> ## Documentation Index > Fetch the complete documentation index at: https://docs.snowflake.com/llms.txt > Use this file to discover all available pages before exploring further. # Models and regional availability for Cortex AI Functions This feature is not available in the People's Republic of China. - [Cortex AI Functions](/user-guide/snowflake-cortex/aisql) - [Cross-region inference](/user-guide/snowflake-cortex/cross-region-inference) - [SHOW CORTEX BASE MODELS](/sql-reference/sql/show-cortex-base-models) 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, understand [legacy and end-of-life access](#label-cortex-model-lifecycle), 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](/user-guide/snowflake-cortex/aisql-cost), 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-5` and `gemini-3.1-pro` are the most capable models offered by Snowflake Cortex, and will give you a good idea of what a state-of-the-art model can do. - `claude-opus-5-5` (Public Preview) is Anthropic's latest Claude Opus model, with further gains in coding, long-horizon agentic work, and professional tasks like document drafting and data analysis. 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. It requires [cross-region inference](#label-use-cross-region-inference). - `claude-opus-5` is Anthropic's flagship Claude Opus model, with improved performance on coding, long-horizon agentic work, and professional tasks like document drafting and data analysis. 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. - `claude-opus-4-8` is Anthropic's 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. - `gemini-3.1-pro` (Public Preview) is Google's most capable Gemini model available in Snowflake Cortex, suited for advanced reasoning and multimodal tasks. It supports up to 64,000 output tokens and requires [cross-region inference](#label-use-cross-region-inference). ### Medium models - `claude-sonnet-5` is Anthropic's latest Claude Sonnet model, with strong performance on coding, tool use, and agentic tasks at lower latency and cost than the Claude Opus models. With a 1,000,000-token context window and up to 64,000 output tokens, it's well-suited for agentic workflows and for analyzing large inputs in a single call. - `claude-sonnet-4-6` 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. - `llama3.3-70b` 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. ### 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](#label-use-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, structuring, 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 | Context Window (Tokens) | MMLU (Reasoning) | HumanEval (Coding) | GSM8K (Arithmetic Reasoning) | Spider 1.0 (SQL) | | -------------------------------------------------------------------------------------------------- | ----------------------- | ---------------- | ------------------ | ---------------------------- | ---------------- | | [GPT-4o](https://openai.com/index/hello-gpt-4o/) | 128,000 | 88.7 | 90.2 | 96.4 | - | | [llama3.1-70b](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md) | 128,000 | 86 | 80.5 | 95.1 | - | | [mistral-large2](https://mistral.ai/news/mistral-large-2407/) | 128,000 | 84 | 92 | 93 | - | | [llama3.1-8b](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md) | 128,000 | 73 | 72.6 | 84.9 | - | | [mixtral-8x7b](https://mistral.ai/news/mixtral-of-experts/) | 32,000 | 70.6 | 40.2 | 60.4 | - | | [mistral-7b](https://mistral.ai/news/announcing-mistral-7b/) | 32,000 | 62.5 | 26.2 | 52.1 | - | ## 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 exceeds 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. In the AWS AP Southeast 2 (Sydney) region: - the context window for `llama3-8b` and `mistral-7b` is 4,096 tokens. - the context window for `llama3.1-8b` is 16,384 tokens. - the context window for the Snowflake managed model from the SUMMARIZE function is 4,096 tokens. In the AWS Europe West 1 (Ireland) region: - the context window for `llama3.1-8b` is 16,384 tokens. - the context window for `mistral-7b` is 4,096 tokens. | Function | Model | Context window (tokens) | Max output (tokens) | | ---------------- | -------------------------- | ------------------------------------------------ | ------------------- | | AI_COMPLETE | `llama4-maverick` | 128,000 | 8,192 | | | `claude-sonnet-5` | 1,000,000 | 64,000 | | | `claude-sonnet-4-6` | 1,000,000 | 64,000 | | | `claude-opus-5-5` | 1,000,000 | 128,000 | | | `claude-opus-5` | 1,000,000 | 128,000 | | | `claude-opus-4-8` | 1,000,000 | 128,000 | | | `claude-opus-4-7` | 1,000,000 | 128,000 | | | `claude-opus-4-6` | 1,000,000 | 128,000 | | | `claude-sonnet-4-5` | 200,000 | 64,000 | | | `claude-haiku-4-5` | 200,000 | 64,000 | | | `claude-opus-4-5` | 200,000 | 64,000 | | | `gemini-3.1-pro` | 1,000,000 | 64,000 | | | `gemini-3.5-flash` | 1,000,000 | 64,000 | | | `gemini-3.8-flash` | 1,000,000 | 64,000 | | | `gemini-3.7-flash` | 1,000,000 | 64,000 | | | `gemini-3.1-flash-lite` | 1,000,000 | 64,000 | | | `mistral-large2` | 128,000 | 8,192 | | | `mistral-large3` | 256,000 | 32,768 | | | `qwen3-32b` | 128,000 | 8,192 | | | `qwen3-next-80b-a3b` | 128,000 | 8,192 | | | `qwen3-vl-235b-a22b` | 128,000 | 8,192 | | | `openai-gpt-5.1` | 272,000 | 8,192 | | | `openai-gpt-5.4-mini` | 400,000 | 128,000 | | | `openai-gpt-5.4-nano` | 400,000 | 128,000 | | | `openai-gpt-5` | 272,000 | 8,192 | | | `openai-gpt-5-mini` | 272,000 | 8,192 | | | `openai-gpt-5-nano` | 272,000 | 8,192 | | | `openai-gpt-4.1` | 128,000 | 32,000 | | | `mixtral-8x7b` | 32,000 | 8,192 | | | `llama3.1-8b` | 128,000 | 8,192 | | | `llama3.1-70b` | 128,000 | 8,192 | | | `llama3.3-70b` | 128,000 | 8,192 | | | `mistral-7b` | 32,000 | 8,192 | | EMBED_TEXT_768 | `e5-base-v2` | 512 | n/a | | | `snowflake-arctic-embed-m` | 512 | n/a | | EMBED_TEXT_1024 | `nv-embed-qa-4` | 512 | n/a | | | `multilingual-e5-large` | 512 | n/a | | | `voyage-multilingual-2` | 32,000 | n/a | | AI_EXTRACT | `arctic-extract` | 128,000 | 51,200 | | AI_FILTER | Snowflake managed model | 128,000 | n/a | | AI_CLASSIFY | Snowflake managed model | 128,000 | n/a | | AI_AGG | Snowflake managed model | 128,000 per row can be used across multiple rows | 8,192 | | AI_SENTIMENT | Snowflake managed model | 2,048 | n/a | | AI_SUMMARIZE_AGG | Snowflake managed model | 128,000 per row can be used across multiple rows | 8,192 | | ENTITY_SENTIMENT | Snowflake managed model | 2,048 | n/a | | EXTRACT_ANSWER | Snowflake managed model | 2,048 for text 64 for question | n/a | | SENTIMENT | Snowflake managed model | 512 | n/a | | SUMMARIZE | Snowflake managed model | 32,000 | 4,096 | | TRANSLATE | Snowflake managed model | 100,000 (input only) | 100,000 | ## Model lifecycle: legacy and end-of-life Snowflake retires Cortex models in two stages: **legacy**, then **end-of-life**. A model marked legacy is not yet unavailable to every account. Access depends on whether your **account** already used that model. ### Legacy After a model's legacy date: - If your account used the model anywhere before the legacy date, the account can continue to use it in every supported application until the model's end-of-life date. Supported applications include AI_COMPLETE, CORTEX.COMPLETE, the Agents API, Cortex Inference, and %sf-intelligence%. - If your account had not used the model anywhere before the legacy date, the account can't start using it. Queries and API calls that name the model fail. Any usage of the model in the account before the legacy date means that the account can continue to use the model until end-of-life, including in applications that had not called that model before. Snowflake recommends that you migrate to a supported model before the end-of-life date, even if your account can still call the legacy model. ### End-of-life After a model's end-of-life date, the model is no longer available to any account, regardless of prior usage. Queries and API calls that name the model fail. Use [SHOW CORTEX BASE MODELS](/sql-reference/sql/show-cortex-base-models) to see each model's `lifecycle_status`, `legacy_date`, and `eol_date`. Deprecation dates for specific models are announced in unbundled behavior change releases, such as [Cortex model deprecations for August 2026](/release-notes/bcr-bundles/un-bundled/bcr-august-model-deprecations). ## 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](#label-use-cross-region-inference). - The AI_COUNT_TOKENS function is available in all regions for any model, but the models themselves are available only in the regions specified in the tables below. **Cross-Region:** The following functions and models are available in any region via [cross-region inference](#label-use-cross-region-inference).

Function | Model Cross Cloud (Any Region) AWS US (Cross-Region) AWS US Commercial Gov (Cross-Region) AWS US FedRAMP High Plus (Cross-Region) AWS US DoD (Cross-Region) AWS EU (Cross-Region) AWS APJ (Cross-Region) AWS JP (Cross-Region) AWS AU (Cross-Region) Azure US (Cross-Region) Azure US FedRAMP High Plus (Cross-Region)† Azure EU (Cross-Region) Google Cloud US (Cross-Region) AI_COMPLETE `claude-fable-5-1` \*\* \*\* `claude-fable-5` \*\* \*\* `claude-sonnet-5` %cm% %cm% %cm% %cm% %cm% `claude-sonnet-5-5` \* `claude-opus-5-5` \* \* \* \* \* \* `claude-opus-5` %cm% %cm% %cm% `claude-opus-4-8` %cm% %cm% %cm% `claude-opus-4-7` %cm% %cm% %cm% `claude-sonnet-4-6` %cm% %cm% %cm% %cm% %cm% %cm% %cm% `claude-opus-4-6` %cm% %cm% %cm% %cm% %cm% `claude-sonnet-4-5` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% `claude-opus-4-5` %cm% %cm% %cm% `claude-haiku-4-5` %cm% %cm% %cm% %cm% %cm% %cm% %cm% `claude-4-sonnet [legacy]` %cm% %cm% %cm% %cm% %cm% %cm% `gemini-3.1-pro` \* `gemini-3.5-flash` %cm% `gemini-3.1-flash-lite` %cm% `gemini-3.8-flash` %cm% %cm% `gemini-3.7-flash` \* `grok-4.6` \* \* `llama4-maverick [legacy]` %cm% %cm% `llama3.1-8b` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% `llama3.1-70b [legacy]` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% `llama3.3-70b` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% `openai-gpt-6-astra` \*\* `openai-gpt-6-luna` \*\* `openai-gpt-6-sol` \*\* `openai-gpt-6.1-sol` \*\* `openai-gpt-5.6-luna` \*\* `openai-gpt-5.6-sol` \*\* `openai-gpt-5.6-terra` \*\* `openai-gpt-5.5` \*\* `openai-gpt-5.4` %cm% %cm% %cm% %cm% %cm% † %cm% `openai-gpt-5.2` %cm% %cm% `openai-gpt-5.1` %cm% %cm% %cm% `openai-gpt-5.4-mini` %cm% %cm% `openai-gpt-5.4-nano` %cm% %cm% `openai-gpt-5` %cm% %cm% %cm% `openai-gpt-5-mini` %cm% %cm% `openai-gpt-5-nano` %cm% %cm% `openai-gpt-4.1 [legacy]` %cm% %cm% `mistral-large2 [legacy]` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% `mistral-large3` \* \* \* \* `kimi-k3` \* `qwen3-32b` %cm% %cm% %cm% `qwen3-next-80b-a3b` %cm% %cm% %cm% `qwen3-vl-235b-a22b` %cm% %cm% %cm% `mixtral-8x7b [legacy]` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% `mistral-7b [legacy]` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_EMBED `e5-base-v2` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% `snowflake-arctic-embed-m` %cm% %cm% %cm% %cm% %cm% %cm% %cm% `snowflake-arctic-embed-m-v1.5` %cm% %cm% %cm% %cm% %cm% %cm% %cm% `snowflake-arctic-embed-l-v2.0` %cm% %cm% %cm% %cm% %cm% %cm% %cm% `snowflake-arctic-embed-l-v2.0-8k` %cm% %cm% %cm% %cm% %cm% %cm% %cm% `nv-embed-qa-4` %cm% %cm% `multilingual-e5-large` %cm% %cm% %cm% %cm% %cm% %cm% %cm% `voyage-multilingual-2` %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_CLASSIFY TEXT %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_CLASSIFY IMAGE %cm% %cm% %cm% AI_EXTRACT %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_FILTER TEXT %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_FILTER IMAGE %cm% %cm% %cm% AI_AGG %cm% %cm% %cm% %cm% %cm% %cm% AI_REDACT %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_SENTIMENT %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_SIMILARITY TEXT %cm% %cm% %cm% %cm% %cm% %cm% AI_SIMILARITY IMAGE %cm% %cm% %cm% %cm% AI_SUMMARIZE_AGG %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_TRANSCRIBE %cm% %cm% %cm% %cm% SENTIMENT %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% ENTITY_SENTIMENT %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% EXTRACT_ANSWER %cm% %cm% %cm% %cm% %cm% %cm% %cm% SUMMARIZE %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% TRANSLATE %cm% %cm% %cm% %cm% %cm% %cm% %cm% %cm% AI_TRANSLATE %cm% %cm% %cm% %cm% %cm% %cm% %cm%

**North America:** The following functions and models are available natively in North American regions.

Function | Model AWS US West 2 (Oregon) AWS US East 1 (N. Virginia) AWS US East (Commercial Gov - N. Virginia) Azure East US 2 (Virginia) Azure East US (Virginia) Azure West US (Washington) Azure West US 3 (Arizona) Azure North Central US (Illinois) Azure South Central US (Texas) AI_COMPLETE `llama4-maverick [legacy]` %cm% `llama3.1-8b` %cm% %cm% %cm% %cm% `llama3.1-70b [legacy]` %cm% %cm% %cm% %cm% `llama3.3-70b` %cm% `openai-gpt-4.1 [legacy]` %cm% `mistral-large2 [legacy]` %cm% %cm% %cm% %cm% `mixtral-8x7b [legacy]` %cm% %cm% %cm% %cm% `mistral-7b [legacy]` %cm% %cm% %cm% %cm% AI_EMBED `e5-base-v2` %cm% %cm% %cm% %cm% `snowflake-arctic-embed-m` %cm% %cm% %cm% %cm% `snowflake-arctic-embed-m-v1.5` %cm% %cm% %cm% %cm% `snowflake-arctic-embed-l-v2.0` %cm% %cm% %cm% %cm% `snowflake-arctic-embed-l-v2.0-8k` %cm% %cm% %cm% %cm% `nv-embed-qa-4` %cm% `multilingual-e5-large` %cm% %cm% %cm% %cm% `voyage-multilingual-2` %cm% %cm% %cm% %cm% AI_CLASSIFY TEXT %cm% %cm% %cm% AI_CLASSIFY IMAGE %cm% %cm% AI_EXTRACT %cm% %cm% %cm% %cm% %cm% AI_FILTER TEXT %cm% %cm% %cm% AI_FILTER IMAGE %cm% %cm% AI_AGG %cm% %cm% %cm% AI_REDACT %cm% %cm% %cm% %cm% AI_SIMILARITY TEXT %cm% %cm% %cm% AI_SIMILARITY IMAGE %cm% %cm% AI_SUMMARIZE_AGG %cm% %cm% %cm% AI_TRANSCRIBE %cm% %cm% %cm% SENTIMENT %cm% %cm% %cm% %cm% ENTITY_SENTIMENT %cm% %cm% %cm% %cm% EXTRACT_ANSWER %cm% %cm% %cm% %cm% SUMMARIZE %cm% %cm% %cm% %cm% TRANSLATE %cm% %cm% %cm% %cm%

**Europe:** The following functions and models are available natively in European regions. | Function \| Model | AWS Europe Central 1 (Frankfurt) | AWS Europe West 1 (Ireland) | AWS Europe West 2 (London) | Azure West Europe (Netherlands) | | ------------------------------------- | -------------------------------- | --------------------------- | -------------------------- | ------------------------------- | | AI_COMPLETE | | | | | | \| `claude-sonnet-4-6` | | | %cm% | | | \| `claude-opus-4-6` | | | %cm% | | | \| `claude-4-sonnet [legacy]` | | | | | | \| `llama4-maverick [legacy]` | | | | | | \| `llama3.1-8b` | %cm% | %cm% | | %cm% | | \| `llama3.1-70b [legacy]` | %cm% | %cm% | | %cm% | | \| `llama3.3-70b` | | | | | | \| `openai-gpt-4.1 [legacy]` | | | | | | \| `mistral-large2 [legacy]` | %cm% | %cm% | | %cm% | | \| `mixtral-8x7b [legacy]` | %cm% | %cm% | | %cm% | | \| `mistral-7b [legacy]` | %cm% | %cm% | | %cm% | | | | | | | | AI_EMBED | | | | | | \| `e5-base-v2` | %cm% | | | %cm% | | \| `snowflake-arctic-embed-m` | %cm% | %cm% | | %cm% | | \| `snowflake-arctic-embed-m-v1.5` | %cm% | %cm% | | %cm% | | \| `snowflake-arctic-embed-l-v2.0` | %cm% | %cm% | | %cm% | | \| `snowflake-arctic-embed-l-v2.0-8k` | %cm% | %cm% | | %cm% | | \| `nv-embed-qa-4` | | | | | | \| `multilingual-e5-large` | %cm% | %cm% | | %cm% | | \| `voyage-multilingual-2` | %cm% | %cm% | | %cm% | | | | | | | | AI_CLASSIFY TEXT | %cm% | %cm% | | %cm% | | AI_CLASSIFY IMAGE | %cm% | | | | | AI_EXTRACT | %cm% | %cm% | | %cm% | | AI_FILTER TEXT | %cm% | %cm% | | %cm% | | AI_FILTER IMAGE | %cm% | | | | | AI_AGG | %cm% | %cm% | | %cm% | | AI_REDACT | %cm% | %cm% | | %cm% | | AI_SIMILARITY TEXT | %cm% | %cm% | | %cm% | | AI_SIMILARITY IMAGE | %cm% | | | | | AI_SUMMARIZE_AGG | %cm% | %cm% | | %cm% | | AI_TRANSCRIBE | %cm% | | | | | SENTIMENT | %cm% | %cm% | | %cm% | | ENTITY_SENTIMENT | %cm% | | | %cm% | | EXTRACT_ANSWER | %cm% | %cm% | | %cm% | | SUMMARIZE | %cm% | %cm% | | %cm% | | TRANSLATE | %cm% | %cm% | | %cm% | **Asia-Pacific:** The following functions and models are available natively in Asia-Pacific regions:

Function | Model AWS AP Southeast 2 (Sydney) AWS AP Northeast 1 (Tokyo) AI_COMPLETE `claude-4-sonnet [legacy]` `llama4-maverick [legacy]` `llama3.1-8b` %cm% %cm% `llama3.1-70b [legacy]` %cm% %cm% `llama3.3-70b` `openai-gpt-4.1 [legacy]` `mistral-large2 [legacy]` %cm% %cm% `mixtral-8x7b [legacy]` %cm% %cm% `mistral-7b [legacy]` %cm% %cm% AI_EMBED `e5-base-v2` %cm% %cm% `snowflake-arctic-embed-m` %cm% %cm% `snowflake-arctic-embed-m-v1.5` %cm% %cm% `snowflake-arctic-embed-l-v2.0` %cm% %cm% `snowflake-arctic-embed-l-v2.0-8k` %cm% %cm% `nv-embed-qa-4` `multilingual-e5-large` %cm% %cm% `voyage-multilingual-2` %cm% %cm% AI_EXTRACT %cm% %cm% AI_CLASSIFY TEXT %cm% %cm% AI_CLASSIFY IMAGE AI_FILTER TEXT %cm% %cm% AI_FILTER IMAGE AI_AGG %cm% %cm% AI_SIMILARITY TEXT %cm% %cm% AI_SIMILARITY IMAGE AI_SUMMARIZE_AGG %cm% %cm% AI_TRANSCRIBE EXTRACT_ANSWER %cm% %cm% SENTIMENT %cm% %cm% ENTITY_SENTIMENT %cm% SUMMARIZE %cm% %cm% TRANSLATE %cm% %cm%

\* Indicates a public preview function or model. Public preview features aren't suitable for production workloads. \*\* Indicates a private preview model. Private preview models are available only to accounts that Snowflake has explicitly enabled, and they aren't suitable for production workloads. † Models are available to Azure US FedRAMP High Plus accounts through cross-region inference with processing on AWS in the United States. See [US FedRAMP High and DoD gov regions](/user-guide/snowflake-cortex/cross-region-inference#label-cortex-cross-region-fedramp-dod). The following Snowflake Cortex AI functions and models are available in the following extended regions. | Function \| Model | AWS US East 2 (Ohio) | AWS CA Central 1 (Central) | AWS SA East 1 (São Paulo) | AWS Europe West 2 (London) | AWS Europe Central 1 (Frankfurt) | AWS Europe North 1 (Stockholm) | AWS AP Northeast 1 (Tokyo) | AWS AP South 1 (Mumbai) | AWS AP Southeast 2 (Sydney) | AWS AP Southeast 3 (Jakarta) | Azure South Central US (Texas) | Azure West US 2 (Washington) | Azure UK South (London) | Azure North Europe (Ireland) | Azure Switzerland North (Zürich) | Azure Central India (Pune) | Azure Japan East (Tokyo, Saitama) | Azure Southeast Asia (Singapore) | Azure Australia East (New South Wales) | Google Cloud Europe West 2 (London) | Google Cloud Europe West 4 (Netherlands) | Google Cloud US Central 1 (Iowa) | Google Cloud US East 4 (N. Virginia) | | ---------------------------------- | -------------------- | -------------------------- | ------------------------- | -------------------------- | -------------------------------- | ------------------------------ | -------------------------- | ----------------------- | --------------------------- | ---------------------------- | ------------------------------ | ---------------------------- | ----------------------- | ---------------------------- | -------------------------------- | -------------------------- | --------------------------------- | -------------------------------- | -------------------------------------- | ----------------------------------- | ---------------------------------------- | -------------------------------- | ------------------------------------ | | AI_EMBED | | | | | | | | | | | | | | | | | | | | | | | | | \| `snowflake-arctic-embed-m-v1.5` | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | | \| `snowflake-arctic-embed-m` \| | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | | \| `multilingual-e5-large` \| | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | %cm% | | AI_EXTRACT | %cm% | %cm% | %cm% | %cm% | %cm% | Cross-region only | %cm% | Cross-region only | %cm% | Cross-region only | %cm% | %cm% | Cross-region only | %cm% | Cross-region only | %cm% | %cm% | %cm% | %cm% | Cross-region only | Cross-region only | Cross-region only | Cross-region only | The following table lists in-region availability of legacy models. These models have not reached end-of-life. After the legacy date, only accounts that already used the model can continue to call it. Accounts that had not used the model can't start. Snowflake recommends newer models for new development. For the full definition, see [](#label-cortex-model-lifecycle). **Legacy** | Function (Model) | AWS US West 2 (Oregon) | AWS US East 1 (N. Virginia) | AWS Europe Central 1 (Frankfurt) | AWS Europe West 1 (Ireland) | AWS AP Southeast 2 (Sydney) | AWS AP Northeast 1 (Tokyo) | Azure East US 2 (Virginia) | Azure West Europe (Netherlands) | | ---------------- | ---------------------- | --------------------------- | -------------------------------- | --------------------------- | --------------------------- | -------------------------- | -------------------------- | ------------------------------- | | AI_COMPLETE | | | | | | | | | | \| `llama3-8b` | %cm% | %cm% | %cm% | | %cm% | %cm% | %cm% | | | \| `llama3-70b` | %cm% | %cm% | %cm% | | | %cm% | %cm% | | ## Previous model versions The Snowflake Cortex AI_COMPLETE and COMPLETE functions also support the following older model versions. We recommend using the latest model versions instead of the versions listed in this table. | Model | Context Window (Tokens) | MMLU (Reasoning) | HumanEval (Coding) | GSM8K (Arithmetic Reasoning) | Spider 1.0 (SQL) | | ---------------------------------------------------------------------- | ----------------------- | ---------------- | ------------------ | ---------------------------- | ---------------- | | [llama-2-70b-chat](https://huggingface.co/meta-llama/Llama-2-70b-chat) | 4,096 | 68.9 | 30.5 | 57.5 | - |