# LlamaIndex (/docs/guides/llamaindex)

Use cheapaimodel models in LlamaIndex through the OpenAI-compatible API.



LlamaIndex reaches OpenAI-compatible services through `OpenAILike` from the `llama-index-llms-openai-like` package. It can't look up a model's details from an ID it doesn't know, so you set two of them yourself: that the model is a chat model, and its context window.

|                | LlamaIndex setting | Value                                                                             |
| -------------- | ------------------ | --------------------------------------------------------------------------------- |
| Base URL       | `api_base`         | `https://api.cheapaimodel.ai/v1`                                                  |
| API key        | `api_key`          | Your key from the console's [API Keys](https://console.cheapaimodel.ai/keys) page |
| Model ID       | `model`            | `deepseek-v4-1-flash`, or any ID from the [catalog](/models)                      |
| Chat model     | `is_chat_model`    | `True`                                                                            |
| Context window | `context_window`   | The model's context length in tokens, from its page in the [catalog](/models)     |

## Set it up [#set-it-up]

The current LlamaIndex packages need Python 3.10 or later.

```bash title="Terminal"
pip install llama-index-llms-openai-like
```

Look up the context length on the model's page, for example [deepseek-v4-1-flash](/models/deepseek-v4-1-flash), and put it in `CONTEXT_WINDOW`:

```python title="main.py"
import os
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai_like import OpenAILike

CONTEXT_WINDOW = ...  # the context length from the model's page, in tokens

llm = OpenAILike(
    model="deepseek-v4-1-flash",
    api_base="https://api.cheapaimodel.ai/v1",
    api_key=os.environ["CHEAPAIMODEL_API_KEY"],
    is_chat_model=True,
    context_window=CONTEXT_WINDOW,
)

response = llm.complete("Explain recursion in one sentence.")
print(response.text)

# Streaming
messages = [ChatMessage(role="user", content="Write a haiku about the sea.")]
for chunk in llm.stream_chat(messages):
    print(chunk.delta, end="", flush=True)
```

With `is_chat_model=True`, every call, `complete` included, goes to `/v1/chat/completions`.

## Use it across LlamaIndex [#use-it-across-llamaindex]

Set it on `Settings` to make it the default for indexes, query engines and chat engines:

```python title="main.py"
from llama_index.core import Settings

Settings.llm = llm
```

Vector indexes also need an embedding model, and `Settings.embed_model` defaults to OpenAI's. cheapaimodel has no embeddings models yet, so set `Settings.embed_model` to another provider's embedding model or a local one. Don't point it at our base URL.

## Common mistakes [#common-mistakes]

* **Requests go to `/v1/completions`.** `is_chat_model` defaults to `False`, which sends `complete` and even `chat` to the legacy completions endpoint as a single prompt. Our models list only `/v1/chat/completions` and `/v1/responses`. Set `is_chat_model=True`.
* **`ValueError: Unknown model 'deepseek-v4-1-flash'. Please provide a valid OpenAI model name`.** The LLM is `OpenAI` from `llama_index.llms.openai`, which accepts only OpenAI's own model names. Use `OpenAILike`.
* **`Could not load OpenAI embedding model` when building an index.** `Settings.embed_model` is still the OpenAI default. Set it to another provider's or a local embedding model.
* **`503` with `model_not_found` on `/v1/embeddings`.** Same cause, with `OPENAI_API_KEY` and `OPENAI_API_BASE` set to your cheapaimodel key and our base URL: the default embedding model reads them and asks us for an embeddings model. Pass `api_key` and `api_base` to `OpenAILike` instead, and set `Settings.embed_model`.
* **`400` from `kimi-k3`.** `OpenAILike` always sends `temperature`, `0.1` by default, and kimi-k3 accepts only `1` or `0.6`. Pass `temperature=1.0`. See [Sampling limits](/docs/concepts/models#sampling-limits).
* **One query makes several requests.** `context_window` defaults to 3900 tokens, and LlamaIndex splits retrieved text to fit, so a query engine sends more requests than it needs to. Set it to the model's context length.

For base URL mistakes such as a missing `/v1`, see [Base URL](/docs/api/base-url#common-mistakes).