# Evaluate Model: AI Model Landscape Report: GPT-5.4, Gemini 3.1 Pro, Claude Opus 4.6, and Grok 4.20
## What This Is
As of April 2026, the AI model market is highly competitive, with top models from Google (Gemini 3.1 Pro), Anthropic (Claude Opus 4.6), OpenAI (GPT-5.4), and xAI (Grok 4.20) showing very close performance. Key differences lie in specific strengths like reasoning (Gemini), long context windows (Meta's Llama 4), and coding (Grok). Pricing varies dramatically, so choosing a model depends heavily on the specific task and budget.
Source: https://www.grandlinux.com/en/blogs/ai-model-comparison.html
## 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.grandlinux.com/en/blogs/ai-model-comparison.html) 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 the primary tasks (e.g., code generation, data analysis, natural language generation, multimodal processing). Create a requirements matrix mapping these tasks to the strengths of the models mentioned: Gemini 3.1 Pro (reasoning), Claude Opus 4.6 (prose), Grok 4.20 (coding), Llama 4 (long context), and GPT-5.4 (all-around/multimodal).
- Based on the requirements matrix, estimate API call volume and token usage for the top 2-3 candidate models. Use the report's pricing guidance (e.g., Gemini Flash for low cost, GPT-5.4 for high performance) to project the monthly cost for each model and present a cost-benefit analysis to the user.
- If the user approves a model change, search the project for the existing AI API client configuration. Refactor the API calls to use the new provider's SDK, configuring the client with the appropriate model ID and API key. Check for an existing key (e.g., GOOGLE_API_KEY, ANTHROPIC_API_KEY) in the user's environment variables before prompting them to add a new one.
## 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