AI Models

Model Usage

You can select which AI model suits you best for different uses from the list of models offered by the platform or plug in your own custom AI model. Supported usages include:

Conversation
Image Analysis
Image Generation
Embeddings Generation
Audio Transcription
Text to Speech
Document Summarization
Speech to Speech (Conversation / Realtime)
Moderation

These models have various functions, performance profiles, and feature sets.

Models used for Conversation must support Tools and streaming simultaneously.

How Praxis AI Uses Models

Praxis AI can orchestrate multiple providers and models in parallel using a unified interface:

Configure several providers in Personalization and AI Models.
Assign preferred models to each Model Use (Conversation, Images, Audio, etc.).
Overwrite the conversation provider for each Assistant

This orchestration is transparent to your LMS, Web SDK, or REST integrations.

Model Selection

Default for Your Digital Twin

Each Digital Twin in Praxis AI can use different models optimized for its domain. To select or change models:

Go to the Admin section.
Edit your Digital Twin.
Open the Personalization and AI Models section.
Review or change the model used for each Model Use (Conversation, Images, Audio, etc.).

Conversation at Runtime

At runtime, you can easily switch the LLM used for Conversation by accessing the Settings in the Side Bar panel and review model capabilities by clicking the Model Options detail

Custom Model is displayed in the same section in the Side Bar

Specific to each assistants

You can specify which Conversation model to use for each assistants

Behind the scenes, Praxis AI’s Neural Engine can route requests to different models based on:

Use Case
Assistant Specific model
Token budget and cost constraints
Model availability and latency
User preferences and history

This allows you to balance quality, speed, and cost without changing your front-end integration.

Platform Models

Praxis AI middleware offers access to a broad catalog of state-of-the-art AI models. You can select the model that best fits your needs based on performance, cost, and capabilities. The default model is configured to use the latest, most capable model available on the platform. In most cases, you should keep default selected unless you have a specific requirement (for example, strict cost control, specific provider, or latency constraints). Models can be accessed using:

The OpenAI Client
Or through Amazon Bedrock

Provider-Based Models

Praxis AI exposes conversation and related capabilities (vision, audio, embeddings, moderation, realtime) through two main provider types:

Amazon Bedrock
OpenAI-Compatible Clients

Each provider contains groups (Amazon, Anthropic, OpenAI, Gemini, etc.) and individual models with specific capabilities and uses.

Amazon Bedrock

Anthropic models via Bedrock are platform models of choice, mainly for Conversation and Image Analysis. Models marked with Extended support the optional 1M token context window (see Inference Settings).

Model Name	Status	Capabilities	Input (tokens)	Output (tokens)	Thinking	Typical Uses
`global.anthropic.claude-sonnet-4-6`	New	Tools, Streaming, Vision	200,000	64,000	Yes (Extended)	Conversation, Image Analysis, Summary
`us.anthropic.claude-sonnet-4-6`	Default	Tools, Streaming, Vision	200,000	64,000	Yes (Extended)	Conversation, Image Analysis, Summary
`us.anthropic.claude-sonnet-4-5-20250929-v1:0`	Current	Tools, Streaming, Vision	200,000	64,000	Yes (Extended)	Conversation, Image Analysis, Summary
`us.anthropic.claude-sonnet-4-20250514-v1:0`	Deprecated	Tools, Streaming, Vision	200,000	64,000	Yes (Extended)	Conversation, Image Analysis, Summary
`us.anthropic.claude-3-7-sonnet-20250219-v1:0`	Deprecated	Tools, Streaming, Vision	200,000	64,000	Yes	Conversation, Image Analysis, Summary
`us.anthropic.claude-3-5-sonnet-20241022-v2:0`	Deprecated	Tools, Streaming, Vision	200,000	8,192	—	Conversation, Image Analysis
`us.anthropic.claude-opus-4-6-v1`	New	Tools, Streaming, Vision	200,000	128,000	Yes (Extended)	Conversation, Image Analysis, Summary
`us.anthropic.claude-opus-4-5-20251101-v1:0`	Current	Tools, Streaming, Vision	200,000	64,000	Yes	Conversation, Image Analysis, Summary
`us.anthropic.claude-opus-4-1-20250805-v1:0`	Deprecated	Tools, Streaming, Vision	200,000	32,000	Yes	Conversation, Image Analysis
`us.anthropic.claude-opus-4-20250514-v1:0`	Deprecated	Tools, Streaming, Vision	200,000	32,000	Yes	Conversation, Image Analysis
`us.anthropic.claude-haiku-4-5-20251001-v1:0`	Current	Tools, Streaming, Vision	200,000	64,000	Yes	Conversation, Summary, Image Analysis
`us.anthropic.claude-3-5-haiku-20241022-v1:0`	Deprecated	Tools, Streaming, Vision	200,000	8,192	—	Conversation, Image Analysis

Deprecated models will be removed in a future release. Migrate to a newer model. When a deprecated model is removed, any assistant or configuration referencing it will automatically fall back to the institution’s default model.

Amazon models hosted on Bedrock, used for Conversation, Image Analysis, Audio Transcription, Image Generation, and Embeddings.

Conversation / Vision / Summary

Model Name	Status	Capabilities	Input (tokens)	Output (tokens)	Typical Uses
`us.amazon.nova-2-lite-v1:0`	New	Tools, Streaming, Vision	1,000,000	10,000	Conversation, Image Analysis
`us.amazon.nova-premier-v1:0`	Current	Tools, Streaming, Vision	1,000,000	10,000	Conversation, Image Analysis
`us.amazon.nova-pro-v1:0`	Current	Tools, Streaming, Vision	300,000	10,000	Conversation, Image Analysis
`us.amazon.nova-lite-v1:0`	Deprecated	Tools, Streaming, Vision	300,000	10,000	Conversation, Image Analysis, Summary
`us.amazon.nova-micro-v1:0`	Current	Tools, Streaming	128,000	10,000	Conversation, Summary
`transcribe`	Current	Audio Analysis	—	—	Audio transcription / translation
`amazon.nova-canvas-v1:0`	Default	Vision	—	—	Image Generation
`amazon.titan-image-generator-v2:0`	Deprecated	Vision	—	—	Image Generation

Embeddings

Model Name	Status	Input (tokens)	Vector Dimensions	Typical Uses
`amazon.titan-embed-text-v2:0`	Current	8,192	1,024	Embeddings
`amazon.titan-embed-text-v1`	Current	8,192	1,536	Embeddings

More information on Amazon Bedrock models: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html

Open-source GPT model exposed via Bedrock-compatible infrastructure.

Model Name	Capabilities	Input (tokens)	Output (tokens)	Typical Uses
`openai.gpt-oss-120b-1:0`	Tools, Streaming	130,000	130,000	Conversation

More information: https://platform.openai.com/docs/models/gpt-oss-120b

Meta (Llama) models via Bedrock group, used for Summary and Image Analysis rather than Conversation.

Model Name	Notes	Capabilities	Input (tokens)	Typical Uses
`us.meta.llama3-2-11b-instruct-v1:0`	No tools+streaming together	Streaming, Vision	1,000,000	Summary, Image Analysis
`us.meta.llama4-maverick-17b-instruct-v1:0`	No tools+streaming together	Streaming, Vision	1,000,000	Summary, Image Analysis
`us.meta.llama4-scout-17b-instruct-v1:0`	Tools and streaming supported	Tools, Streaming, Vision	10,000,000	Summary, Image Analysis

More information: https://www.llama.com/docs/get-started/

Cohere model via Bedrock, used for Conversation and Summary.

Model Name	Capabilities	Input (tokens)	Output (tokens)	Typical Uses
`cohere.command-r-plus-v1:0`	Tools, Streaming	128,000	4,000	Conversation, Summary

More information: https://docs.cohere.com/docs/models

Mistral models in the Bedrock provider, used for Conversation, Summary, and Image Analysis.

Model Name	Capabilities	Input (tokens)	Typical Uses
`mistral.mistral-large-3-675b-instruct`	Tools, Streaming, Vision	256,000	Summary, Conversation, Image Analysis
`us.mistral.pixtral-large-2502-v1:0`	Tools, Streaming, Vision	128,000	Summary, Image Analysis

More information: https://docs.mistral.ai/getting-started/models/models_overview

OpenAI-Compatible Clients

These models are configured against the OpenAI API and used across Conversation, Image Analysis, Summary, Audio, TTS, Moderation, and Realtime.

Conversation / Vision / Summary

Model Name	Status	Capabilities	Input (tokens)	Output (tokens)	Thinking	Typical Uses
`gpt-5.2`	Current	Tools, Streaming, Vision, MCP	400,000	128,000	Yes	Conversation, Image Analysis, Summary
`gpt-5.1`	Current	Tools, Streaming, Vision, MCP	400,000	128,000	Yes	Conversation, Image Analysis, Summary
`gpt-5-2025-08-07`	Deprecated	Tools, Streaming, Vision, MCP	272,000	128,000	Yes	Conversation, Image Analysis, Summary
`gpt-5-mini`	Current	Tools, Streaming, Vision, MCP	272,000	128,000	Yes	Conversation, Image Analysis, Summary
`gpt-5-nano-2025-08-07`	Current	Tools, Streaming, Vision, MCP	272,000	128,000	Yes	Conversation, Image Analysis, Summary
`gpt-5`	Deprecated	Tools, Streaming, Vision, MCP	272,000	128,000	Yes	Conversation, Image Analysis, Summary
`gpt-4.1`	Current	Tools, Streaming, Vision, MCP	1,047,576	32,768	—	Conversation, Image Analysis, Summary
`gpt-4o`	Deprecated	Tools, Streaming, Vision	128,000	16,384	—	Conversation, Image Analysis, Summary
`gpt-4o-mini`	Deprecated	Tools, Streaming, Vision	128,000	16,384	—	Conversation, Image Analysis, Summary
`o4-mini-deep-research`	Specialized	Streaming, Vision, MCP	200,000	100,000	Yes	Deep research, Image Analysis
`o4-mini`	Current	Tools, Streaming, Vision, MCP	200,000	100,000	Yes	Conversation, Image Analysis
`o3-deep-research`	Specialized	Streaming, Vision, MCP	200,000	100,000	Yes	Deep research, Image Analysis
`o3-pro`	Deprecated	Tools, Streaming, Vision, MCP	200,000	100,000	Yes	Conversation, Image Analysis
`o3`	Deprecated	Tools, Streaming, Vision, MCP	200,000	100,000	Yes	Conversation, Image Analysis
`o3-mini`	Deprecated	Tools, Streaming, Vision	200,000	100,000	Yes	Conversation, Image Analysis
`o1`	Deprecated	Tools, Streaming, Vision	200,000	100,000	Yes	Conversation, Image Analysis

Image Generation

Model Name	Status	Capabilities	Typical Uses
`gpt-image-1.5`	New	Vision	Image Generation
`gpt-image-1`	Current	Vision	Image Generation
`gpt-image-1-mini`	Current	Vision	Image Generation
`dall-e-3`	Current	Vision	Image Generation

Embeddings

Model Name	Input (tokens)	Vector Dimensions	Typical Uses
`text-embedding-3-small`	8,191	1,536	Embeddings
`text-embedding-3-large`	8,191	3,072	Embeddings

Audio Transcription and Translation

Model Name	Input (Hz)	Output (tokens)	Typical Uses
`whisper-1`	—	—	Audio Analysis
`gpt-4o-mini-transcribe`	16,000	2,000	Audio Analysis (Default)
`gpt-4o-transcribe`	16,000	2,000	Audio Analysis
`gpt-4o-transcribe-diarize`	16,000	2,000	Audio Analysis (Speaker ID)

Text-to-Speech (TTS)

Model Name	Typical Uses
`tts-1`	TTS
`tts-1-hd`	TTS
`gpt-4o-mini-tts`	TTS

Moderation

Model Name	Typical Uses
`omni-moderation-latest`	Moderation

Real-Time Speech-to-Speech (RT / STS)

Model Name	Status	Input Tokens	Output Tokens	Typical Uses
`gpt-realtime`	Default	32,000	4,096	Realtime voice agent
`gpt-realtime-mini`	Current	32,000	4,096	Realtime voice agent
`gpt-4o-realtime-preview`	Deprecated	32,000	4,096	Realtime voice agent
`gpt-4o-mini-realtime-preview`	Current	16,000	4,096	Realtime voice agent

More information: https://platform.openai.com/docs/models

ElevenLabs Conversational AI can be used as an alternative real-time speech-to-speech provider. When selected, Pria’s Convo Mode connects to your configured ElevenLabs agent instead of OpenAI GPT-Realtime.

Model Name	Typical Uses
`elevenlabs`	Realtime voice agent (Convo Mode)

Requires an ElevenLabs Agent ID and API Key configured in Integrations. See ElevenLabs Voice Agent for setup instructions.

Gemini models are accessed through the Google GenAI native SDK and are used for Conversation, Image Analysis, Summary, and Image Generation. Requires an API key.

Conversation / Vision / Summary

Model Name	Label	Capabilities	Input (tokens)	Output (tokens)	Thinking	Typical Uses
`gemini-3.1-pro-preview`	Advanced Reasoning	Tools, Streaming, Vision	1,048,576	65,536	Yes	Conversation, Image Analysis, Summary
`gemini-3-flash-preview`	Default	Tools, Streaming, Vision	1,048,576	65,536	Yes	Conversation, Image Analysis, Summary
`gemini-3-pro-preview`	Complex Reasoning	Tools, Streaming, Vision	1,048,576	65,536	Yes	Conversation, Image Analysis, Summary
`gemini-2.5-flash`	Fast	Tools, Streaming, Vision	1,048,576	65,536	Yes	Conversation, Image Analysis, Summary
`gemini-2.5-flash-lite`	Ultra Fast	Tools, Streaming, Vision	1,048,576	65,536	Yes	Conversation, Image Analysis, Summary
`gemini-2.5-pro`	Production	Tools, Streaming, Vision	1,048,576	65,536	Yes	Conversation, Image Analysis, Summary
`gemini-2.0-flash`	—	Tools, Streaming, Vision	1,048,576	8,192	—	Conversation, Image Analysis, Summary

Image Generation

Model Name	Label	Capabilities	Typical Uses
`gemini-2.5-flash-image`	Native Image Gen	Vision	Image Generation
`gemini-3-pro-image-preview`	HQ Image Gen	Vision	Image Generation

Embeddings

Model Name	Input (tokens)	Vector Dimensions	Typical Uses
`gemini-embedding-001`	2,048	3,072	Embeddings

More information: https://ai.google.dev/gemini-api/docs/models

Mistral models via direct API are used for Conversation, Image Analysis, Summary, Audio, Embeddings, and Moderation. Requires an API key.

Conversation / Vision / Summary

Model Name	Capabilities	Input (tokens)	Typical Uses
`pixtral-large-2411`	Tools, Streaming, Vision	128,000	Conversation, Image Analysis
`mistral-large-2411`	Tools, Streaming, Vision	128,000	Conversation, Summary
`mistral-medium-2508`	Tools, Streaming, Vision	128,000	Conversation, Image Analysis, Summary
`mistral-small-2506`	Tools, Streaming, Vision	128,000	Conversation, Image Analysis, Summary
`magistral-medium-2509`	Tools, Streaming, Vision	128,000	Conversation, Image Analysis, Summary
`magistral-small-2509`	Tools, Streaming, Vision	40,000	Conversation, Image Analysis, Summary
`codestral-2508`	Tools, Streaming, Vision	128,000	Conversation, Summary (code-focused)
`devstral-medium-2507`	Tools, Streaming, Vision	128,000	Conversation, Image Analysis, Summary

Audio and Embeddings

Model Name	Input Tokens	Typical Uses
`voxtral-mini-2507`	—	Audio Analysis
`mistral-embed`	8,000	Embeddings
`codestral-embed`	8,000	Embeddings

Moderation

Model Name	Typical Uses
`mistral-moderation-2411`	Moderation

xAI models via OpenAI-compatible API, used for Conversation, Image Analysis, Summary, and code-focused chat. Requires an API key.

Model Name	Capabilities	Input (tokens)	Typical Uses
`grok-4`	Tools, Streaming, Vision	256,000	Conversation, Image Analysis, Summary
`grok-4-fast-reasoning`	Tools, Streaming	2,000,000	High-context Conversation, Reasoning, Summary
`grok-4-fast-non-reasoning`	Tools, Streaming	2,000,000	Fast Conversation, Image Analysis, Summary
`grok-code-fast-1`	Tools, Streaming	256,000	Code-focused Conversation

More information: https://docs.x.ai/docs/models

Anthropic models via direct API, used for Conversation, Image Analysis, and Summary. Requires an API key. Models marked with Extended support the optional 1M token context window.

Model Name	Status	Capabilities	Input (tokens)	Output (tokens)	Thinking	Typical Uses
`claude-sonnet-4-6`	New	Tools, Streaming, Vision	200,000	64,000	Yes (Extended)	Conversation, Image Analysis, Summary
`claude-sonnet-4-5-20250929`	Current	Tools, Streaming, Vision	200,000	64,000	Yes (Extended)	Conversation, Image Analysis, Summary
`claude-haiku-4-5-20251001`	Current	Tools, Streaming	200,000	64,000	Yes	Conversation, Image Analysis, Summary
`claude-opus-4-6`	New	Tools, Streaming, Vision	200,000	128,000	Yes (Extended)	Conversation, Image Analysis, Summary
`claude-opus-4-5-20251101`	Current	Tools, Streaming	200,000	64,000	Yes	Conversation, Image Analysis, Summary
`claude-opus-4-1-20250805`	Deprecated	Tools, Streaming, Vision	200,000	32,000	Yes	Conversation, Image Analysis, Summary

More information: https://docs.anthropic.com/en/docs/about-claude/models/overview

Cohere model via OpenAI-compatible endpoint, used for Conversation, Image Analysis, and Summary. Requires an API key.

Model Name	Capabilities	Input (tokens)	Output (tokens)	Typical Uses
`command-a-03-2025`	Tools, Streaming, Vision	128,000	4,000	Conversation, Image Analysis, Summary

More information: https://docs.cohere.com/docs/models

Reasoning Effort

Some AI models support extended thinking (also called reasoning), where the model can spend additional time analyzing a problem before responding. Praxis AI provides a unified 5-level reasoning effort system that works across all supported providers.

Level	Description	Best For
None	Disable thinking. Fastest responses, lowest cost.	Simple queries, quick lookups
Low	Minimal reasoning.	Straightforward questions
Medium	Balanced reasoning.	Most everyday tasks
High	Thorough reasoning.	Complex analysis, multi-step problems
Max	Maximum reasoning depth. Highest latency and cost.	Research, detailed technical analysis

How Reasoning Effort is Applied

The reasoning effort level is resolved using this priority:

AI Model override — If a custom AI model has a reasoning effort configured, that takes precedence
Institution setting — The institution-level default reasoning effort
Platform default — None (thinking disabled)

Reasoning effort is mapped to each provider’s native format automatically — OpenAI reasoning_effort, Anthropic budget_tokens, Gemini thinkingConfig, and Bedrock budgetTokens. You don’t need to configure provider-specific parameters.

Models with Thinking Support

Not all models support extended thinking. Look for models marked with thinking support in the tables above. Currently supported thinking models include:

Anthropic: Claude Opus 4.6, Opus 4.5, Sonnet 4.6, Sonnet 4.5, Sonnet 4, Claude 3.7 Sonnet, Haiku 4.5 (via Bedrock or Direct API)
OpenAI: GPT-5 series (5.2, 5.1, 5-mini, 5-nano), o-series (o4-mini, o3, o3-mini, o1)
Google Gemini: Gemini 3.1 Pro Preview, Gemini 3 Flash/Pro Preview, Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.5 Flash Lite

Provider Types

Praxis AI routes AI requests through four backend providers:

Provider	How It Works
Amazon Bedrock	Models hosted on AWS infrastructure. Uses IAM credentials for authentication.
OpenAI API	Direct OpenAI API calls. Used for OpenAI models and OpenAI-compatible endpoints.
Anthropic Direct API	Direct Anthropic API calls. Bypasses Bedrock for Claude models when preferred.
Google GenAI	Direct Google Gemini API calls via the `@google/genai` SDK.

Some model families (e.g., Anthropic Claude) are available through multiple providers — both via Bedrock and via Direct API. The admin can choose which provider to use based on latency, cost, and regional availability preferences.

Bring Your Own AI Model (BYOM)

You can connect your own hosted LLM (for example, a model deployed on Google Vertex AI, private OpenAI-compatible endpoint, or a Bedrock-hosted custom model) and use it as a replacement for any of the supported usages.

Configure a Custom Model

To add a custom model for Conversation (or any other use):

In the Admin UI, edit your Digital Twin.
Under Personalization and AI Models, click Add AI Model.

In the Add AI Model panel, enter the properties required to connect to your LLM:

Model Name The exact model identifier published by your hosting platform. This value is case sensitive and must match your provider’s model name, for example: gemini-flash or projects/my-proj/locations/us/models/my-model.
Status Active models are considered by the system for routing and selection. Inactive models are ignored but kept in configuration.
Description Human-readable description of the LLM for admins and authors using this Digital Twin.
Model Use The specific usage for this model (for example, Conversation, Image Generation, Document Summarization). This determines which internal calls will use this model.
Client Library Type Choose from:
- Open AI for OpenAI-compatible endpoints (including many custom or Vertex AI gateways exposing an OpenAI-style API).
- Bedrock for Amazon Bedrock-hosted models. Most Gemini-based models connected through an OpenAI-compatible proxy should use Open AI.
API URL The base public URL of your model endpoint, for example: https://ai.my-school.edu or your Bedrock-compatible endpoint. Typically, the model name or ID is appended to this base URL when interacting with the LLM.
API Key The secret key used to authenticate requests to your endpoint. Keep this key secure and confidential; rotate it periodically for security.

Click Save to register the new custom AI model.

Once saved:

The model appears in the list of custom AI models.
For its configured Model Use, it will replace the platform default model.
All conversations or tasks mapped to that Model Use will start using your custom model without any client-side code changes.

Use a non-production Digital Twin first to validate latency, cost, and behavior of your custom model before assigning it to high-traffic or mission-critical usages.

End-to-End Workflow

Configure Provider Credentials

Go to Configuration → Personalization and AI Models and enter API keys and endpoints for each provider you plan to use (OpenAI-compatible, Bedrock, or custom gateways).

Select Models per Usage

For each Model Use (Conversation, Image, Audio, etc.), select the preferred model from the list of available platform and custom models.

Enable and Test Your Digital Twin

Use the Test or preview mode to run conversations against your updated configuration. Validate:

Response quality
Latency
Tool and streaming support (for Conversation models)

Monitor and Optimize

Use Analytics to track token usage, latency, and error rates per model. Adjust your model selection or routing preferences to balance performance and cost.

Scale to Production

Once validated, deploy your Digital Twin to users through LMS integration (e.g., Canvas), Web SDK, or REST APIs—no additional code changes required when switching models.

Connect New Digital Twins

Repeat the configuration setup for any additional twins so they can connect to the same custom LLM

Need help choosing models or configuring BYOM? Praxis AI supports multi-LLM orchestration and can route across OpenAI, Anthropic, Amazon, Google, Mistral, and your own hosted models in a single Digital Twin configuration.

Getting Started

Account Management

Instance Settings

AI & Intelligence

User Management

Analytics & Monitoring

Model Usage

How Praxis AI Uses Models