> ## Documentation Index
> Fetch the complete documentation index at: https://docs.xpander.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Multi-Model Reasoning
> Select different AI models for different agents and workflow steps based on what each task requires.
Xpander connects to multiple LLM providers through a single interface. You can assign different models to different parts of your system so a ticket classifier runs on a fast, cheap model while a customer response agent uses a frontier model.
## Supported providers
| Provider | Example models |
| -------------------- | ------------------------------------------------------ |
| **Anthropic** | Claude Opus 4.6 (T1), Sonnet 4.6 (T2), Sonnet 4.5 (T2) |
| **OpenAI** | GPT-5.4 (T1), GPT-5 (T2), GPT-5 Mini (T3), GPT-4o (T2) |
| **Google AI Studio** | Gemini models |
| **Amazon Bedrock** | Claude and Titan via AWS |
| **Azure AI Foundry** | Azure-hosted models |
| **Nvidia** | NIM-hosted models |
| **Fireworks** | Fast inference models |
| **OpenRouter** | Multi-provider routing |
The table above shows common examples. The full list varies by plan. Also available: Nebius Token Factory, Cloudflare AI Gateway, Tzafon LightCone, ByteDance ModelArk.
Each provider shows a **Featured** tab (curated models) and a **Custom** tab (enter any model ID). Models are labeled by tier: T1 (most capable), T2 (balanced), T3 (fast/cheap).
Use Xpander's built-in API access (no keys needed) or [bring your own credentials](/guides/agents/ai-models-intelligence#bring-your-own-keys) through Admin Settings (enterprise plans).
## Model selection hierarchy
More specific settings override broader ones:
```text theme={"dark"}
Org default (Admin Settings)
└── Per-agent (Agent > General > LLM Settings)
└── Per-workflow (Workflow > Settings > LLM Settings)
└── Per-node (Classifier / Summarizer / Guardrail)
```
**Org default:** New agents and workflows inherit this unless overridden. Configure in Admin Settings, LLM Settings tab.
**Per-agent:** Each agent picks its own provider and model in the General tab. This is where most model decisions happen.
**Per-workflow:** Applies to all AI nodes in a workflow unless a specific node overrides it.
**Per-node:** Classifier, Summarizer, and Guardrail nodes each have independent LLM settings in Advanced Configuration.
**Agent nodes don't have LLM settings.** They inherit the model from the agent you selected. Only Classifier, Summarizer, and Guardrail have per-node model selection.
## Pick the right tier for the task
| Tier | Cost | Speed | Use when |
| ------------------- | -------- | -------- | ------------------------------------------------------------------- |
| **T1** (frontier) | Highest | Slowest | Complex reasoning, customer-facing responses, compliance evaluation |
| **T2** (balanced) | Moderate | Moderate | Classification, summarization, data extraction. Best default. |
| **T3** (fast/cheap) | Lowest | Fastest | High-volume routing, sentiment detection, simple categorization |
### Example: mixed models in one workflow
```text theme={"dark"}
[Webhook trigger]
│
▼
[Classifier] ← T3 (GPT-5 Mini): route tickets by category
│
├── Billing ──► [Agent] ← T1 (Claude Opus): draft accurate refund response
│ │
│ ▼
│ [Guardrail] ← T2 (Claude Sonnet): check policy compliance
│
└── Technical ──► [Agent] ← T2 (Claude Sonnet): look up docs + draft fix
│
▼
[Summarizer] ← T3 (GPT-5 Mini): compress for internal log
```
The classifier runs on T3 because routing is straightforward. The billing agent uses T1 because refund responses need precision. The guardrail uses T2 for balanced quality and speed. The summarizer uses T3 because internal logs don't need frontier quality.
## Output formats
Each agent supports four output formats in the General tab: **Default** (text), **Markdown**, **Structured output** (JSON to a schema), and **Voice** (audio).
Structured output is particularly useful in multi-agent setups. A specialist returning JSON is more reliable for the coordinator to parse than free text.
## What's next
Coordinate specialized agents on shared tasks.
Backend automation on the visual canvas.