modelintermediateGeneral AI

Gemini 3.1 Pro Preview Tops April 2026 AI Benchmarks

As of April 2026, Google's Gemini 3.1 Pro Preview is a top performer in many AI capability tests, especially for complex reasoning. However, other models like Anthropic's Claude 4.6 and OpenAI's GPT-5.4 remain highly competitive, with specific strengths in areas like coding and versatile task handling.

AI SETUP PROMPT

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

# Evaluate Model: Gemini 3.1 Pro Preview Tops April 2026 AI Benchmarks

## What This Is
As of April 2026, Google's Gemini 3.1 Pro Preview is a top performer in many AI capability tests, especially for complex reasoning. However, other models like Anthropic's Claude 4.6 and OpenAI's GPT-5.4 remain highly competitive, with specific strengths in areas like coding and versatile task handling.

Source: https://www.youtube.com/watch?v=NZVaF-3Yvfs

## 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.youtube.com/watch?v=NZVaF-3Yvfs) 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 task (e.g., data analysis, creative writing, code generation, multi-language support).
- Cross-reference the identified project task with the April 2026 benchmark data. Recommend a primary model (e.g., Gemini 3.1 for reasoning, GPT-5.4 for versatile coding, Claude 4.6 for natural prose).
- If the user approves, locate the project's AI provider configuration (e.g., in `.env`, `settings.json`, or an API client). Update the model identifier string to the recommended model, using existing API keys or prompting the user to add one if necessary.

## 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

3,038 charactersCompatible with Claude Code & Codex CLI

MANUAL SETUP STEPS

01Scan the user's current project codebase and `README.md` to identify the primary task (e.g., data analysis, creative writing, code generation, multi-language support).
02Cross-reference the identified project task with the April 2026 benchmark data. Recommend a primary model (e.g., Gemini 3.1 for reasoning, GPT-5.4 for versatile coding, Claude 4.6 for natural prose).
03If the user approves, locate the project's AI provider configuration (e.g., in `.env`, `settings.json`, or an API client). Update the model identifier string to the recommended model, using existing API keys or prompting the user to add one if necessary.

FIELD OPERATIONS

Dynamic Model Routing Gateway

Create an API gateway that accepts a prompt and a 'task_type' (e.g., 'coding', 'reasoning', 'prose'). The gateway routes the request to the optimal model for that task (GPT-5.4 for 'coding', Claude 4.6 for 'prose') based on the benchmark data, returning the response transparently.

Automated Multi-Model Benchmark Runner

Build a script that takes a specific user problem (e.g., a complex function to write, a dataset to summarize) and runs it against multiple model APIs (Gemini, Claude, GPT). The script should then score the outputs based on correctness, verbosity, and speed, presenting a cost-performance report.

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