> For the complete documentation index, see [llms.txt](/llms.txt).
> Markdown versions of each page are available by appending .md to any URL.

# Agent model choice

Choose from a curated set of top LLMs for Warp's Agents (or let Warp auto-select the best model).

Warp lets you choose from a curated set of large language models to power your agents, or let Warp auto-select the best model for each task. Models from OpenAI, Anthropic, Google, and open source providers are available, with configurable reasoning levels and per-profile defaults.

## Available models

Warp lets you choose from a curated set of Large Language Models (LLMs) to power your Agentic Development Environment.

**Warp supports the following models.**

The `model_id` values shown below can be used when configuring models via the [Oz Platform](/agent-platform/cloud-agents/platform/) or [CLI](/reference/cli/).

### Auto models

| Model | `model_id` | Description |
| --- | --- | --- |
| Auto (Responsive) | `auto` | Selects the highest-quality, fastest available model. May consume credits more quickly. |
| Auto (Cost-efficient) | `auto-efficient` | Optimizes for lower credit consumption while maintaining strong output quality. |
| Auto (Genius) | `auto-genius` | Adapts to task complexity and selects Warp’s most capable model when it’s worth it. Best for deep debugging, architecture decisions, and `/plan` sessions. |
| Auto (Open-weights) | `auto-open` | Routes between the best open-source models available in Warp. Optimizes for low cost and fast speed using open-weights models. |

All Auto models perform well across all agent workflows and are ideal if you prefer Warp to manage model selection dynamically.

#### OpenAI

| Model | `model_id` | Reasoning Level |
| --- | --- | --- |
| GPT-5.5 | `gpt-5-5-low` | Low |
| GPT-5.5 | `gpt-5-5-medium` | Medium |
| GPT-5.5 | `gpt-5-5-high` | High |
| GPT-5.5 | `gpt-5-5-xhigh` | Extra High |
| GPT-5.4 | `gpt-5-4-low` | Low |
| GPT-5.4 | `gpt-5-4-medium` | Medium |
| GPT-5.4 | `gpt-5-4-high` | High |
| GPT-5.4 | `gpt-5-4-xhigh` | Extra High |
| GPT-5.3 Codex | `gpt-5-3-codex-low` | Low |
| GPT-5.3 Codex | `gpt-5-3-codex-medium` | Medium |
| GPT-5.3 Codex | `gpt-5-3-codex-high` | High |
| GPT-5.3 Codex | `gpt-5-3-codex-xhigh` | Extra High |
| GPT-5.2 Codex | `gpt-5-2-codex-low` | Low |
| GPT-5.2 Codex | `gpt-5-2-codex-medium` | Medium |
| GPT-5.2 Codex | `gpt-5-2-codex-high` | High |
| GPT-5.2 Codex | `gpt-5-2-codex-xhigh` | Extra High |
| GPT-5.2 | `gpt-5-2-low` | Low |
| GPT-5.2 | `gpt-5-2-medium` | Medium |
| GPT-5.2 | `gpt-5-2-high` | High |
| GPT-5.2 | `gpt-5-2-xhigh` | Extra High |

#### Anthropic

| Model | `model_id` | Variant |
| --- | --- | --- |
| Claude Opus 4.7 | `claude-4-7-opus-xhigh` | Default effort |
| Claude Opus 4.7 | `claude-4-7-opus-high` | High effort |
| Claude Opus 4.7 | `claude-4-7-opus-max` | Max effort |
| Claude Opus 4.6 | `claude-4-6-opus-high` | Default effort |
| Claude Opus 4.6 | `claude-4-6-opus-max` | Max effort |
| Claude Sonnet 4.6 | `claude-4-6-sonnet-high` | Default effort |
| Claude Sonnet 4.6 | `claude-4-6-sonnet-max` | Max effort |
| Claude Opus 4.5 | `claude-4-5-opus` | Thinking off |
| Claude Opus 4.5 | `claude-4-5-opus-thinking` | Thinking on |
| Claude Sonnet 4.5 | `claude-4-5-sonnet` | Thinking off |
| Claude Sonnet 4.5 | `claude-4-5-sonnet-thinking` | Thinking on |
| Claude Haiku 4.5 | `claude-4-5-haiku` | — |

#### Google

| Model | `model_id` |
| --- | --- |
| Gemini 3.1 Pro | `gemini-3.1-pro` |

#### Hosted models (via [Fireworks AI](https://fireworks.ai))

Warp also supports leading open source models hosted via Fireworks AI, so you can run them from inside Warp without setting up your own inference infrastructure.

| Model | `model_id` |
| --- | --- |
| GLM 5.1 | `glm-5.1-fireworks` |
| Kimi K2.5 | `kimi-k25-fireworks` |
| Kimi K2.6 | `kimi-k26-fireworks` |
| Minimax 2.7 | `minimax-2.7-fireworks` |
| Qwen 3.6 Plus | `qwen-3.6-plus-fireworks` |

### How to change models

You can use the model picker in your prompt input to quickly switch between models. The currently active model appears directly in the input editor.

![Model selector dropdown showing available models with Intelligence, Speed, and Cost benchmarks](/_astro/model-selector-dropdown.C5X4qk_B_1dmhsq.webp?dpl=dpl_4vCZzeekRJioNeuBPoaTvj21NeLw)

Model selector in Warp’s input.

To change models, click the displayed model name (for example, *Claude Sonnet 4.5*) to open a dropdown with all supported options. Your selection will automatically persist for future prompts.

### Model fallback

Warp uses a model fallback system to ensure uninterrupted service if your selected model becomes temporarily unavailable due to provider outages or capacity issues.

**How it works:**

-   If your selected model isn’t available, Warp automatically uses a fallback model from a predefined chain to continue your conversation without errors.
-   As soon as your originally selected model becomes available again, Warp automatically switches back to it.
-   The fallback model is selected to provide comparable quality and capabilities to your original choice.

### Configuring models per Agent Profile

You can configure the base model for each [Agent Profile](/agent-platform/capabilities/agent-profiles-permissions/), alongside the Agent’s autonomy, tool access, and other permissions. The base model is also used for [Planning](/agent-platform/capabilities/planning/).

Edit your default profile or any other profile directly in **Settings** > **Agents** > **Profiles**.

### Zero data retention policies

Warp integrates with multiple Large Language Model (LLM) providers to power its AI-driven features.

**These providers include, but are not limited to:**

-   OpenAI
-   Anthropic
-   Google
-   xAI
-   Fireworks AI

Warp has executed **Zero Data Retention (ZDR)** agreements with these providers. This means that, by default across all plans:

-   LLM providers commit not to train their models on any customer-generated data processed through Warp’s services.
-   LLM providers commit to delete inputs and outputs after generating the relevant output, within a fixed time period.

Warp enforces these commitments through both technical measures and contractual safeguards with the LLM providers.