> For the complete documentation index, see [llms.txt](https://guide.indigo.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://guide.indigo.ai/getting-started/ai-knowledge-hub/large-language-models-llms-available-on-our-platform.md).

# Large Language Models (LLMs) Available on Our Platform

Large Language Models (LLMs) play a crucial role in powering the AI Agents and workflows within **indigo.ai**. These models enable natural language understanding, reasoning, and content generation, ensuring businesses can automate interactions and provide intelligent, context-aware responses. This article explores the LLMs available in **indigo.ai**, their capabilities, and best practices for selecting the right model for your needs.

## Understanding LLMs in indigo.ai

At indigo.ai, we integrate multiple LLMs to offer a **flexible, high-performance AI ecosystem**. Different models are optimized for **speed or power**, allowing users to choose the best fit for their use case.

Here’s how we categorize them:

<table><thead><tr><th>Speed ⚡</th><th>Power 🚀</th><th>Reasoning 🧠</th><th data-hidden></th></tr></thead><tbody><tr><td><strong>gpt-4.1-mini</strong></td><td><strong>gpt-4.1</strong></td><td><strong>gpt-5.1</strong></td><td><strong>Balanced</strong> ⚖️</td></tr><tr><td>gpt-4.1-nano</td><td>gemini-2.5-flash</td><td>gpt-5-mini</td><td>gpt-4o-mini</td></tr><tr><td>gemini-2.5-flash-lite</td><td>claude-4.5-sonnet</td><td>gpt-5-nano</td><td>gemini-1.5-flash</td></tr><tr><td>mistral-small-3.2</td><td>gpt-oss-120b</td><td>gemini-2.5-pro</td><td></td></tr><tr><td>claude-4.5-haiku</td><td></td><td></td><td></td></tr><tr><td>gpt-oss-20b</td><td></td><td></td><td></td></tr></tbody></table>

## **LLM Categories and Their Use Cases**

* **Speed:** Models that prioritize response time over advanced reasoning. Best for real-time interactions where immediate feedback is essential.
* **Power:** High-performance models with **strong generative capabilities**, designed for complex tasks but with longer response times.
* **Reasoning**: Models designed to generate a reasoning process before providing an answer. This feature makes them capable of solving complex tasks that require deep reasoning, at the cost of higher latency.

Models in **bold** within each category represent the **recommended models** based on performance and reliability.

## List of LLMs in indigo.ai

### **Available Models, Providers, and Server Locations**

<table><thead><tr><th width="208.94140625">Model Name in Platform</th><th width="164.7109375">LLM Backend</th><th width="130.703125">Provider</th><th width="141.41796875">Server Location</th><th>Comment</th></tr></thead><tbody><tr><td>gpt-4.1-mini (EU)</td><td>gpt-4.1-mini-2025-04-14</td><td>Microsoft Azure</td><td>Sweden</td><td>Default</td></tr><tr><td>gpt-4.1 (EU)</td><td>gpt-4.1-2025-04-14</td><td>Microsoft Azure</td><td>Sweden</td><td></td></tr><tr><td>gpt-4.1-nano (EU)</td><td>gpt-4.1-nano-2025-04-14</td><td>Microsoft Azure</td><td>Sweden</td><td></td></tr><tr><td>gpt-5.1 (EU)</td><td>azure-se-gpt-5.1</td><td>Microsoft Azure</td><td>Sweden</td><td></td></tr><tr><td>gpt-5-mini (EU)</td><td>azure-se-gpt-5-mini</td><td>Microsoft Azure</td><td>Sweden</td><td></td></tr><tr><td>gpt-5-nano (EU)</td><td>azure-se-gpt-5-nano</td><td>Microsoft Azure</td><td>Sweden</td><td></td></tr><tr><td>gemini-2.5-pro (EU)</td><td>gemini-2.5-pro</td><td>Google</td><td>Belgium</td><td></td></tr><tr><td>gemini-2.5-flash (EU)</td><td>gemini-2.5-flash</td><td>Google</td><td>Belgium</td><td></td></tr><tr><td>gemini-2.5-flash-lite (EU)</td><td>gemini-2.5-flash-lite</td><td>Google</td><td>Belgium</td><td><br></td></tr><tr><td>claude-4.5-sonnet (EU)</td><td>claude-sonnet-4-5@20250929</td><td>GoogleVertex</td><td>Belgium</td><td></td></tr><tr><td>claude-4.5-haiku (EU)</td><td>claude-haiku-4-5@20251001</td><td>GoogleVertex</td><td>Belgium</td><td></td></tr><tr><td>mistral-small-3.2 (EU)</td><td>mistral-small-2506</td><td>Mistral</td><td>Sweden</td><td></td></tr><tr><td>gpt-oss-120b (EU)</td><td>openai/gpt-oss-120b</td><td>Groq</td><td>EU</td><td>Open-weight, high throughput</td></tr><tr><td>gpt-oss-20b (EU)</td><td>openai/gpt-oss-20b</td><td>Groq</td><td>EU</td><td>Open-weight, low latency</td></tr><tr><td>maestrale-chat (self-hosted)</td><td>hf.co/mii-llm/maestrale-chat-v0.4-beta-GGUF</td><td>indigo.ai</td><td>Germany</td><td></td></tr></tbody></table>

{% hint style="info" %}
**Groq-hosted open-weight models** (`gpt-oss-120b`, `gpt-oss-20b`) are served on Groq infrastructure with EU data residency. They offer significantly lower latency than equivalent-size proprietary models, which makes them a strong fit for real-time interactions (e.g. voice channel, high-traffic web chat).
{% endhint %}

## **Default Model in indigo.ai**

By default, we use **azure-gpt-4.1-mini (EU)** in our AI Agents and workflows. This model is selected because:

* ✅ It offers a strong balance between **performance and response time**.
* ✅ It is hosted on **Microsoft Azure EU servers**, ensuring **compliance with European data regulations**.
* ✅ It supports **advanced reasoning capabilities** while maintaining a reasonable token cost and latency.

However, you can choose to use **different models** based on your specific requirements.

## **How to Choose the Right Model**

Selecting the best model depends on several factors, including response speed, accuracy, reasoning ability, and token consumption. Here are some guidelines:

**1. Prioritize Speed (Fastest Response Time)**

Use **gpt-4.1-mini** if:

✔ You need real-time responses.\
✔ Your use case involves quick user interactions.\
✔ Advanced reasoning is not the top priority.

**2. Prioritize Power**

Use **gpt-4.1** or **gpt-5.1** if:\
✔ You need deep contextual understanding.\
✔ Your use case involves complex responses (e.g., legal, medical, or technical AI agents).\
✔ You’re willing to trade speed for accuracy.

### Model Selector (New)

indigo.ai provides an enhanced model selector panel that helps users choose the most appropriate LLM directly from the interface.

Instead of a simple dropdown, the selector displays key information for each model to support informed decision-making.

<figure><img src="/files/enJYQcTPYgxIYoPOPoYQ" alt=""><figcaption></figcaption></figure>

#### What you can see

For each model, the selector shows:

* Pricing → input/output cost per million tokens
* Context window → maximum supported context size
* Price tier → Light, Standard, or Premium
* Deployment region → e.g. EU or US (when available)
* Recommended label → highlights suggested models

Models are grouped by provider (e.g. OpenAI, Google, Anthropic, Mistral), and can be searched by name.

#### Price tiers

To simplify model selection, each model is categorized into one of three tiers:

* Light → low-cost models, ideal for simple tasks and high-volume usage
* Standard → balanced models for most use cases
* Premium → top-tier models for complex and high-performance tasks

#### Where it is available

The model selector is available in all areas where a model can be selected:

* Agent global settings
* Agent block (Agent Builder)
* Prompt block (Agent Builder)

## Best Practices for Choosing an LLM in Prompts

**Impact of Model Selection on Performance**

When configuring your AI Agent in indigo.ai, the model you choose affects:

* **Response Length**: More powerful models generate more detailed responses but consume more tokens.
* **Accuracy**: Higher-end models provide better coherence and logical reasoning.
* **Speed**: Faster models provide instant replies but may lack depth in reasoning.

## Model Deprecation and Automatic Redirects

Model providers regularly retire older versions of their LLMs. When that happens, indigo.ai guarantees **zero-downtime migrations**: agents and workflows configured on a deprecated model are **automatically redirected to the recommended replacement** at runtime, with no manual intervention required.

**How it works:**

* Each deprecated model is mapped to a successor (typically the next recommended model in the same category — e.g. `gpt-4o-mini` → `gpt-4.1-mini`).
* When an agent invokes a deprecated model, the platform transparently serves the request with the replacement and logs the redirect for observability.
* In the model selector UI, deprecated models are marked as such and are visually distinguished so you can plan migrations proactively.

**What you should do:**

* Treat automatic redirects as a safety net, not a permanent solution. When you see a model flagged as deprecated, update the configuration to the recommended replacement to keep your prompts tuned for the actual model you are running on.
* Re-test prompts after migrating: newer models often respond to instructions differently, especially around output format and tone.

{% hint style="warning" %}
Redirects keep your agents running, but prompt behavior may shift. Plan a short QA pass whenever you migrate away from a deprecated model.
{% endhint %}

## **Conclusion**

The **indigo.ai platform** offers a **diverse selection of LLMs**, each optimized for different use cases. Whether you need **fast interactions, a balanced approach, or maximum reasoning power**, selecting the right model is key to optimizing your AI’s performance. By default, we recommend **azure-gpt-4.1-mini (EU)** for most workflows, but users can choose based on their specific requirements.

Understanding LLM capabilities allows businesses to build **smarter, more efficient AI Agents**, ensuring they meet customer expectations with high-quality automated interactions.