Select two models to compare their capabilities head-to-head.
SUPA's LLM Comparison Tool provides the insights you need to identify the best model for your specific use case. All comparison data will be open sourced to support LLM research and benchmarking.
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If there's one thing that makes SUPA stand out, it's their commitment to providing consistent support throughout the data labeling process. The team actively and efficiently engaged with us to ensure any ambiguity in the dataset was cleared up.
Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.
I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.
The annotators were really quick. I would upload and 5 minutes later - 10 images done. I checked 5 minutes later - 100 images done.
The team at [SUPA] has been very professional & easy to work with since we started our collaboration in 2019. They've provided us with good quality judgments to train, tune, and validate our Search & Recommendations models.
We're excited to introduce a new open-source tool that empowers users to evaluate and compare LLM performance firsthand. While LLMs have shown immense potential across various applications, the ability to meaningfully assess their capabilities—especially in specific domains and use cases—has remained a challenge.
Our tool aims to bridge this gap by providing a transparent, hands-on comparison environment where users can pit different models against each other and draw their own conclusions.
What sets our tool apart is its inclusive approach to model evaluation. We've noticed a gap in representation, particularly for Southeast Asian LLMs, in existing comparison platforms. Our tool will include models from the SEA region alongside global options, ensuring a more comprehensive evaluation landscape. We believe this diversity is crucial for users working across different linguistic and cultural contexts.
In line with our commitment to advancing the field, all data generated through our platform will be made publicly available for research purposes and model fine-tuning.
Made with love by the SUPA Team
“Aerobotics has partnered with SUPA for data annotation since 2020. Their dependable and collaborative approach has made them an integral part of our AI journey."
Read more about how SUPA helped Aerobotics scale up to 170% in a week at 97% accuracy
"SUPA's labeling support has been instrumental in our accelerated progress."
Read more about SUPA's ongoing collaboration with Greyparrot
“What do we like best about SUPA? The quality and thoughtful approach to requirements elicitation."
Read more about SUPA’s fast delivery of segmentation data with over 99% accuracy
"The best interface for self-service labeling. Everything from uploading data to seeing it labeled in real time was really cool. Very impressed with how the platform delivered exactly the label quality we needed."
"The annotators were really quick. Checked 5 minutes after uploading data - 100 images done."
"Real-time view of the generated labels gave me instant feedback about the label quality. No more waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's FiftyOne."
"SUPA has provided us with good quality judgments to train, tune, and validate our Search & Recommendations models. Before this, it was difficult to scale our labeling workflows."
"What stands out is their commitment to providing consistent support throughout the data labeling process. The team actively engaged with us to clear up any ambiguity in the dataset including edge cases we didn't foresee."
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