How does QuickCompare work?

You upload your dataset in CSV or JSONL format, select which models to compare, and run evaluations. The tool returns a side-by-side view of quality, cost, and speed metrics.

Which LLMs are supported?

QuickCompare supports models from OpenAI, Anthropic, Google, and many others – over 50 models in total.

Is there a free tier or trial?

Yes, QuickCompare offers a free tier to get started, and Product Hunt users receive an additional $10 in credits.

Can I use my own evaluation criteria?

Yes, you can use static metrics like exact match or ROUGE, as well as LLM-as-a-Judge for more nuanced evaluation.

How long does a typical comparison take?

It depends on the number of models and data size, but QuickCompare is designed for fast inference, typically completing within minutes.

QuickCompare by Trismik

Compare LLMs on your data, measure, and pick the best.

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What is QuickCompare by Trismik

QuickCompare by Trismik is a model evaluation and selection tool that lets teams upload their own data and compare over 50 LLMs side by side on quality, cost, and speed. It moves beyond generic benchmarks to provide real-world performance insights, enabling data-driven decisions for deployment or fine-tuning.

Key Features

Upload your own data (CSV or JSONL) and evaluate on your specific prompts

Compare 50+ models from OpenAI, Anthropic, Google, and more in one workflow

Side-by-side metrics: quality (static metrics and LLM-as-Judge), cost, and latency

Fast inference with Jinja template support for dynamic prompt generation

Use Cases

ML engineers selecting the best LLM for a production application based on real data
Product teams evaluating cost-speed-quality trade-offs before deploying a chatbot or content generator
Researchers testing model performance on domain-specific tasks like medical or legal text

Why do startups need this tool?

Startups building LLM-powered features often face high costs and performance surprises. QuickCompare eliminates guesswork by providing real performance data on their own data, helping them select the most cost-effective and reliable model. This reduces wasted engineering time and ensures a better user experience from day one.