AI Model Comparison
Corporate AI Policy Restriction Mandatory
Public, consumer-grade Generative AI tools (e.g., ChatGPT Free, Google Gemini Public) are strictly prohibited for internal company data. Please use only the Enterprise Editions listed below to ensure IP protection.
General Requirements & Compliance
- All models must comply with company data governance policies and security standards
- Usage must adhere to vendor-specific terms of service and acceptable use policies
- PII and sensitive data handling requires additional approval and encryption
- Production deployments require security review and compliance sign-off
- Cost monitoring and budget allocation must be configured before use
- Models with specific legal requirements (shown in red below) require additional documentation
- Model not accessible to external partners/customers
- Can't use with third-party confidential information that requires consent to disclose to vendors without Legal approval (e.g., licensed chip designs)
- Can't use ITAR, CUI, and/or CDI controlled information/technology with the model without approval from Trade Compliance
- Don't use to ideate solutions for new product features without individual use-case approval (submit Service Now AI request)
Per-model limitations
Additional vendor-specific or deployment limitations may apply to individual models. Expand a model row to view its Limitation / Warning when one is listed.
Gemini 2.5 Pro
Google's most advanced model with massive context window and multimodal capabilities. Excellent for complex reasoning, long document analysis, and multimodal tasks.
Specifications
When to Use
- Long context analysis
- Multimodal tasks
- Complex reasoning
- Research & analysis
Pros
- Massive 1M context
- Excellent multimodal
- Latest knowledge
- Strong reasoning
- Advanced capabilities
Cons
- Higher cost
- Moderate speed
- Newer model
- Less ecosystem maturity
Limitation #1: Use only through the approved Vertex AI enterprise configuration (not consumer Gemini).
Limitation #2: Configure access so Google is granted WD permission to process customer content as required for the service; follow approved data-processing and logging settings.
Gemini 2.5 Flash
Fast and efficient Gemini model with large context window. Optimized for speed while maintaining strong capabilities across text and multimodal tasks.
Specifications
When to Use
- Fast processing
- Long documents
- Cost optimization
- Real-time apps
- High-volume tasks
Pros
- Very fast
- 1M context window
- Cost-effective
- Good multimodal
- Balanced performance
Cons
- Less capable than Pro
- Newer model
- Limited complex reasoning
Limitation #1: Use only through the approved Vertex AI enterprise configuration (not consumer Gemini).
Limitation #2: Configure access so Google is granted WD permission to process customer content as required for the service; follow approved data-processing and logging settings.
Anthropic Claude (3.5 Sonnet)
Flagship model balancing intelligence, speed, and cost. Strong at analysis, coding, content creation, and reasoning with nuanced understanding.
Specifications
When to Use
- Complex reasoning
- Code review
- Content creation
- Data analysis
- Long conversations
- Safety-critical apps
Pros
- Excellent reasoning
- Strong safety
- 200K context window
- Nuanced understanding
- Good code generation
- Balanced cost
Cons
- More expensive than smaller models
- May be cautious at times
- Limited real-time info
- Smaller ecosystem
- No image generation
Limitation #1: Warn users that model output must not be relied upon without independent verification and appropriate human judgment.
Anthropic Claude (3.5 Haiku)
Fast and efficient Claude model optimized for speed and cost. Best for high-volume processing and quick response times while maintaining quality.
Specifications
When to Use
- Simple tasks
- Quick responses
- High throughput
- Cost-sensitive apps
- Real-time interactions
Pros
- Very fast
- Low cost
- Good for simple tasks
- 200K context window
- Reliable performance
Cons
- Limited complex reasoning
- Less capable than Sonnet
- Simpler outputs
Limitation #1: Warn users that model output must not be relied upon without independent verification and appropriate human judgment.
Anthropic Claude (3.7 Sonnet)
Balanced Claude Sonnet model for general-purpose tasks. Enhanced version with improved performance over 3.5.
Specifications
When to Use
- General purpose tasks
- Content creation
- Code review
- Analysis work
- Document processing
Pros
- Good balance
- 200K context
- Cost-effective
- Improved over 3.5
- Reliable
Cons
- Not the latest version
- Older knowledge cutoff
- Less advanced than 4.x series
Limitation #1: Warn users that model output must not be relied upon without independent verification and appropriate human judgment.
Anthropic Claude (Sonnet 4)
High-performance Claude Sonnet model with strong reasoning capabilities. Next-generation model with significant improvements.
Specifications
When to Use
- Complex reasoning
- Code generation
- Analysis tasks
- Advanced workflows
- Enterprise applications
Pros
- Strong performance
- 200K context window
- Latest knowledge cutoff
- Advanced reasoning
- Better code quality
Cons
- Higher cost
- May be overkill for simple tasks
- Premium pricing
Limitation #1: Warn users that model output must not be relied upon without independent verification and appropriate human judgment.
Anthropic Claude (Sonnet 4.5)
Latest iteration of Claude Sonnet with enhanced reasoning, improved coding capabilities, and better instruction following. Offers superior performance while maintaining the balanced approach of the Sonnet series.
Specifications
When to Use
- Advanced reasoning tasks
- Complex code generation
- Agentic workflows
- Multi-step problem solving
- Technical documentation
- Data transformation
Pros
- Enhanced reasoning over 4.0
- Better code quality
- Improved instruction following
- Strong agentic capabilities
- 200K context window
- Maintains safety standards
- Best-in-class for complex tasks
Cons
- Higher cost than earlier versions
- May be overkill for simple tasks
- Similar speed to 4.0
- Limited ecosystem vs OpenAI
- No image generation
Limitation #1: Warn users that model output must not be relied upon without independent verification and appropriate human judgment.
Anthropic Claude (Haiku 4.5)
Fast and efficient Claude model optimized for speed and cost. Latest Haiku version with improved capabilities.
Specifications
When to Use
- High-volume processing
- Quick responses
- Cost-sensitive apps
- Real-time interactions
- Simple automated tasks
Pros
- Extremely fast
- Cost-effective
- 200K context window
- Latest version
- Good for high throughput
Cons
- Less capable than Sonnet
- Limited complex reasoning
- Simpler outputs
Limitation #1: Warn users that model output must not be relied upon without independent verification and appropriate human judgment.
Cohere Command (Command R/R )
Enterprise-focused models optimized for retrieval and business applications. Strong RAG capabilities and multilingual support.
Specifications
When to Use
- RAG applications
- Business tasks
- Information retrieval
- Multilingual processing
- Enterprise search
Pros
- Excellent for RAG
- Enterprise features
- Strong retrieval
- Multilingual support
- Cost-effective
Cons
- Limited compared to GPT-4
- Niche use cases
- Smaller context window
Limitation #1: Do not use as a stand-alone tool exposed to customers or external parties.
Limitation #2: Use only with non–highly sensitive information, consistent with approved internal use cases.
Cohere Embed (3)
Semantic search and embedding model for document processing and similarity matching. High-quality vector representations.
Specifications
When to Use
- Search applications
- Similarity matching
- Document clustering
- Recommendation systems
- Semantic search
Pros
- High-quality embeddings
- Fast processing
- Multilingual support
- Cost-effective
- Specialized purpose
Cons
- Single purpose only
- Not a generative model
- Requires integration
Limitation #1: Do not use as a stand-alone tool exposed to customers or external parties.
Limitation #2: Use only with non–highly sensitive information, consistent with approved internal use cases.
Cohere Embed (4)
Enhanced embedding model with improved accuracy and better multilingual capabilities. Latest version with optimized performance.
Specifications
When to Use
- Advanced search
- Cross-lingual tasks
- Large-scale retrieval
- Semantic analysis
- Production embeddings
Pros
- Enhanced embeddings
- Improved accuracy
- Better multilingual
- Optimized performance
- Latest version
Cons
- Single purpose only
- Higher cost than v3
- Not a generative model
Limitation #1: Do not use as a stand-alone tool exposed to customers or external parties.
Limitation #2: Use only with non–highly sensitive information, consistent with approved internal use cases.
Cohere Rerank (3.5)
Specialized model for reranking search results and relevance scoring. Optimized for improving search quality.
Specifications
When to Use
- Search reranking
- Relevance scoring
- Information retrieval
- Search optimization
- Result refinement
Pros
- Excellent reranking
- Very fast
- Specialized purpose
- Improves search quality
- Easy integration
Cons
- Single purpose only
- Not a general LLM
- Requires search system
Limitation #1: Do not use as a stand-alone tool exposed to customers or external parties.
Limitation #2: Use only with non–highly sensitive information, consistent with approved internal use cases.
Amazon Bedrock Llama (2)
Open source model with versatile capabilities. Community-driven with good performance for general tasks.
Specifications
When to Use
- General tasks
- Research
- Development
- Experimentation
Pros
- Open source
- Versatile
- Community support
- Self-hostable
Cons
- Not approved for WD use (Meta Llama)
- Older knowledge
- Limited context
- Do not deploy
Meta Llama models are not approved for WD use. Do not deploy or use for production workloads.
Amazon Bedrock Llama (3)
Improved version of Llama with better reasoning and enhanced performance. Stronger capabilities than Llama 2.
Specifications
When to Use
- Advanced tasks
- Research projects
- Development
- Testing
Pros
- Improved capabilities
- Better reasoning
- Enhanced performance
- Open source
Cons
- Not approved for WD use (Meta Llama)
- Smaller context than competitors
- Do not deploy
Meta Llama models are not approved for WD use. Do not deploy or use for production workloads.
Amazon Bedrock Mistral (7B)
Efficient open-source model with 7 billion parameters. Good balance of performance and resource usage.
Specifications
When to Use
- Cost-sensitive apps
- Self-hosting
- Simple tasks
- High-volume processing
Pros
- Open source
- Very cost-effective
- Fast processing
- Efficient
- Good for simple tasks
Cons
- Limited capabilities
- Older knowledge
- Smaller context than newer models
- Less sophisticated
Amazon Bedrock Mistral (8x7B)
Mixture of Experts model with efficient 8x7B parameter architecture. Better performance than standard 7B.
Specifications
When to Use
- Efficient processing
- Balanced performance
- Cost optimization
- Production workloads
Pros
- Efficient MoE architecture
- Good performance/cost
- Fast processing
- Better than 7B
- Open source
Cons
- Limited vs larger models
- Smaller context
- Less capable than premium models
GCP Mistral OCR (25.05)
Specialized optical character recognition model for document processing and text extraction.
Specifications
When to Use
- Document scanning
- Text extraction
- Image to text
- Form processing
Pros
- Specialized OCR
- Good accuracy
- Fast processing
- Multi-format support
Cons
- Single purpose
- Not a general LLM
- Limited to OCR tasks
Amazon Bedrock OpenAI GPT OSS (20B)
Mid-size open source model with 20 billion parameters. Good balance for self-hosting scenarios.
Specifications
When to Use
- Medium-scale tasks
- Self-hosting
- Balanced capability
- Private deployment
Pros
- Open source
- Good balance
- Self-hostable
- Privacy control
- Cost-effective
Cons
- Requires infrastructure
- Limited vs GPT-4
- Smaller context
Amazon Bedrock OpenAI GPT OSS (120B)
Large open source model with 120 billion parameters. High capability for complex tasks.
Specifications
When to Use
- Complex tasks
- Self-hosting
- High capability needs
- Advanced processing
Pros
- Open source
- Large capacity
- Strong performance
- Self-hostable
- Privacy control
Cons
- High infrastructure needs
- Slower processing
- Resource intensive
- Complex deployment
Amazon Bedrock DeepSeek (R1)
Advanced reasoning model optimized for complex analytical tasks, mathematical problems, and logical reasoning.
Specifications
When to Use
- Complex reasoning
- Mathematical problems
- Analytical tasks
- Code generation
- Technical analysis
Pros
- Strong reasoning
- Good analytical performance
- Cost-effective
- Math capabilities
- Code generation
Cons
- Limited context window
- Smaller ecosystem
- Less versatile
GCP Imagen (4)
Advanced image generation model with high-quality output and customizable options. Fast rendering capabilities.
Specifications
When to Use
- Image generation
- Creative visuals
- Fast rendering
- Marketing materials
- Design prototypes
Pros
- High quality images
- Fast generation
- Customizable
- Good control
- Versatile styles
Cons
- Image only
- Not a text model
- Content policy restrictions
- Usage limits
External, partner-facing, or customer-facing use of generated images requires Legal review. Comply with WD policy on distribution, licensing, and labeling of AI-generated visual content.
Black Forest Labs Flux (.1 schnell)
Advanced image generation model with high fidelity and text-to-image capabilities. Professional-grade output quality.
Specifications
When to Use
- Professional graphics
- Marketing
- Design
- Content creation
- High-quality visuals
Pros
- High fidelity output
- Text-to-image
- Style control
- Professional quality
- Google integration
Cons
- Image only
- Higher cost
- Moderate speed
- Requires GCP
Krea Flux (.1 krea)
Fast image generation model optimized for speed. Quick iterations and rapid prototyping.
Specifications
When to Use
- Rapid prototyping
- Quick visuals
- Iterative design
- Time-sensitive projects
- Fast rendering
Pros
- Fast generation
- High quality images
- Good control
- Quick iterations
- Efficient
Cons
- Image only
- Not a text model
- Limited availability
GCP Veo (3, 3 Fast)
Krea's variant of Flux optimized for creative image generation with artistic style control.
Specifications
When to Use
- Creative projects
- Artistic content
- Design exploration
- Visual research
- Style experiments
Pros
- Creative control
- High quality
- Artistic style
- Flexible output
- Unique aesthetics
Cons
- Image only
- Slower than Schnell
- Niche provider
Azure GPT (5)
Google's advanced video generation model with multiple format support and quality output options.
Specifications
When to Use
- Video generation
- Animation
- Creative video content
- Marketing videos
- Presentations
Pros
- Video generation
- High quality output
- Multi-format
- Flexible duration
- Advanced AI
Cons
- Video only
- Slow processing
- High cost
- Resource intensive
Self-hosted Qwen3-coder (480B-A35B-Instruct)
Code-focused large model with extensive context. Self-hosted for privacy-preserving code generation.
Specifications
When to Use
- Code generation
- Development
- Private projects
- Internal tools
- Secure environments
Pros
- Code-focused
- Large context
- Self-hosted
- Privacy-preserving
- No data sharing
Cons
- Requires infrastructure
- Resource intensive
- Complex deployment
- Maintenance overhead
Enable the required network isolation for the deployment (e.g., airplane mode / no external connectivity) per IT security direction unless explicitly approved otherwise.
Self-hosted Qwen3-coder (30-A3B-Instruct)
Efficient coding model with fast inference. Self-hosted for internal development use.
Specifications
When to Use
- Code assistance
- Development tasks
- Private deployment
- Internal use
- Quick coding help
Pros
- Efficient
- Fast inference
- Self-hosted
- Resource-efficient
- Privacy control
Cons
- Requires infrastructure
- Smaller than 480B
- Less capable for complex tasks
Enable the required network isolation for the deployment (e.g., airplane mode / no external connectivity) per IT security direction unless explicitly approved otherwise.
Self-hosted Codellama (7b, 13b, 34b, 70b)
Code generation models in multiple sizes. Open source with flexible deployment options.
Specifications
When to Use
- Development
- Code completion
- Learning
- Prototyping
- Experimentation
Pros
- Multiple sizes
- Open source
- Flexible deployment
- Good code generation
- Community support
Cons
- Meta Llama–family model; not approved for WD use
- Older model
- Limited context
Meta Llama models are not approved for WD use. Do not deploy or use for production workloads.
Self-hosted Codegemma (2b, 7b)
Lightweight coding models for fast performance. Easy deployment for code assistance.
Specifications
When to Use
- Code assistance
- Development
- Learning
- Quick tasks
- Fast completion
Pros
- Lightweight
- Fast performance
- Open source
- Easy deployment
- Resource efficient
Cons
- Small context
- Limited capabilities
- Basic features
- Less sophisticated
Enable the required network isolation for the deployment (e.g., airplane mode / no external connectivity) per IT security direction unless explicitly approved otherwise.
Self-hosted Codestral (22b)
Advanced coding model with high quality output. Requires commercial license for use.
Specifications
When to Use
- Professional development
- Enterprise projects
- Advanced coding
- Production use
Pros
- Advanced coding
- High quality
- Specialized
- Professional features
Cons
- Requires commercial license
- Currently denied
- Cost barrier
- License complexity
Self-hosted DeepSeek-coder (v2)
Strong code generation model with reasoning capabilities. Self-hosted for secure environments.
Specifications
When to Use
- Code development
- Problem-solving
- Internal projects
- Secure environments
- Private deployment
Pros
- Strong code generation
- Reasoning
- Self-hosted
- Privacy-focused
- Good performance
Cons
- Requires infrastructure
- Moderate speed
- Self-management needed
Enable the required network isolation for the deployment (e.g., airplane mode / no external connectivity) per IT security direction unless explicitly approved otherwise.
Self-hosted Granite-code
IBM enterprise-focused code generation model. Designed for regulated environments and corporate projects.
Specifications
When to Use
- Enterprise development
- Corporate projects
- Regulated environments
- Internal tools
Pros
- Enterprise-focused
- Code generation
- Self-hostable
- IBM support
Cons
- Not yet approved
- Limited information
- Pending review
Enable the required network isolation for the deployment (e.g., airplane mode / no external connectivity) per IT security direction unless explicitly approved otherwise.
Self-hosted Llama4Scout
Exploration variant of Llama 4. Versatile open source model for research and experimentation.
Specifications
When to Use
- Research
- Experimentation
- Development
- Testing
Pros
- Exploration model
- Versatile
- Open source
- Self-hosted
Cons
- Meta Llama family; not approved for WD use
- Do not deploy
Meta Llama models are not approved for WD use. Do not deploy or use for production workloads.
Self-hosted Llama4Maverick
Advanced variant of Llama 4 with enhanced features. Open source with flexible deployment.
Specifications
When to Use
- Advanced projects
- Research
- Development
- Innovation
Pros
- Advanced variant
- Enhanced features
- Open source
- Flexible
Cons
- Meta Llama family; not approved for WD use
- Do not deploy
Meta Llama models are not approved for WD use. Do not deploy or use for production workloads.
Self-hosted Llama3-gradient
Specialized Llama 3 variant optimized with gradient techniques. Research-focused model.
Specifications
When to Use
- Research applications
- Specialized tasks
- Development
- Experimentation
Pros
- Specialized variant
- Gradient-optimized
- Self-hosted
- Research-focused
Cons
- Meta Llama family; not approved for WD use
- Do not deploy
Meta Llama models are not approved for WD use. Do not deploy or use for production workloads.
Self-hosted Kimi-K2
Long context multilingual model. Self-hosted for document analysis and multilingual tasks.
Specifications
When to Use
- Document analysis
- Multilingual tasks
- Research
- Long-form content
Pros
- Long context
- Multilingual
- Self-hosted
- Research model
Cons
- Not yet approved
- Limited availability
- Unknown performance
Enable the required network isolation for the deployment (e.g., airplane mode / no external connectivity) per IT security direction unless explicitly approved otherwise.
Self-hosted GPT-OSS (20B)
Open source GPT model for self-hosting. Community-driven with flexible deployment.
Specifications
When to Use
- General tasks
- Research
- Development
- Private deployment
Pros
- Open source
- Self-hosted
- Community-driven
- Flexible
Cons
- Not yet approved
- Requires infrastructure
- Unknown capabilities
Enable the required network isolation for the deployment (e.g., airplane mode / no external connectivity) per IT security direction unless explicitly approved otherwise.
Self-hosted GPT-OSS (120B)
Large open source GPT model with advanced capabilities. Self-hosted for high-performance tasks.
Specifications
When to Use
- Complex tasks
- Research
- Enterprise projects
- Advanced applications
Pros
- Large model
- Advanced capabilities
- Self-hosted
- High performance
Cons
- Not yet approved
- High resource needs
- Complex deployment
Enable the required network isolation for the deployment (e.g., airplane mode / no external connectivity) per IT security direction unless explicitly approved otherwise.
Azure GPT-OSS-Safeguard (120B)
Safety-enhanced large GPT model with moderation capabilities. Enterprise-ready for regulated environments.
Specifications
When to Use
- Regulated environments
- Safety-critical apps
- Compliance-focused
- Enterprise use
Pros
- Safety-enhanced
- Moderation
- Secure
- Enterprise-ready
- Compliance features
Cons
- Not yet approved
- Unknown cost
- Pending availability
Azure GPT-OSS-Safeguard (20B)
Efficient safeguard model with moderation. Cost-effective with security features.
Specifications
When to Use
- Moderated applications
- Safety requirements
- Compliance needs
- Controlled environments
Pros
- Efficient
- Moderation
- Cost-effective
- Secure
Cons
- Not yet approved
- Smaller than 120B
- Limited info
Self-hosted BGE-BASE-EN (1.5)
English-focused embedding model, self-hosted for semantic search and document retrieval.
Specifications
When to Use
- Semantic search
- Document retrieval
- Similarity matching
- Internal applications
Pros
- English-focused
- Self-hosted
- Efficient
- Fast processing
- Privacy control
Cons
- Single language
- Single purpose
- Not generative
- Requires infrastructure
Self-hosted clip-ViT-B-32 (v2)
Vision-language model for image-text matching and cross-modal retrieval.
Specifications
When to Use
- Image search
- Cross-modal retrieval
- Visual applications
- Content matching
Pros
- Vision-language
- Image-text matching
- Multimodal
- Versatile
Cons
- License not specified
- Currently denied
- Legal uncertainty
- Deployment blocked
Amazon Bedrock Anthropic Claude (Opus 4.5)
Premium performance Claude model with maximum capability. Advanced reasoning and enterprise features.
Specifications
When to Use
- Critical applications
- Strategic projects
- High-value tasks
- Complex analysis
Pros
- Premium performance
- Maximum capability
- Advanced reasoning
- Enterprise features
- Best quality
Cons
- Highest cost
- Slower than other models
- May be overkill
- Premium pricing
Limitation #1: Warn users that model output must not be relied upon without independent verification and appropriate human judgment.
GCP Gemini (1.5)
Next-generation Gemini model with advanced multimodal capabilities and enhanced reasoning.
Specifications
When to Use
- Advanced applications
- Multimodal projects
- Research
- Innovation
- Long context tasks
Pros
- Next-gen model
- Advanced multimodal
- Enhanced reasoning
- 1M context
Cons
- Higher cost
- Moderate speed
- Very new model
- Limited track record
Limitation #1: Use only through the approved Vertex AI enterprise configuration (not consumer Gemini).
Limitation #2: Configure access so Google is granted WD permission to process customer content as required for the service; follow approved data-processing and logging settings.
GCP NanoBanana (1)
Lightweight efficient model for edge deployment and resource-limited environments.
Specifications
When to Use
- Edge deployment
- Resource-limited
- Quick tasks
- Efficient processing
Pros
- Lightweight
- Efficient
- Fast
- Low cost
Cons
- Limited capabilities
- Small context
- Basic features
- Less sophisticated
Self-hosted HunyuanOCR
Specialized OCR model designed for high-accuracy text extraction from images and documents.
Specifications
When to Use
- Document scanning
- Text extraction
- Form processing
- OCR tasks
Pros
- High accuracy
- Specialized OCR
- Self-hosted
- Cost-effective
Cons
- Single purpose
- Not a general LLM
- Basic capabilities
Self-hosted SigLIP-base (16-384)
Efficient vision-language model for image classification and embedding generation.
Specifications
When to Use
- Image classification
- Feature extraction
- Visual search
- Efficient processing
Pros
- Lightweight
- Efficient
- Fast
- Low cost
Cons
- Limited capabilities
- Small context
- Basic features
- Less sophisticated
Self-hosted all-MiniLM-L6-v2 (2)
Compact and fast sentence embedding model for semantic search and clustering.
Specifications
When to Use
- Semantic search
- Clustering
- Text similarity
- Balanced tasks
Pros
- Compact
- Fast
- Self-hosted
- Cost-effective
Cons
- Small model
- Basic capabilities
- Limited depth
Self-hosted Openai/whisper-base (20230621)
Robust speech recognition model for transcribing and translating audio.
Specifications
When to Use
- Transcription
- Translation
- Voice commands
- Efficient processing
Pros
- Accurate ASR
- Multilingual
- Fast
- Open source
Cons
- Limited capabilities
- Small context
- Basic features
Self-hosted Salesforce/blip-image (16-dec-25)
Bootstrapping Language-Image Pre-training model for image captioning and VQA.
Specifications
When to Use
- Image captioning
- Visual Q&A
- Content understanding
- Balanced tasks
Pros
- Versatile vision
- Good performance
- Self-hosted
- Cost-effective
Cons
- Still limited vs larger models
- Smaller context
- Basic capabilities
Amazon Bedrock Nova 2 Lite
Lightweight efficient model for edge deployment and resource-limited environments.
Specifications
When to Use
- Edge deployment
- Resource-limited
- Quick tasks
- Efficient processing
Pros
- Lightweight
- Efficient
- Fast
- Resource-friendly
- Low cost
Cons
- Limited capabilities
- Small context
- Basic features
- Less sophisticated
Amazon Bedrock Nova 2 Pro
Balanced model delivering better performance while maintaining efficiency.
Specifications
When to Use
- Production workloads
- Scalable solutions
- Efficient operations
- Balanced tasks
Pros
- Enhanced version
- Better performance
- Efficient
- Balanced
- Cost-effective
Cons
- Still limited vs full models
- Smaller context
- Basic capabilities
Amazon Bedrock Nova 2 Omni
Versatile multimodal model capable of processing text, audio, and visual inputs.
Specifications
When to Use
- Multimodal interactions
- Complex inputs
- Quick tasks
- Efficient processing
Pros
- Multimodal
- Efficient
- Fast
- Resource-friendly
- Low cost
Cons
- Limited capabilities
- Small context
- Basic features
- Less sophisticated
Amazon Bedrock Nova 2 Sonic
Ultra-fast audio processing model for real-time speech applications.
Specifications
When to Use
- Real-time speech
- Audio processing
- Efficient operations
- Balanced tasks
Pros
- High speed
- Audio focused
- Efficient
- Balanced
- Cost-effective
Cons
- Audio only
- Smaller context
- Basic capabilities
Amazon Bedrock Nova Multimodal Embeddings
Generates embeddings for both text and images to enable multimodal search.
Specifications
When to Use
- Multimodal search
- Embeddings
- Content matching
- Efficient processing
Pros
- Multimodal
- Embeddings
- Fast
- Resource-friendly
- Low cost
Cons
- Specific use case
- Small context
- Basic features
- Less sophisticated
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