LLM Farm

What is LLM Farm?

LLM Farm is your control center for working with multiple AI models in one place. Instead of switching between different providers and interfaces, you can compare responses from GPT, Claude, Gemini, and others side by side, automatically route prompts to the most suitable model, and keep track of token usage and costs — all from a single chat interface.

It's built for teams and individuals who want more control over which model handles which task, or who simply want to find the best model for their workflow. Whether you're evaluating a new model for production use or just curious how different providers handle your domain, LLM Farm gives you a structured way to explore and decide.

How to Use LLM Farm

Getting Started

Click LLM Farm in the sidebar.
Open the Settings panel and choose your preferred model (or leave it on auto-routing).
Type your message and send.
Switch models at any time during a conversation to compare responses.

LLM Farm is accessible directly from the main sidebar under the Agents section. The Settings tab lets you configure model preferences, token budgets, and API keys. The History tab shows past sessions so you can revisit previous comparisons or benchmark runs.

Key Features

Multi-Provider Access

Multi-provider routing with fallback — Automatically sends your prompt to the best available model. If one provider is down or over capacity, it falls back gracefully.
Centralized API key management — One place to manage all your provider credentials, so you don't need to reconfigure each integration separately.

Budget and Performance

Token budget management — Set spending limits per session or per user to avoid unexpected costs.
Real-time benchmarking — See response time, token count, and quality metrics for each model side by side.

Testing and Templates

Custom prompt templates — Save reusable prompt structures so you don't have to retype them each session.
A/B testing — Run the same prompt against multiple models simultaneously and compare outputs in a structured view.

Example Use Cases

Common Scenarios

Compare GPT, Claude, and Gemini side by side on the same question to pick the best answer.
Auto-route simple queries to cheaper models (like summarization or classification) while reserving powerful models for complex reasoning.

Advanced Scenarios

Run prompt regression tests to catch when a model update changes the output you rely on.
Enforce token spending limits across a team to keep AI usage costs predictable and auditable.

Tips for Best Results

Getting Better Output

Use smaller, faster models (like GPT-4o Mini or Gemini Flash) for straightforward tasks such as rewrites, translations, or simple Q&A. Save larger models for deep analysis, code generation, or multi-step reasoning.
Try the same prompt on two or three models and compare — different models have different strengths, and the best one depends on your specific task.
Use prompt templates to standardize how your team interacts with models, which makes benchmarking more meaningful and results easier to compare over time.

Common Mistakes to Avoid

Avoid running every task through the largest model by default — costs add up quickly and the quality difference is often negligible for simpler tasks.
Don't skip the benchmarking step when evaluating a new model; impressions from a single prompt are rarely representative of real-world performance across your workload.