# Evaluate Model: 2026 Multimodal AI Landscape: Llama 4, GPT-5, and Gemini 3
## What This Is
The next generation of AI models from Meta, OpenAI, and Google will understand multiple data types at once, including text, images, video, and audio. This unified approach enables more powerful business solutions like advanced robotics, comprehensive data analysis for customer service, and more accurate diagnostic tools.
Source: https://www.tiledb.com/blog/multimodal-ai-models
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and current AI integrations
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
- Which AI models I currently use and for what purposes
Then ask me before proceeding:
1. Am I interested in evaluating this model for my project, or just want a summary of what it offers?
2. If I want to try it — which part of my current AI stack should it replace or complement?
## Source Access Note
The source URL (https://www.tiledb.com/blog/multimodal-ai-models) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is a **New AI Model** — a model release, update, or capability announcement.
- Analyze the best use cases for this model within my project and current AI stack
- Compare its strengths, pricing, and context window against whatever I currently use
- Give me a clear, convincing argument for why this model would (or would not) be a good fit for my project
- If I want to try it: update my API configuration (provider, model ID, any new parameters) to point to this model
- If it requires a new API key or provider signup, tell me exactly what to do
## Additional Context
- Scan the user's current project codebase and `README.md` to identify potential use cases for multimodal AI, such as processing image uploads with text descriptions, analyzing video streams, or transcribing audio inputs.
- Compare the features of Llama 4, GPT-5, and Gemini 3 against the model currently configured in the user's environment. Generate a report on which new model would offer the most significant upgrade for the identified use cases.
- Review the project's data handling logic and suggest architectural changes to create a unified data pipeline in preparation for future integration with a multimodal model API. Propose a data structure or class that can flexibly accommodate text, image paths, audio buffers, and video streams.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it