Expand LLM Catalog
Overview
Expand LLM Catalog introduces the latest generation of AI models to the iX Hello vNext model catalog, giving builders and administrators greater flexibility to select the most suitable model for their specific business and customer experience needs.
The expanded catalog now includes support for:
Anthropic Claude 4.x family
OpenAI GPT-5.3 series text models
Next generation real-time audio models
With a broader range of AI models available, organizations can optimize model selection based on performance, capabilities, and use case requirements. This expansion enhances support for:
Text-based experiences such as content generation, summarization, and question answering
Coding and agentic workflows that require advanced reasoning and task execution
Conversational AI applications for natural, context-aware interactions
Voice and real-time experiences that enable low-latency speech and multimodal interactions
By expanding the model catalog, iX Hello Customer v2 provides greater flexibility, improved AI capabilities, and more options for building intelligent customer and employee experiences.
What Problem Does It Solve
Expands Model Options
The expanded model catalog gives builders access to a broader selection of AI providers and model families, removing the limitations of a narrower model set.
This enables teams to:
Choose the most appropriate model for specific use cases
Balance performance, speed, and cost requirements
Optimize AI experiences based on business needs
Reduces Single-Provider Dependency
By supporting models from multiple AI providers, the platform reduces reliance on a single vendor.
This provides:
Greater flexibility in model selection
Improved resilience and future scalability
The ability to adopt new innovations as they become available
Improves Model-to-Task Fit
Different AI workloads require different capabilities. The expanded catalog allows builders to select models that best match the task at hand, whether it requires:
Fast, cost-efficient responses
Long-context understanding and reasoning
Advanced coding and agentic capabilities
Realtime voice and conversational interactions
This helps improve both application performance and user experience.
Enables Next-Generation Voice Experiences
Support for newer real-time audio models provides a modern foundation for voice-based applications.
Benefits include:
Improved support for low-latency voice interactions
Enhanced real-time conversational experiences
Reduced reliance on older voice models that may be deprecated over time
Overall, the expanded LLM catalog gives organizations greater flexibility, better model alignment, and access to the latest AI capabilities across text, coding, conversational, and voice use cases.
Why Use the Expanded LLM Catalog
The expanded LLM catalog provides greater flexibility, better performance alignment, and improved scalability by giving organizations access to a wider range of AI models optimized for different workloads.
Better Model Choice and Quality
Different AI models excel at different types of tasks. The expanded catalog enables builders to select the model that best fits their application requirements, helping improve both performance and user experience.
Examples include:
Claude Opus 4.7 for complex reasoning, advanced problem-solving, and agentic coding tasks
Claude Sonnet 4.6 for a balanced combination of performance, cost efficiency, and large-context processing
Claude Haiku 4.5 for fast, responsive, and cost-effective interactions
GPT-5.3-codex and GPT-5.3-chat for advanced coding, automation, and conversational experiences
Increased Resilience and Vendor Flexibility
Supporting multiple AI providers reduces dependency on a single model vendor and provides greater operational flexibility.
This benefits critical platform capabilities such as:
Runtime Engine workloads
Flow Summary generation
Debug Inspector system flows
A multi-provider approach also helps organizations adapt more easily as new models and technologies become available.
Cost Optimization
Not every use case requires the most powerful model. The expanded catalog allows teams to select a model that meets business requirements while optimizing operational costs.
Organizations can choose from a performance-based model tiering strategy:
Haiku 4.5 / GPT-5-mini for lightweight, high-volume workloads
Sonnet 4.6 / GPT-5.3-chat for general-purpose intelligent interactions
Opus 4.7 / GPT-5.3-codex for advanced reasoning and coding scenarios
Realtime-2 for voice and real-time experiences
This flexibility helps balance quality, performance, and cost.
Improved Voice and Realtime Experiences
The introduction of gpt-realtime-2 provides a modern foundation for voice-based applications and real-time conversational experiences.
Benefits include:
Enhanced voice interaction quality
Lower-latency conversational experiences
Support for future voice capabilities and innovations
Reduced dependence on older real-time model versions that may eventually be retired
Greater Operational Scalability
The platform's provider abstraction layer simplifies the integration of new AI model families.
This enables:
Faster adoption of emerging AI technologies
Easier expansion of the model catalog over time
Reduced integration effort for product and engineering teams
A more scalable and future-ready AI ecosystem
Feature Goals
Provider-Agnostic Runtime Architecture
The Runtime Engine uses a provider abstraction layer, making it easier to add and support new AI models without changing core runtime functionality.
New Model Deployments
The platform will introduce:
Three Claude model variants
Two GPT-5.3 model variants
These models will initially be deployed in non-production environments for evaluation and testing before being promoted to production.
Realtime Voice Model Support
The platform can support gpt-realtime-2 for eligible environments, enabling newer voice experiences without requiring changes to the Channel Gateway.
GPT-5.x Reasoning Enhancements
GPT-5.x models require explicit reasoning configuration to ensure optimal performance.
Key updates include:
Support for the reasoning_effort parameter
Availability of reasoning_effort=xhigh for advanced coding and reasoning workloads
Updates to model-specific settings for reasoning-enabled variants
Enhanced Model Catalog Service
The Configuration Service now returns detailed model metadata instead of only deployment names, helping applications and users discover available model capabilities more easily.
Access to the catalog continues to be controlled through the flows:read permission.
Builder Experience Updates
Model selection is available across all supported AI configuration surfaces, including:
Global Persona AI Model dropdown
Flow Designer AI-powered nodes:
Ask
Agent
Summary
Classifier
End-to-End Model Validation
All newly supported models are validated to ensure they work correctly with supported platform capabilities, including:
Text conversations
Voice conversations
Streaming responses
Tool calling
Supported runtime behaviors
Where Users Will See This Feature
Global Persona Configuration
The AI Model dropdown in the Global Persona settings now includes the newly supported models.
Flow Designer Nodes
Builders can select the new models directly within AI-powered flow nodes, including:
Ask
Agent
Summary
Classifier
Environment-Specific Model Catalogs
Available models may vary depending on the environment and use case. Separate catalogs can be maintained for chat and voice experiences based on:
Environment configuration
Supported modality (chat or voice)
Model availability and deployment status
List of Model Catalog:
How the feature works
Model metadata is returned by configuration
When the platform retrieves available models, it uses the Configuration service.
The service returns model metadata including:
Provider
Capabilities
Extended thinking
Reasoning effort
Context window
Output cap
Modality
Supported APIs
Tier metadata
Builders choose models in the UI
Users select models from supported dropdowns in persona settings or flow nodes.
The available set depends on the configured catalog for that environment.
Runtime uses provider abstraction
Once selected, the Runtime Engine invokes the model through a provider abstraction layer.
This avoids one-off integrations for each model family.
Runtime behavior varies by model capability
Some models are optimized for:
Reasoning
Coding
General conversation
Realtime audio
The platform uses catalog metadata to understand those differences.
User Workflow: Selecting and Configuring AI Models
The platform supports multiple AI providers and models, allowing you to choose the best option based on your channel, use case, and performance requirements.
Create or Open a Flow
Navigate to Flow Designer.
Create a new flow or open an existing one.
Select the channel you want to configure:
Chat
Voice
Select a Language
In the flow configuration, choose the desired Language.
The available model options will be displayed based on the selected channel and language.
Choose an AI-Model for Chat
The Chat channel now supports models from multiple providers:
OpenAI Models : Available OpenAI models include:
GPT-4 series models
GPT-5.x series models
GPT-5 Mini variants
GPT-5 Nano variants
Real-Time models
Codex (high-reasoning, code-focused model) and so on
Anthropic Models : Selected Anthropic models are now available and can be used for chat-based experiences.
To configure a model:
Open the Chat Settings section.
Select an AI model from the available providers.
Choose the model that best matches your requirements for:
Response quality
Reasoning capabilities
Speed and latency
Cost efficiency
Available Anthropic models include:
Claude Haiku
Claude Sonnet
Claude Opus
Note: The Codex model is designed for code generation and advanced reasoning tasks. Because of its specialized purpose, it is available only for chat experiences and is not supported in voice channels.
Choose an AI-Model for Voice
Switch to the Voice channel configuration.
Select a language.
Choose between:
Standard (Non-Real-Time) Models
Real-Time Models
Standard Voice Models
Most supported OpenAI and Anthropic models are available for voice interactions.
Real-Time Voice Models
Real-time voice experiences currently support OpenAI models only.
Available options include:
GPT-4-based Real-Time models
GPT-4-based Real-Time 5 models
GPT-5-based Real-Time 2 models
Note: Real-Time 2 models leverage GPT-5 reasoning capabilities. While they may have slightly higher latency than GPT-4-based real-time models, they are optimized to maintain responsive conversational experiences.
Realtime model considerations
Voice path forward
gpt-realtime-2 is identified as the recommended forward path for the voice pipeline.
No gateway code change required
The JIRA description states that making this model available does not require Channel Gateway changes.
Environment-specific availability
Realtime model availability may be controlled per environment.
Understand Model Performance Considerations
Different models can produce different results in terms of quality, reasoning ability, speed, and latency.
OpenAI Models : OpenAI models are generally recommended for:
Conversational AI
Customer-facing interactions
Voice experiences
Low-latency use cases
The Mini and Real-Time variants offer an optimal balance between performance, response quality, and speed.
Anthropic Models
Anthropic's Claude models, particularly Claude Opus and Claude Sonnet, are designed for advanced reasoning tasks.
While these models can provide strong analytical capabilities, they typically have higher response times compared to OpenAI models.
For some requests, response times may be significantly longer, making them less suitable for:
Real-time conversations
Voice interactions
Fast-paced customer service workflows
Recommendation: Use OpenAI Real-Time or Mini models for production voice experiences and latency-sensitive chat applications.
Configure Model-Specific Settings
Some models expose additional configuration options.
Review the settings available for the selected model.
Configure any reasoning or advanced parameters as required.
For GPT-5 reasoning models, specify the required
reasoning_effortsetting where applicable.
GPT-5.x reasoning considerations
Explicit reasoning effort is required
For GPT-5.x reasoning-capable models, calls must explicitly pass reasoning_effort.
The default changed to none beginning with GPT-5.1.
Risk of silent regression
If flows rely on implicit reasoning behavior, output quality may regress without obvious errors.
Reasoning-specific parameter handling
Paths targeting 'chat' reasoning variants should remove:
Temperature
Other non-reasoning parameters that do not apply
Higher reasoning levels for coding workloads
reasoning_effort=xhigh should be available for codex-class tasks.
Test and Validate the Flow
After selecting a model, thoroughly test the flow before publishing.
Run the flow using the selected model.
Validate:
Response quality
Accuracy
Latency
Streaming behavior
Tool-calling capabilities
Voice interaction quality (where applicable)
Compare model behavior across different scenarios to determine the best fit for your use case.
Model Availability and Vendor Support
New models can be added as they become available from supported vendors.
Currently supported vendors include:
OpenAI
Anthropic
Important: Adding a model from an existing supported vendor is typically straightforward. However, supporting a completely new AI vendor requires additional platform integration and development effort before its models can be made available.
Last updated
Was this helpful?