Models and regional availability for Cortex AI Functions¶

Snowflake Cortex AI Functions support a range of language and embedding models with varying capabilities, context-window sizes, and regional availability. Use this reference to choose a model for your workload, check its input and output limits, confirm that it’s available in your region, and identify previous model versions that are still supported.

Choosing a model¶

The Snowflake Cortex AI_COMPLETE function supports multiple models of varying capability, latency, and cost. These models have been carefully chosen to align with common customer use cases. To achieve the best performance per credit, choose a model that’s a good match for the content size and complexity of your task. Here are brief overviews of the available models.

Large models¶

If you’re not sure where to start, try the most capable models first to establish a baseline to evaluate other models. claude-opus-4-7 and gemini-3.1-pro are the most capable models offered by Snowflake Cortex, and will give you a good idea what a state-of-the-art model can do.

• claude-opus-4-7 is Anthropic’s flagship Claude Opus model, built for advanced reasoning, long-running agentic workflows, and complex coding tasks. With a 1,000,000-token context window and up to 128,000 output tokens, it can analyze large document collections and produce detailed responses in a single call.

Medium models¶

• Claude 4-6 Sonnet is a leader in general reasoning and multimodal capabilities. It outperforms its predecessors in tasks that require reasoning across different domains and modalities. You can use its large output capacity to get more information from either structured or unstructured queries. Its reasoning capabilities and large context windows make it well-suited for agentic workflows.
• llama3.1-70b is an open source model that demonstrates state-of-the-art performance ideal for chat applications, content creation, and enterprise applications. It is a highly performant, cost effective model that enables diverse use cases with a context window of 128K. llama3-70b is still supported and has a context window of 8K.
• snowflake-llama3.3-70b is a model derived from the open source llama3.3 model. It uses the SwiftKV optimizations developed by the Snowflake AI research team to deliver up to a 75% inference cost reduction. SwiftKV achieves higher throughput performance with minimal accuracy loss.
• mixtral-8x7b is ideal for text generation, classification, and question answering. Mistral models are optimized for low latency with low memory requirements, which translates into higher throughput for enterprise use cases.

Small models¶

• claude-haiku-4-5 is Anthropic’s fast and cost-efficient Claude Haiku model, optimized for low-latency, high-throughput workloads. With a 200,000-token context window and up to 64,000 output tokens, it’s well-suited for simple summarization, classification, and question-answering tasks where speed and cost matter more than top-tier reasoning.
• openai-gpt-5-mini is OpenAI’s small, fast variant of the GPT-5 family, designed for low-latency tasks where quick responses matter more than top-tier reasoning. It has a 272,000-token context window and up to 8,192 output tokens. To use it, your account must have cross-region inference enabled (cross-cloud or from Azure US).
• llama3.1-8b is ideal for tasks that require low to moderate reasoning. It’s a light-weight, ultra-fast model with a context window of 128K. llama3-8b provides a smaller context window and relatively lower accuracy.
• mistral-7b is ideal for your simplest summarization, structuration, and question answering tasks that need to be done quickly. It offers low latency and high throughput processing for multiple pages of text with its 32K context window.

The following table provides information on how popular models perform on various benchmarks, including the models offered by Snowflake Cortex AI_COMPLETE as well as a few other popular models.

Model restrictions¶

Models used by Snowflake Cortex have limitations on size as described in the table below. Sizes are given in tokens. According to industry estimates, tokens generally represent about four characters of text, so the number of words corresponding to a token limit is less than the number of tokens. Inputs exceeding the context window limit result in an error. Output that exceed the context window limit is truncated.

The maximum size of the output that a model can produce is limited by the following:

• The model’s output token limit.
• The space available in the context window after the model consumes the input tokens.

For example, claude-sonnet-4-6 has a context window of 1,000,000 tokens. If 100,000 tokens are used for the input, the model can generate up to 8,192 tokens. However, if 195,000 tokens are used as input, then the model can only generate up to 5,000 tokens for a total of 200,000 tokens.

Regional availability¶

Snowflake Cortex AI functions are available in the following regions. If your region is not listed for a particular function, use cross-region inference.

***** Indicates a preview function or model. Preview features are not suitable for production workloads.

The following Snowflake Cortex AI functions and models are available in the following extended regions.

The following table lists availability of legacy models. These models have not been deprecated and can still be used. However, Snowflake recommends newer models for new development.

Legacy

Previous model versions¶

The Snowflake Cortex AI_COMPLETE and COMPLETE functions also supports the following older model versions. We recommend using the latest model versions instead of the versions listed in this table.