Claude 3.5 Sonnet excels at document understanding with an impressive 90.3% on DocVQA benchmark, hence making it an optimal choice for extracting information from financial statements, legal contracts, and compliance documentation. Pixtral Large stands out with exceptional chart analysis (88.1% ChartQA) and mathematical reasoning (69.4% Mathvista), perfect for financial reports and manufacturing specification measurements. GPT-4.1 (coming soon) offers industry-leading image understanding benchmarks like MMMU (74.8%) for diverse business imagery needs like visual question answering from diagrams and maps. All these models operate directly within the Snowflake environment—no complex external integrations are required.
Flexible audio transcription: Separate from Cortex AI’s native multimodal capabilities, customers have the ability to bring any modality, including audio processing, to Snowflake using Snowpark Container Services. Snowpark Container Services offers managed infrastructure for containerized applications that empowers developers to deploy audio transcription models at scale.
Using Snowpark Container Services, you have the flexibility to deploy and optimize models like OpenAI Whisper, Nvidia Canary, or Nvidia Parakeet on Snowflake based on your specific needs. Customers often select their preferred model based on Word Error Rate (WER) but also based on individual model features like multilingual support, performance in challenging environments like call centers, or resource efficiency. Snowflake’s secure, efficient, environment allows you to run the model of your choice, offering a great combination of flexibility, power, and trust.
State-of-the-art entity sentiment analysis: Beyond audio processing, Cortex AI delivers sophisticated text analytics capabilities for deriving insights from diverse textual sources. Whether analyzing transcribed customer conversations, social media posts, product reviews, or other text data, our state-of-the-art entity sentiment analysis offers a nuanced understanding of expressed opinions.
Snowflake’s aspect-based sentiment analysis sets a new quality standard in the industry, providing superior sentiment classification compared to leading large language models based on the benchmark listed below. Specifically, Cortex AI Entity_Sentiment enables the extraction of nuanced insights from text by analyzing sentiment toward specific entities, rather than relying only on overall Positive or Negative classification. Cortex AI Entity Sentiment is up to 45% more cost efficient than prompting a large model like GPT-4o for substantially higher sentiment accuracy. Entity_Sentiment effectively handles complex sentiment expressions, including mixed and unknown sentiments, facilitating the analysis of relative emotion in product reviews or call transcripts.
