modelintermediateGeneral AI

State of AI Models (April 2026): GPT-5, Gemini 3, Claude 4, Llama 4

As of early 2026, the AI model landscape is highly competitive, with no single model being the best for every task. Google's Gemini 3.1 Pro and OpenAI's GPT-5.4 Pro often lead in reasoning, while Anthropic's Claude 4.6 excels at coding and Meta's Llama 4 specializes in analyzing extremely long documents.

AI SETUP PROMPT

Paste into Claude Code or Codex CLI — it will scan your project and set everything up

# Evaluate Model: State of AI Models (April 2026): GPT-5, Gemini 3, Claude 4, Llama 4

## What This Is
As of early 2026, the AI model landscape is highly competitive, with no single model being the best for every task. Google's Gemini 3.1 Pro and OpenAI's GPT-5.4 Pro often lead in reasoning, while Anthropic's Claude 4.6 excels at coding and Meta's Llama 4 specializes in analyzing extremely long documents.

Source: https://admix.software/blog/ai-model-benchmarks-2026

## 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://admix.software/blog/ai-model-benchmarks-2026) 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 current project's configuration files (e.g., package.json, .env, claude.mcp) to identify the currently configured AI model provider and model name.
- Based on the user's project goals (e.g., coding, RAG, long-context analysis), analyze the provided benchmark table and recommend the top-performing model, such as Claude Opus 4.6 for coding or Llama 4 for a 10M token context task.
- If the user approves a model switch, generate a new configuration snippet for their AI environment. Use existing API keys from their .env file for the new provider (e.g., Google, Anthropic, OpenAI); if a key is not present, generate a placeholder and instruct the user to add the required API key.

## 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

3,136 charactersCompatible with Claude Code & Codex CLI

MANUAL SETUP STEPS

01Scan the current project's configuration files (e.g., package.json, .env, claude.mcp) to identify the currently configured AI model provider and model name.
02Based on the user's project goals (e.g., coding, RAG, long-context analysis), analyze the provided benchmark table and recommend the top-performing model, such as Claude Opus 4.6 for coding or Llama 4 for a 10M token context task.
03If the user approves a model switch, generate a new configuration snippet for their AI environment. Use existing API keys from their .env file for the new provider (e.g., Google, Anthropic, OpenAI); if a key is not present, generate a placeholder and instruct the user to add the required API key.

FIELD OPERATIONS

Dynamic Model Selector API

Build a small API service that takes a task description (e.g., 'summarize legal document', 'write python script') and a user-defined budget. The service uses the 2026 benchmark data to route the request to the most cost-effective and highest-performing model for that specific task, like using Llama 4 for the legal doc and Claude Opus 4.6 for the script.

Long-Context RAG Legal Assistant

Create a research tool for legal teams using Meta's Llama 4 model. Ingest a 10 million token corpus of case law and internal documents, and build a Retrieval-Augmented Generation (RAG) system that can answer complex legal questions and cite specific sources from the massive context window.

STRATEGIC APPLICATIONS