# OpenAI
URL: /reference/llm-providers/openai

OpenAI provides a comprehensive suite of language models optimized for different use cases, from high-intelligence reasoning to cost-efficient production tasks. Tambo supports the full range of OpenAI models, including the latest GPT-5 series with advanced reasoning capabilities.

## Supported Models

Tambo supports 18 OpenAI models organized into five families:

### GPT-5 Family

The latest generation of OpenAI models with advanced reasoning capabilities and massive context windows.

#### gpt-5.4

**Status:** Tested
**API Name:** `gpt-5.4`
**Context Window:** 1,050,000 tokens
**Provider Docs:** [OpenAI GPT-5.4](https://platform.openai.com/docs/models/gpt-5.4)

Current flagship model with 1M+ context, native computer-use capabilities, and 33% fewer hallucinations vs GPT-5.2.

**Best for:**

* Tasks requiring massive context (1M+ tokens)
* Computer-use and agentic workflows
* Applications where factual accuracy is critical
* Complex multi-step reasoning with large codebases

**Notes:** Released March 2026. The most capable GPT model available. Use [`reasoningEffort`](#reasoningeffort) and [`reasoningSummary`](#reasoningsummary) parameters to control thinking behavior.

#### gpt-5.4-pro

**Status:** Tested
**API Name:** `gpt-5.4-pro`
**Context Window:** 1,050,000 tokens
**Provider Docs:** [OpenAI GPT-5.4 Pro](https://platform.openai.com/docs/models/gpt-5.4-pro)

Maximum capability variant of GPT-5.4. Uses more compute for harder problems. Requests may take several minutes.

**Best for:**

* The hardest reasoning and coding tasks
* High-stakes professional knowledge work
* Complex multi-step projects requiring maximum accuracy
* Tasks where quality matters more than latency

**Notes:** Released March 2026. Highest-compute variant — expect longer response times. Use [`reasoningEffort`](#reasoningeffort) and [`reasoningSummary`](#reasoningsummary) parameters to control thinking behavior.

#### gpt-5.3-chat-latest

**Status:** Tested
**API Name:** `gpt-5.3-chat-latest`
**Context Window:** 128,000 tokens
**Provider Docs:** [OpenAI GPT-5.3 Chat Latest](https://platform.openai.com/docs/models/gpt-5.3-chat-latest)

Conversational model optimized for everyday use. Does not support reasoning parameters.

**Best for:**

* Conversational applications requiring natural responses
* Everyday chat interactions
* Applications where low latency matters
* Use cases that don't require extended reasoning

**Notes:** Points to the GPT-5.3 Instant snapshot used in ChatGPT. Does not support reasoning parameters.

#### gpt-5.2-pro

**Status:** Tested
**API Name:** `gpt-5.2-pro`
**Context Window:** 400,000 tokens
**Provider Docs:** [OpenAI GPT-5.2 Pro](https://platform.openai.com/docs/models/gpt-5.2-pro)

Highest-compute GPT-5.2 variant, optimized for complex reasoning and professional knowledge work.

**Best for:**

* Complex reasoning tasks requiring maximum GPT-5.2 capability
* Professional knowledge work (spreadsheets, presentations, code)
* Long-context analysis and generation
* Tasks where GPT-5.4 Pro is overkill but GPT-5.2 base isn't enough

**Notes:** Released December 2025. Use [`reasoningEffort`](#reasoningeffort) and [`reasoningSummary`](#reasoningsummary) parameters to control thinking behavior.

#### gpt-5.2

**Status:** Tested
**API Name:** `gpt-5.2`
**Context Window:** 400,000 tokens
**Provider Docs:** [OpenAI GPT-5.2](https://platform.openai.com/docs/models/gpt-5.2)

GPT-5.2 model with improved capabilities. Supports reasoning parameters for exposing internal thought processes.

**Best for:**

* Advanced coding and agentic tasks
* Complex multi-step reasoning workflows
* Tasks requiring cutting-edge model capabilities
* Applications needing the latest improvements

**Notes:** Use [`reasoningEffort`](#reasoningeffort) and [`reasoningSummary`](#reasoningsummary) parameters to control thinking behavior.

#### gpt-5

**Status:** Tested
**API Name:** `gpt-5-2025-08-07`
**Context Window:** 400,000 tokens
**Provider Docs:** [OpenAI GPT-5](https://platform.openai.com/docs/models/gpt-5)

The flagship GPT-5 model, best for coding and agentic tasks across domains. Supports reasoning parameters for exposing internal thought processes.

**Best for:**

* Complex coding tasks and refactoring
* Agentic workflows requiring multi-step reasoning
* Tasks requiring deep analysis and problem-solving
* Applications where showing reasoning builds trust

**Notes:** The most powerful model for reasoning-intensive tasks. Use [`reasoningEffort`](#reasoningeffort) and [`reasoningSummary`](#reasoningsummary) parameters to control thinking behavior.

#### gpt-5-mini

**Status:** Tested
**API Name:** `gpt-5-mini-2025-08-07`
**Context Window:** 400,000 tokens
**Provider Docs:** [OpenAI GPT-5 Mini](https://platform.openai.com/docs/models/gpt-5-mini)

A faster, more cost-efficient version of GPT-5 for well-defined tasks.

**Best for:**

* Production applications requiring balance of intelligence and cost
* Well-scoped tasks with clear requirements
* High-volume reasoning workloads
* Applications where latency matters

**Notes:** Maintains GPT-5's reasoning capabilities while optimizing for speed and cost. Ideal for production deployments.

#### gpt-5-nano

**Status:** Tested
**API Name:** `gpt-5-nano-2025-08-07`
**Context Window:** 400,000 tokens
**Provider Docs:** [OpenAI GPT-5 Nano](https://platform.openai.com/docs/models/gpt-5-nano)

The fastest, most cost-efficient version of GPT-5.

**Best for:**

* High-volume applications requiring reasoning at scale
* Simple reasoning tasks
* Latency-sensitive applications
* Cost-optimized production deployments

**Notes:** Smallest GPT-5 variant, optimized for efficiency while retaining reasoning capabilities.

#### gpt-5.1

**Status:** Tested (Default Model)
**API Name:** `gpt-5.1`
**Context Window:** 400,000 tokens
**Provider Docs:** [OpenAI Latest Model](https://platform.openai.com/docs/guides/latest-model)

GPT-5.1 with adaptive reasoning. Dynamically varies thinking time based on task complexity for better token efficiency.

**Best for:**

* Tasks with variable complexity
* Applications requiring intelligent cost optimization
* Complex problem-solving with automatic effort adjustment
* Production systems handling diverse query types

**Notes:** Released November 2025. Adaptive reasoning automatically adjusts thinking time, optimizing cost without sacrificing quality on complex tasks.

#### gpt-5.1-chat-latest

**Status:** Tested
**API Name:** `gpt-5.1-chat-latest`
**Context Window:** 400,000 tokens
**Provider Docs:** [OpenAI Latest Model](https://platform.openai.com/docs/guides/latest-model)

GPT-5.1 Chat Latest - warmer, more conversational model with adaptive reasoning.

**Best for:**

* Conversational applications requiring natural responses
* Latency-sensitive chat interfaces
* Applications balancing warmth and intelligence
* Real-time interactions with optional reasoning

**Notes:** Released November 2025. Conversational variant with improved responsiveness.

### GPT-4.1 Family

High-intelligence models excelling at function calling and instruction following with massive context windows.

#### gpt-4.1

**Status:** Tested
**API Name:** `gpt-4.1-2025-04-14`
**Context Window:** 1,047,576 tokens
**Provider Docs:** [OpenAI GPT-4.1](https://platform.openai.com/docs/models/gpt-4.1)

Excels at function calling and instruction following.

**Best for:**

* Function calling and tool use
* Following complex instructions precisely
* General-purpose applications
* Large context requirements (1M+ tokens)

**Notes:** Balances intelligence, reliability, and cost. Ideal for most production applications.

#### gpt-4.1-mini

**Status:** Tested
**API Name:** `gpt-4.1-mini-2025-04-14`
**Context Window:** 1,047,576 tokens
**Provider Docs:** [OpenAI GPT-4.1 Mini](https://platform.openai.com/docs/models/gpt-4.1-mini)

Balanced for intelligence, speed, and cost.

**Best for:**

* Production applications requiring cost efficiency
* High-volume workloads
* Applications balancing quality and performance
* Large context with cost constraints

**Notes:** Offers excellent value proposition with maintained quality at reduced cost.

#### gpt-4.1-nano

**Status:** Tested
**API Name:** `gpt-4.1-nano-2025-04-14`
**Context Window:** 1,047,576 tokens
**Provider Docs:** [OpenAI GPT-4.1 Nano](https://platform.openai.com/docs/models/gpt-4.1-nano)

Fastest, most cost-efficient version of GPT-4.1.

**Best for:**

* Maximum throughput applications
* Simple, well-defined tasks
* Cost-critical deployments
* High-volume production systems

**Notes:** Validated on common Tambo tasks including streaming responses and generative UI components. Some edge cases require explicit prompts and are documented.

### o3 Family

Specialized reasoning model for complex problem-solving.

#### o3

**Status:** Tested
**API Name:** `o3-2025-04-16`
**Context Window:** 200,000 tokens
**Provider Docs:** [OpenAI o3](https://platform.openai.com/docs/models/o3)

The most powerful reasoning model available.

**Best for:**

* Mathematical proofs and calculations
* Complex code review and debugging
* Strategic planning requiring deep analysis
* Research and analysis tasks
* Any task where showing reasoning is critical

**Notes:** Dedicated reasoning model with the most powerful thinking capabilities. Higher latency and cost but unmatched reasoning depth.

### GPT-4o Family

Versatile multimodal models with text and image input support.

#### gpt-4o

**Status:** Tested
**API Name:** `gpt-4o-2024-11-20`
**Context Window:** 128,000 tokens
**Provider Docs:** [OpenAI GPT-4o](https://platform.openai.com/docs/models/gpt-4o)

Versatile and high-intelligence model with text and image input support. Best for most tasks, combining strong reasoning, creativity, and multimodal understanding.

**Best for:**

* Multimodal applications (text + images)
* Creative tasks requiring nuanced understanding
* General-purpose applications requiring versatility
* Tasks requiring both analysis and generation

**Notes:** Excellent all-around model with multimodal capabilities. Strong choice when you need both text and image understanding.

#### gpt-4o-mini

**Status:** Tested
**API Name:** `gpt-4o-mini-2024-07-18`
**Context Window:** 128,000 tokens
**Provider Docs:** [OpenAI GPT-4o Mini](https://platform.openai.com/docs/models/gpt-4o-mini)

Fast, affordable model ideal for focused tasks and fine-tuning. Supports text and image inputs, with low cost and latency for efficient performance.

**Best for:**

* Cost-sensitive multimodal applications
* Fine-tuning for specific use cases
* High-volume image analysis
* Production deployments prioritizing efficiency

**Notes:** Most efficient multimodal option with excellent performance-to-cost ratio.

## Provider-Specific Parameters

OpenAI reasoning models support specialized parameters to control their thinking behavior.

### Reasoning Parameters

Configure reasoning capabilities through your project's LLM provider settings in the [dashboard](#configuration).

#### reasoningEffort

**Type:** `string`
**Values:** `"none"`, `"low"`, `"medium"`, `"high"`
**Description:** Controls the intensity of the model's reasoning process. Only effective when [`reasoningSummary`](#reasoningsummary) is also set.

* **`"none"`** - No extended reasoning (fastest)
* **`"low"`** - Light reasoning for simpler tasks (faster, cheaper)
* **`"medium"`** - Balanced reasoning for most use cases (recommended)
* **`"high"`** - Deep reasoning for complex problems (slower, more expensive)

**Default Values by Model:**

| Model                                  | Default    |
| -------------------------------------- | ---------- |
| gpt-5.4                                | `"low"`    |
| gpt-5.4-pro, gpt-5.2-pro               | `"medium"` |
| gpt-5.1                                | `"none"`   |
| gpt-5.1-chat-latest                    | `"medium"` |
| gpt-5.2, gpt-5, gpt-5-mini, gpt-5-nano | `"low"`    |
| o3                                     | `"medium"` |

#### reasoningSummary

**Type:** `string`
**Values:** `"auto"`, `"detailed"`
**Description:** Enables reasoning token output, allowing you to see the model's internal thought process.

* **`"auto"`** - Automatically determines appropriate reasoning detail level
* **`"detailed"`** - Provides more comprehensive reasoning output

**Example Configuration:**

In your Tambo Cloud dashboard under **Settings** → **LLM Providers** → **Custom LLM Parameters**:

```
reasoningEffort: "medium"
reasoningSummary: "auto"
```

### Supported Reasoning Models

Reasoning parameters are available for the following OpenAI models:

* GPT-5.4 (`gpt-5.4`)
* GPT-5.4 Pro (`gpt-5.4-pro`)
* GPT-5.2 Pro (`gpt-5.2-pro`)
* GPT-5.2 (`gpt-5.2`)
* GPT-5.1 (`gpt-5.1`)
* GPT-5.1 Chat Latest (`gpt-5.1-chat-latest`)
* GPT-5 (`gpt-5-2025-08-07`)
* GPT-5 Mini (`gpt-5-mini-2025-08-07`)
* GPT-5 Nano (`gpt-5-nano-2025-08-07`)
* o3 (`o3-2025-04-16`)

## Configuration

### Setting Up OpenAI in Tambo

1. Navigate to your project in the Tambo Cloud dashboard
2. Go to **Settings** → **LLM Providers**
3. Select **OpenAI** as your provider
4. Choose your desired model from the dropdown
5. Configure any provider-specific parameters (reasoning, temperature, etc.)
6. Click **Save**

### Dashboard Features

When you select an OpenAI reasoning model, the dashboard automatically suggests relevant parameters:

* [**reasoningEffort**](#reasoningeffort) - Suggested for all reasoning models
* [**reasoningSummary**](#reasoningsummary) - Suggested for all reasoning models

Simply click the suggested parameter to add it to your configuration, set the desired value, and save.

### Best Practices

**Model Selection:**

* Use **gpt-5.1** for most production applications (default)
* Use **gpt-5.4** or **gpt-5.4-pro** for maximum capability and 1M+ context
* Use **gpt-5.3-chat-latest** for low-latency conversational use cases
* Use **gpt-4o** for multimodal applications
* Use **mini/nano** variants for cost-optimized deployments
* Use **o3** for maximum reasoning depth

**Reasoning Configuration:**

* Start with [`reasoningEffort: "medium"`](#reasoningeffort) for balanced performance
* Set [`reasoningSummary: "auto"`](#reasoningsummary) to enable reasoning display
* Increase effort for complex tasks, decrease for simple queries
* Monitor token usage as reasoning consumes additional tokens

**Production Considerations:**

* Test untested models thoroughly before production deployment
* Monitor costs with reasoning parameters enabled
* Use separate projects for different reasoning requirements
* Consider using non-reasoning models for high-volume simple tasks

## See Also

* [Labels](/reference/llm-providers/labels) - Understanding model status labels and observed behaviors
* [Custom LLM Parameters](/guides/setup-project/llm-provider) - Configuring temperature, max tokens, and other parameters
* [Reasoning Models](/reference/llm-providers/reasoning-models) - Deep dive into reasoning capabilities for OpenAI and Gemini models