model-compare 2025.12.22083425

model-compare evaluates AI models side‑by‑side based on user tasks, rating accuracy, creativity, and efficiency to guide model choice.

Model Compare Package

A new package that helps users compare and evaluate different AI language models by analyzing their performance and capabilities.

Overview

This package takes user-provided text input describing specific tasks or scenarios and returns a structured comparison of how different models, like Gemini 3 Flash and Claude Code, would handle those tasks. It focuses on providing objective, side-by-side evaluations based on criteria such as accuracy, creativity, and efficiency, helping users make informed decisions about which model to use for their specific needs.

Installation

pip install model_compare

Usage

from model_compare import model_compare

response = model_compare(
    user_input="Compare Gemini 3 Flash and Claude Code on a text-to-image generation task",
    api_key="your_llm7_api_key",
    llm=None  # optional, defaults to ChatLLM7
)

You can also pass your own BaseChatModel instance, e.g., to use a different LLM like OpenAI:

from langchain_openai import ChatOpenAI
from model_compare import model_compare

llm = ChatOpenAI()
response = model_compare(
    user_input="Compare Gemini 3 Flash and Claude Code on a text-to-image generation task",
    llm=llm
)

Or to use Anthropic:

from langchain_anthropic import ChatAnthropic
from model_compare import model_compare

llm = ChatAnthropic()
response = model_compare(
    user_input="Compare Gemini 3 Flash and Claude Code on a text-to-image generation task",
    llm=llm
)

Or to use Google Generative AI:

from langchain_google_genai import ChatGoogleGenerativeAI
from model_compare import model_compare

llm = ChatGoogleGenerativeAI()
response = model_compare(
    user_input="Compare Gemini 3 Flash and Claude Code on a text-to-image generation task",
    llm=llm
)

You can also pass your own API key via the environment variable LLM7_API_KEY or directly as an argument.

Rate Limits

The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits, you can pass your own API key via environment variable LLM7_API_KEY or directly as an argument.

Getting Started

You can get a free API key by registering at https://token.llm7.io/

Contributing

Please submit issues and pull requests to https://github.com/chigwell/model-compare

Author

Eugene Evstafev ([e-mail])

License

This package is licensed under the MIT License.