AI Models

Comprehensive guide to available AI models, their capabilities, and when to use them

Elementum supports a wide range of AI models across multiple providers. Models are accessed through configured AI providers — you’ll select your provider first, then choose from the models available through that provider. To use a model in your workflows, create an AI Service that references the provider and model you want.

Available Providers

• Snowflake — Claude, OpenAI (Cortex), Llama, Mistral, DeepSeek, embedding models
• OpenAI — GPT models
• Gemini — Multimodal Gemini models
• Bedrock — AWS Bedrock agents (agent orchestration only). See the Bedrock setup guide for configuration details.

Important: Models are accessed through providers you configure in Organization Settings. For example, Claude models are accessed through the Snowflake provider, not directly from Anthropic.

Model availability varies by provider configuration, account tier, and region. Not all models listed below may be available in your environment. Check your provider’s service creation screen for the models currently accessible to your organization.

Quick Reference: Model Capabilities

This table shows which capabilities each model category supports:

Model	Provider	Multimodal	Structured Output	Reasoning	Agents	Best Use Case
GPT-5 Series	OpenAI	No	Yes	Yes	Yes	Complex reasoning, demanding applications
GPT-4.1 Series	OpenAI	No	Yes	No	Yes	Production deployments, reliable automation
GPT-4o Series	OpenAI	Yes	Yes	No	Yes	General-purpose, document analysis
o3-mini / o1-mini	OpenAI	No	No	Yes (req.)	No	Mathematical reasoning, logic problems
GPT-4 / GPT-3.5	OpenAI	No	Partial	No	No	Legacy applications
Claude 4.5 Sonnet	Snowflake	No	No	No	Yes	Advanced reasoning, detailed analysis
Claude 4 Opus	Snowflake	No	No	No	Yes	Highest-capability reasoning
Claude 4 Sonnet	Snowflake	No	No	No	Yes	Balanced performance
Claude Haiku 4.5	Snowflake	No	No	No	Yes	Fast, efficient processing
Claude 3.7 / 3.5	Snowflake	No	No	No	Yes	Cost-effective reasoning
Cortex GPT-5	Snowflake	No	Yes	Yes	Yes	OpenAI through Snowflake
Cortex GPT-4.1	Snowflake	No	Yes	No	Yes	Production OpenAI via Snowflake
Cortex o4-mini	Snowflake	No	No	Yes (req.)	Yes	Reasoning through Snowflake
Gemini 3 Pro	Gemini	Yes	No	No	Yes	Latest multimodal
Gemini 2.5 Pro/Flash	Gemini	Yes	No	No	Yes	Production multimodal
Gemini 2.0 Flash	Gemini	Yes	No	No	No	Cost-effective multimodal
Gemini 1.5 Pro	Gemini	Yes	No	No	No	Established multimodal
DeepSeek R1	Snowflake	No	No	No	No	Open-source reasoning
Llama 3.3 70B	Snowflake	No	No	No	No	Open-source, balanced
Llama 3.1 Series	Snowflake	No	Partial	No	No	Open-source, structured output
Llama 3 Series	Snowflake	No	No	No	No	Open-source, function calling
Mistral Large 2	Snowflake	No	No	No	No	Multilingual, European focus
Mistral 7B / Mixtral	Snowflake	No	No	No	No	Efficient small models
Snowflake Arctic	Snowflake	No	No	No	No	Data cloud native
Arctic Embeddings	Snowflake	N/A	N/A	N/A	N/A	Semantic search

Legend:

• Multimodal: Processes text and images together
• Structured Output: Guaranteed JSON/XML format responses
• Reasoning: Advanced reasoning mode (req. = required, always on)
• Agents: Supports Elementum agent workflows

Models by Provider

Snowflake Cortex

Snowflake Cortex provides access to multiple AI model families through your Snowflake data cloud.

Data Residency: All Snowflake Cortex models run within your Snowflake environment, keeping data in your cloud.

Anthropic Claude (via Snowflake)

Claude 4.5 Sonnet

claude-sonnet-4-5

• Best for: Complex reasoning and analysis tasks
• Capabilities: Strong reasoning, nuanced understanding, extensive context windows
• Use cases: Complex research, detailed analysis, advanced automation, agent workflows
• Temperature range: 0.0 - 1.0 (default: 0.7)
• When to use: Demanding applications requiring deep understanding and analysis

Claude 4 Series

Claude 4 Opus (claude-4-opus)

• Best for: Highest-capability reasoning tasks
• Use cases: Strategic decisions, complex research, mission-critical analysis
• When to use: Tasks where quality matters more than cost

Claude 4 Sonnet (claude-4-sonnet)

• Best for: Balanced performance and cost for demanding tasks
• Use cases: Business automation, production workflows, detailed analysis
• When to use: Production workloads needing strong reasoning

Claude Haiku 4.5 (claude-haiku-4-5)

• Best for: Fast, efficient processing
• Use cases: High-volume operations, real-time interactions, simple automation
• When to use: Speed and cost-efficiency are priorities

Claude 3.7 & 3.5 Sonnet

Claude 3.7 Sonnet (claude-3-7-sonnet)

• Best for: Daily tasks requiring strong reasoning at lower cost
• Use cases: Standard business automation, customer support, content generation
• When to use: Cost-effective production deployments

Claude 3.5 Sonnet (claude-3-5-sonnet)

• Best for: Reliable production performance
• Use cases: Established workflows, production automation
• When to use: Stability and consistent performance matter

OpenAI via Cortex

Access OpenAI models through your Snowflake environment:

Cortex GPT-5 Series

openai-gpt-5 - Advanced reasoning openai-gpt-5-mini - Efficient reasoning openai-gpt-5-nano - Maximum efficiency openai-gpt-5-chat - Optimized for conversations

• Capabilities: Structured output, reasoning mode, through Snowflake
• Use cases: Complex analysis, conversations, classification, intelligence features, agents
• When to use: Need OpenAI capabilities with Snowflake data residency

Cortex GPT-4.1 & o4-mini

openai-gpt-4.1

• Production-ready OpenAI through Snowflake
• Structured output, reliable reasoning

openai-o4-mini

• Reasoning model through Snowflake
• Required temperature: 1.0 (not adjustable)
• System role not supported

When to use: Production OpenAI workloads within Snowflake environment

Open Source Models (via Snowflake)

DeepSeek R1

deepseek-r1

• Best for: Advanced reasoning with open-source flexibility
• Capabilities: Strong reasoning, open-source architecture
• Use cases: Research, academic applications, cost-conscious deployments
• When to use: Open-source requirements or research projects

Meta Llama Models

Llama 3.3 Series

• Llama 3.3 70B (llama3.3-70b): Latest generation, balanced performance

Llama 3.2 Series

• Llama 3.2 3B (llama3.2-3b): Efficient, compact
• Llama 3.2 1B (llama3.2-1b): Maximum efficiency for simple tasks

Llama 3.1 Series

• Llama 3.1 405B (llama3.1-405b): Largest, most capable
• Llama 3.1 70B (llama3.1-70b): Production-ready, structured output support
• Llama 3.1 8B (llama3.1-8b): Cost-effective, structured output support

Llama 3 Series

• Llama 3 70B (llama3-70b): Function calling, reliable
• Llama 3 8B (llama3-8b): Efficient operation

Llama 2 Series

• Llama 2 70B Chat (llama2-70b-chat): Conversational focus

When to use: Open-source requirements, cost optimization, specific model sizes

Mistral Models

Mistral Large 2 (mistral-large2)

• Advanced capabilities, multilingual support, European markets

Mistral Large (mistral-large)

• Previous generation, reliable performance

Mistral 7B (mistral-7b)

• Compact, efficient, cost-effective

Mixtral 8x7B (mixtral-8x7b)

• Mixture-of-experts architecture, balanced performance

When to use: International applications, multilingual needs, small model requirements

Other Snowflake Models

Snowflake Arctic (snowflake-arctic)

• Data cloud native processing, integrated with Snowflake infrastructure
• When to use: Data-intensive workflows within Snowflake

Gemma 7B (gemma-7b)

• Lightweight Google-developed model
• When to use: Efficient processing on smaller tasks

Jamba Instruct (jamba-instruct)

• Instruction-following optimization
• Note: Not recommended for JSON/YAML parsing

Reka Core / Flash (reka-core, reka-flash)

• Advanced processing or fast operation

Snowflake Embedding Models

• Arctic L V2.0 (snowflake-arctic-embed-l-v2.0) — High-quality embeddings for semantic search. Use for new AI search implementations.
• Arctic M V1.5 (snowflake-arctic-embed-m-v1.5) — Balanced performance and quality. Use for production search systems.

OpenAI Direct

Access OpenAI models directly through OpenAI API.

GPT-5 Series - Latest Generation

GPT-5 (gpt-5)

• Best for: Complex reasoning and demanding applications
• Capabilities: Structured output, reasoning mode, advanced problem-solving
• Use cases: Complex analysis, strategic planning, research tasks

GPT-5 Mini (gpt-5-mini)

• Best for: Daily reasoning at lower cost
• Capabilities: Structured output, reasoning mode, balanced performance
• Use cases: Standard business logic, moderate analysis, automation

GPT-5 Nano (gpt-5-nano)

• Best for: Simple reasoning requiring efficiency
• Capabilities: Structured output, reasoning mode, cost-effective
• Use cases: Basic classification, simple analysis, high-volume operations

GPT-5.1 (gpt-5.1)

• Best for: Enhanced reasoning with improved accuracy
• Use cases: Business intelligence, detailed analysis, critical decisions

GPT-5.2 (gpt-5.2)

• Best for: Highest-tier reasoning
• Use cases: Complex problem-solving, research, mission-critical applications

All GPT-5 models support: Prompts, conversations, classification, intelligence features, agents

GPT-4.1 Series - Production Ready

GPT-4.1 (gpt-4.1)

• Best for: Production applications requiring consistent performance
• Capabilities: Structured output, reliable reasoning
• Use cases: Customer-facing applications, production workflows

GPT-4.1 Mini (gpt-4.1-mini)

• Best for: Cost-effective production deployments
• Use cases: High-volume automation, chatbots, content generation

GPT-4.1 Nano (gpt-4.1-nano)

• Best for: Maximum efficiency for simple tasks
• Use cases: Real-time interactions, simple classification, quick responses

All GPT-4.1 models support: Prompts, conversations, classification, intelligence features, agents

GPT-4o Series - Optimized

GPT-4o (gpt-4o)

• Best for: Balanced performance and capability
• Capabilities: Structured output, multimodal support (text + images)
• Use cases: General-purpose applications, document analysis, versatile automation
• Supports: Prompts, conversations, email analysis, classification, intelligence, agents

GPT-4o Mini (gpt-4o-mini-2024-07-18)

• Best for: Cost-effective general-purpose tasks
• Capabilities: Structured output, multimodal support, efficient operation
• Use cases: Standard automation, customer support, content processing
• Supports: Prompts, conversations, email analysis, translation, classification, intelligence, agents

Reasoning Models - o3-mini & o1-mini

o3-mini (o3-mini)

• Best for: Latest reasoning-focused tasks requiring deep analysis
• Capabilities: Advanced reasoning mode (required temperature: 1.0)
• Use cases: Mathematical problems, logical analysis, complex problem-solving
• Note: System role not supported; fixed temperature requirement

o1-mini (o1-mini)

• Best for: Previous-generation reasoning tasks
• Capabilities: Reasoning mode (required temperature: 1.0)
• Use cases: Logic puzzles, analytical tasks, structured problem-solving
• Note: System role not supported; fixed temperature requirement

When to use: Tasks requiring explicit step-by-step reasoning, math, logic

Legacy Models

GPT-4 Turbo Preview (gpt-4-turbo-preview)

• Function calling, extended context
• Recommendation: Consider upgrading to GPT-4.1 or GPT-5 series

GPT-4 (gpt-4)

• Structured output, reliable performance
• Supports: Prompts, conversations, summarization, email analysis, classification

GPT-3.5 Turbo (gpt-3.5-turbo, gpt-3.5-turbo-1106)

• Function calling, basic capabilities
• Recommendation: Upgrade to GPT-4.1 Mini for better performance

Google Gemini

Access Google’s multimodal Gemini models directly.

Gemini 3 Series - Latest

Gemini 3 Pro Preview (gemini-3-pro-preview)

• Best for: Latest multimodal capabilities
• Capabilities: Multimodal processing (text, images, audio), advanced reasoning
• Use cases: Document analysis with images, multimedia processing, complex automation
• Temperature range: 0.0 - 1.0 (default: 0.7)
• Supports: Prompts, translation, classification, file analysis, agents

Gemini 2.5 Series - Production Advanced

Gemini 2.5 Pro (gemini-2.5-pro)

• Best for: Complex multimodal tasks requiring high performance
• Capabilities: Multimodal, large context windows, detailed analysis
• Use cases: Document understanding, comprehensive analysis, advanced automation

Gemini 2.5 Flash (gemini-2.5-flash)

• Best for: Fast multimodal processing
• Capabilities: Multimodal, efficient operation, quick responses
• Use cases: Real-time document analysis, responsive automation

Both support: Prompts, translation, classification, file analysis, agents

Gemini 2.0 Series - Efficient

Gemini 2.0 Flash (gemini-2.0-flash)

• Best for: Cost-effective multimodal processing
• Use cases: Standard document processing, general automation

Gemini 2.0 Flash Lite (gemini-2.0-flash-lite)

• Best for: Lightweight multimodal tasks
• Use cases: Simple document analysis, high-volume operations

Both support: Prompts, translation, classification, file analysis

Gemini 1.5 Pro - Established

Gemini 1.5 Pro (gemini-1.5-pro)

• Best for: Established multimodal performance
• Capabilities: Multimodal processing, reliable operation
• Use cases: Production workloads, established workflows
• Supports: Prompts, translation, classification, file analysis

Model Selection Guide

By Use Case

Conversational Agents

Recommended Models:

1. GPT-4o Mini - Best balance of cost and performance
2. Claude 3.7 Sonnet - Strong reasoning at reasonable cost
3. Gemini 2.5 Flash - Fast multimodal conversations
4. GPT-5 Mini - Advanced reasoning for complex interactions

Why these models:

• Support structured output for reliable responses
• Handle context well for conversation continuity
• Cost-effective for high-volume interactions
• Consistent reliability in production

Document Analysis with Images

Recommended Models:

1. Gemini 2.5 Pro - Complex multimodal analysis
2. Gemini 3 Pro Preview - Latest document understanding
3. GPT-4o - Strong multimodal processing
4. Gemini 2.5 Flash - Fast multimodal analysis

Why these models:

• Multimodal support for images and text together
• Large context windows for lengthy documents
• Strong reasoning for extracting insights
• Handle charts, diagrams, and visual elements

Data Classification & Extraction

Recommended Models:

1. GPT-4.1 Nano - Fast, cost-effective
2. GPT-4o Mini - Structured output for consistency
3. Claude Haiku 4.5 - Quick, efficient
4. GPT-4.1 Mini - Production-ready reliability

Why these models:

• Structured output ensures consistent categorization
• Cost-effective for high-volume operations
• Fast response times for real-time classification
• Reliable accuracy for business logic

Complex Reasoning & Analysis

Recommended Models:

1. o3-mini - Specialized reasoning mode for logic
2. Claude 4 Opus - Highest-capability reasoning
3. GPT-5 - Advanced problem-solving
4. Claude Sonnet 4.5 - Detailed analysis

Why these models:

• Advanced reasoning capabilities
• Handle multi-step logic effectively
• Understand complex relationships
• Provide detailed explanations

Content Generation

Recommended Models:

1. Claude Sonnet 4.5 - High-quality writing
2. GPT-5 - Creative and coherent content
3. Gemini 2.5 Pro - Long-form content
4. Claude 4 Sonnet - High-quality balanced output

Why these models:

• Natural, fluent writing style
• Good creativity control via temperature
• Handle various content types well
• Consistent quality and tone

Semantic Search

Required Models:

1. Snowflake Arctic L V2.0 - Latest, highest quality
2. Snowflake Arctic M V1.5 - Reliable production

Why these models:

• Optimized for semantic similarity
• Consistent vector representations
• Efficient processing at scale
• Note: Must use Snowflake provider for embedding models

By Budget

Cost-Conscious

Lowest Cost Models:

• GPT-4.1 Nano: Minimal cost, simple tasks
• Claude Haiku 4.5: Fast and efficient
• Gemini 2.0 Flash Lite: Lightweight multimodal
• Llama 3.2 1B/3B: Maximum efficiency
• Mistral 7B: Small but capable

Best for: High-volume operations, simple automation, basic classification

Balanced

Best Value Models:

• GPT-4o Mini: Strong cost-performance ratio
• GPT-4.1 Mini: Production-ready efficiency
• Claude 3.7 Sonnet: Strong reasoning, moderate cost
• Gemini 2.5 Flash: Fast multimodal, reasonable cost
• GPT-5 Mini: Advanced capabilities, acceptable cost

Best for: Production applications, customer-facing features, standard automation

Performance

High-Performance Models:

• GPT-5.2: Highest OpenAI performance
• Claude 4 Opus: Highest-capability reasoning
• Claude Sonnet 4.5: Latest advanced capabilities
• GPT-5: Advanced reasoning and quality
• Gemini 3 Pro Preview: Latest multimodal

Best for: Mission-critical applications, complex analysis, high-performance features

By Provider Strengths

Snowflake

Strengths:

• Data residency in your cloud
• Wide model selection
• Claude and OpenAI access
• Native data processing

Choose when: Data security, Snowflake integration, diverse model needs

OpenAI Direct

Strengths:

• Latest GPT models first
• Structured output
• Mature ecosystem
• Reliable performance

Choose when: Latest OpenAI features, production performance

Gemini

Strengths:

• Multimodal capabilities
• Large context windows
• Fast processing
• Latest multimodal features

Choose when: Document analysis with images, large contexts, latest AI

Key Model Capabilities

Multimodal Processing

What it is: Process text and images together in the same request

Supported Models:

• All Gemini models (2.0+)
• GPT-4o, GPT-4o Mini

Use cases:

• Document analysis with charts/diagrams
• Image-based data extraction
• Visual content understanding
• OCR and form processing

Structured Output

What it is: Guaranteed JSON/XML format responses for reliable automation

Supported Models:

• All GPT-4o, GPT-4.1, GPT-5 series
• Cortex GPT models
• GPT-4 (partial)
• Llama 3.1 8B, 70B

Use cases:

• Data extraction to databases
• Automated classification
• API integrations
• Workflow automation

Reasoning Mode

What it is: Extended thinking for complex problems with step-by-step reasoning

Supported Models:

• o1-mini, o3-mini (dedicated reasoning, always on)
• GPT-5 series (configurable)
• Cortex o4-mini (dedicated reasoning)

Use cases:

• Mathematical problems
• Logic puzzles
• Complex analysis
• Multi-step problem-solving

Note: Dedicated reasoning models (o1/o3/o4-mini) require temperature = 1.0 and don’t support system roles

Agent Support

What it is: Optimized for Elementum agent workflows and multi-step tasks

Supported Models:

• GPT-4o, GPT-4.1, GPT-5 series
• All Claude models (via Snowflake)
• Cortex OpenAI models
• Gemini 2.5+, Gemini 3 Pro

Use cases:

• Conversational agents
• Multi-turn interactions
• Complex workflows
• Autonomous task execution

Temperature Settings

All models except dedicated reasoning models support customizable temperature. Temperature is configured at the LLM Service level in Organization Settings.

• 0.0 - 0.3: Deterministic, consistent (classification, data extraction)
• 0.4 - 0.7: Balanced creativity (conversation, general tasks)
• 0.8 - 1.0: Creative, diverse (content generation, brainstorming)

Default: 0.7 for most models

Special cases: o1-mini, o3-mini, Cortex o4-mini require temperature 1.0 (not adjustable)

Best Practices

Model Selection

1. Identify your use case — Determine if you need conversation, classification, analysis, generation, or search.
2. Check required capabilities — Verify if you need multimodal, structured output, or reasoning capabilities.
3. Consider your provider — Choose based on data residency, integration, and model access requirements.
4. Balance cost and performance — Select the smallest model that meets your quality requirements.
5. Test before committing — Compare 2-3 models with your actual use cases.
6. Monitor and optimize — Track quality, cost, and speed metrics to refine your selection.

Cost Optimization

Choose right-sized models:

• Use Nano/Mini for simple tasks
• Reserve Pro/Opus for complex analysis
• Test if smaller models meet needs

Optimize prompts:

• Write concise, clear instructions
• Remove unnecessary context
• Set appropriate max tokens
• Use structured output formats

Consider provider costs:

• Snowflake Cortex models cost ~4.5x base rate (includes infrastructure and data residency)
• Direct provider access may be more cost-effective for high-volume, simple tasks
• Snowflake provides value through data residency and unified platform

Performance Optimization

For speed:

• Use Mini/Nano/Haiku models
• Lower max tokens
• Choose geographically close providers

For quality:

• Use Pro/Opus/Sonnet tier models
• Provide detailed context
• Test with real examples

For consistency:

• Use low temperature (0.0-0.2)
• Enable structured output
• Choose models with structured output support

Next Steps

AI Providers

Set up your AI provider connections

Create AI Services

Configure specific model instances for your workflows

Build Agents

Create conversational AI assistants using these models

AI Automations

Use AI models in automation workflows