# Evaluate Model: Model Benchmark Showdown: Gemini 3.1 Pro vs. Claude Opus 4.6 vs. GPT-5.4
## What This Is
Recent AI model tests from March 2026 show that Google's Gemini 3.1 Pro is the top choice for complex reasoning and advanced coding tasks, offering the best performance for its cost. For building automated software agents or processing very large documents, Anthropic's Claude Opus 4.6 and OpenAI's GPT-5.4 are the leading options.
Source: https://lmcouncil.ai/benchmarks
## 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://lmcouncil.ai/benchmarks) 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 project files to determine the primary task requirement (e.g., novel reasoning, coding issue resolution, DevOps automation, large document analysis).
- Compare the project's primary task against the provided benchmark data (ARC-AGI-2 for reasoning, SWE-Bench for coding, Terminal-Bench for DevOps) to recommend the optimal model: Gemini 3.1 Pro for reasoning/coding, Claude Opus 4.6 for agent reliability, or GPT-5.4 for DevOps/long-context.
- Upon user approval, modify the project's AI configuration file (e.g., `claude.json`, `openclaw-config.yml`, `.env`) to use the recommended model. Check for an existing API aggregator configuration first (like OpenRouter) and update the model string. If a direct API is required, use the corresponding provider key from the user's environment or prompt them to add it.
## 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