D generation. Produces detailed 3D scenes and objects with realistic lighting, materials, and geometry from natural language descriptions. Enables creation of view-consistent 3D content without requiring 3D training data, making it ideal for generating complex 3D scenes, objects, and environments for visualization, games, and virtual reality applications. Pioneering approach uses 2D diffusion models to guide 3D NeRF generation, enabling high-quality 3D creation from text.

Generates 3D models from single images using Zero-1-to-3, a model that learns to generate novel views of objects from a single input image. Produces view-consistent 3D representations by understanding object geometry and appearance from limited input. Enables creation of 3D assets from photographs, product images, or concept art, making it ideal for 3D reconstruction, product visualization, and asset generation workflows. Advanced geometric understanding enables high-quality 3D reconstruction from single images with view consistency across different angles.