Which AI model actually works best for your needs? Upload your own data and evaluate any Gooey.AI workflow, LLM or AI model against any other. Great for large data sets, AI model evaluation, task automation, parallel processing and automated testing. To get started, paste in a Gooey.AI workflow, upload a CSV of your test data (with header names!), check the mapping of headers to workflow inputs and tap Submit. More tips in the Details below.

Here we take an input sample of Kikuyu phrases (and their expert transcription and English translations) and compare the top Speech Reco and Translation models. As of Mar 25, 2025, we've found that Meta's MMS-Large works best for Kikuyu with Ghana NLP's Kikuyu to English translation services.

. Which AI model actually works best for your needs?
Upload your own data and evaluate any Gooey.AI workflow, LLM or AI model against any other.
Great for large data sets, AI model evaluation, task automation, parallel processing and automated testing.
To get started, paste in a Gooey.AI workflow, upload a CSV of your test data (with header names!), check the mapping of headers to workflow inputs and tap Submit.
More tips in the Details below.

This run is useful if you are only interested in Regression testing, Monitoring and Evaluation, Observability and Bugs.

Compare Swahili Speech Recognition and Translation to English with this run. We have 10 variations in this. Including the latest Seamless M4T v2, MMS, Azure, Whisper v3 and more.

Which AI model actually works best for your needs? Upload your own data and evaluate any Gooey.AI workflow, LLM or AI model against any other. Great for large data sets, AI model evaluation, task automation, parallel processing and automated testing. To get started, paste in a Gooey.AI workflow, upload a CSV of your test data (with header names!), check the mapping of headers to workflow inputs and tap Submit. More tips in the Details below.

Which AI model actually works best for your needs? Upload your own data and evaluate any Gooey.AI workflow, LLM or AI model against any other. Great for large data sets, AI model evaluation, task automation, parallel processing and automated testing. To get started, paste in a Gooey.AI workflow, upload a CSV of your test data (with header names!), check the mapping of headers to workflow inputs and tap Submit. More tips in the Details below.

Which AI model actually works best for your needs? Upload your own data and evaluate any Gooey.AI workflow, LLM or AI model against any other. Great for large data sets, AI model evaluation, task automation, parallel processing and automated testing. To get started, paste in a Gooey.AI workflow, upload a CSV of your test data (with header names!), check the mapping of headers to workflow inputs and tap Submit. More tips in the Details below.

In this bulk run example, we compare 3 different Gooey.AI speech recognition + translations workflows (https://gooey.ai/speech), each of which uses a different AI speech model:

Whisper v2
Google Chirp
Azure

We then evaluate them at https://gooey.ai/eval/ (where you can see what works best....)

In this bulk run example, we compare 6 different Gooey.AI speech recognition + translations workflows (https://gooey.ai/speech), each of which uses a different AI speech model:
Whisper Telugu Bhashini
Whisper v2
Google Chirp
Azure
Meta Seemless 4MT
AI4Bharat.org Conformer Hindi

We then evaluate them at https://gooey.ai/eval/ (where you can see what works best....)

In this bulk run example, we compare 5 different Gooey.AI speech recognition + translations workflows (https://gooey.ai/speech), each of which uses a different AI speech model:
Whisper Telugu Bhashini
Whisper v2
Google Chirp
Azure
Meta Seemless 4MT

We then evaluate them at https://gooey.ai/eval/?example_id=lc1f4ka1 (where you can see what works best....)

Testing AI bots and copilots (https://gooey.ai/copilot) in practice is complex because the answer a bot will give is affected not just by the user's last question but also by the conversation history that preceded that question. Hence, this bulk example shows you how to format your CSV to include a bot/user conversation history before each question is asked. The output columns also show which knowledge documents were returned as results that the bot used in trying to formulate its answer.

Testing AI bots and copilots (https://gooey.ai/copilot) in practice is complex because the answer a bot will give is affected not just by the user's last question but also by the conversation history that preceded that question. Hence, this bulk example shows you how to format your CSV to include a bot/user conversation history before each question is asked. The output columns also show which knowledge documents were returned as results that the bot used in trying to formulate its answer.

This example takes a CSV with URLs and image descriptions and then runs our artistic QR Code maker over each of them, generating two QR codes for each URL + description pair. This is great if you want to create 100s of art QR Codes in one huge batch.

This bulk run compares two different AI Image editor workflows - one that takes profile pictures and renders them in an anime vs Pixar style. Swap out the CSV with your own list of profile photo URLs to test even more. Swap out the workflow URLs with different Image prompts to test out new styles.

About the Bulk Runner:
Which AI model actually works best for your needs? Upload your own data and evaluate any Gooey.AI workflow, LLM or AI model against any other. Great for large data sets, AI model evaluation, task automation, parallel processing and automated testing. To get started, paste in a Gooey.AI workflow, upload a CSV of your test data (with header names!), check the mapping of headers to workflow inputs and tap Submit. More tips in the Details below.

This example takes a CSV with URLs and image descriptions and then runs our artistic QR Code maker over each of them, generating two QR codes for each URL + description pair. This is great if you want to create 100s of art QR Codes in one huge batch.