# Evaluate Model: Early 2026 LLM Landscape: Gemini 3.1 Pro, GPT-5 Series, Claude Opus 4.6, Llama 4
## What This Is
In early 2026, major AI labs released new, more powerful models like Google's Gemini 3.1 Pro, OpenAI's GPT-5 series, and Anthropic's Claude Opus 4.6. These models offer significant upgrades in reasoning, coding, and understanding large documents, with different models excelling at specific tasks, such as Gemini for video analysis and Claude for complex programming.
Source: https://www.ideas2it.com/blogs/llm-comparison
## 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.ideas2it.com/blogs/llm-comparison) 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
- Analyze the user's current project codebase and agent configuration file (e.g., `claude.mcp`, `codex.json`) to identify the currently configured AI model provider and model name.
- Compare the user's current model against the benchmark data for Gemini 3.1 Pro, Claude Opus 4.6, and GPT-5.3. Identify the top 2 new models best suited for the project's primary function (e.g., coding, data analysis) based on scores from SWE-Bench, GPQA, and Terminal-Bench.
- Formulate a plan to test a recommended model. Locate the new model's API endpoint, confirm if the user has the necessary API key in their `.env` file (for the new provider if applicable), and then draft the specific configuration changes required to update the agent's settings.
## Reference Implementation
```
| Benchmark | Top Performers | Scores |
|-----------|----------------|--------|
| ARC-AGI-2 (novel reasoning) | Gemini 3.1 Pro | 77.1%[4] |
| GPQA (PhD science) | Gemini 3.1 Pro (94.3%), GPT-5.3 (92.4%) | 88-94%[4] |
| SWE-Bench (coding issues) | Claude Opus 4.6 (80.8%) | 76-81%[4] |
| Terminal-Bench (DevOps) | GPT-5.3 Codex (77.3%) | 59-77%[4] |
| Humanity’s Last Exam (expert Qs w/tools) | Claude Opus 4.6 (53.1%) | 28-53%[4] |
| GDPval-AA Elo (knowledge work) | Claude Sonnet 4.6 (1,633) | 1,317-1,633[4] |
| Arena Elo (live trading) | Grok 4.20 | 1,505-1,535[4] |
```
## 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