---
title: "OpenAI APIs - Completions"
metatags:
    description: "This tutorial covers the following popular APIs: 'chat/completions' and 'completions'"
---
SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.
A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/api-reference).

This tutorial covers the following popular APIs:

- `chat/completions`
- `completions`

Check out other tutorials to learn about [vision APIs](./openai_api_vision) for vision-language models and [embedding APIs](./openai_api_embeddings) for embedding models.

## Launch A Server

Launch the server in your terminal and wait for it to initialize.

```python Example
from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process

server_process, port = launch_server_cmd(
    "python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0 --log-level warning"
)

wait_for_server(f"http://localhost:{port}")
print(f"Server started on http://localhost:{port}")
```

## Chat Completions

### Usage

The server fully implements the OpenAI API.
It will automatically apply the chat template specified in the Hugging Face tokenizer, if one is available.
You can also specify a custom chat template with `--chat-template` when launching the server.

```python Example
import openai

client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")

response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)

print_highlight(f"Response: {response}")
```

### Model Thinking/Reasoning Support

Some models support internal reasoning or thinking processes that can be exposed in the API response. SGLang provides unified support for various reasoning models through the `chat_template_kwargs` parameter and compatible reasoning parsers.

#### Supported Models and Configuration

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25%"}} />
    <col style={{width: "25%"}} />
    <col style={{width: "25%"}} />
    <col style={{width: "25%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model Family</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Chat Template Parameter</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Reasoning Parser</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Notes</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>DeepSeek-R1 (R1, R1-0528, R1-Distill)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`enable_thinking`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`--reasoning-parser deepseek-r1`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Standard reasoning models</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>DeepSeek-V3.1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`thinking`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`--reasoning-parser deepseek-v3`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Hybrid model (thinking/non-thinking modes)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen3 (standard)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`enable_thinking`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`--reasoning-parser qwen3`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Hybrid model (thinking/non-thinking modes)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen3-Thinking</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A (always enabled)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`--reasoning-parser qwen3-thinking`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Always generates reasoning</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Kimi</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A (always enabled)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`--reasoning-parser kimi`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Kimi thinking models</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Gpt-Oss</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A (always enabled)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`--reasoning-parser gpt-oss`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Gpt-Oss thinking models</td>
    </tr>
  </tbody>
</table>

#### Basic Usage

To enable reasoning output, you need to:
1. Launch the server with the appropriate reasoning parser
2. Set the model-specific parameter in `chat_template_kwargs`
3. Optionally use `separate_reasoning: False` to not get reasoning content separately (default to `True`)

<Note>
**Note for Qwen3-Thinking models:** These models always generate thinking content and do not support the `enable_thinking` parameter. Use `--reasoning-parser qwen3-thinking` or `--reasoning-parser qwen3` to parse the thinking content.
</Note>

#### Example: Qwen3 Models

```python Example
# Launch server:
# python3 -m sglang.launch_server --model Qwen/Qwen3-4B --reasoning-parser qwen3

from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url=f"http://127.0.0.1:30000/v1",
)

model = "Qwen/Qwen3-4B"
messages = [{"role": "user", "content": "How many r's are in 'strawberry'?"}]

response = client.chat.completions.create(
    model=model,
    messages=messages,
    extra_body={
        "chat_template_kwargs": {"enable_thinking": True},
        "separate_reasoning": True
    }
)

print("Reasoning:", response.choices[0].message.reasoning_content)
print("-"*100)
print("Answer:", response.choices[0].message.content)
```

**ExampleOutput:**
```text Output
Reasoning: Okay, so the user is asking how many 'r's are in the word 'strawberry'. Let me think. First, I need to make sure I have the word spelled correctly. Strawberry... S-T-R-A-W-B-E-R-R-Y. Wait, is that right? Let me break it down.

Starting with 'strawberry', let's write out the letters one by one. S, T, R, A, W, B, E, R, R, Y. Hmm, wait, that's 10 letters. Let me check again. S (1), T (2), R (3), A (4), W (5), B (6), E (7), R (8), R (9), Y (10). So the letters are S-T-R-A-W-B-E-R-R-Y.
...
Therefore, the answer should be three R's in 'strawberry'. But I need to make sure I'm not counting any other letters as R. Let me check again. S, T, R, A, W, B, E, R, R, Y. No other R's. So three in total. Yeah, that seems right.

----------------------------------------------------------------------------------------------------
Answer: The word "strawberry" contains **three** letters 'r'. Here's the breakdown:

1. **S-T-R-A-W-B-E-R-R-Y**
   - The **third letter** is 'R'.
   - The **eighth and ninth letters** are also 'R's.

Thus, the total count is **3**.

**Answer:** 3.
```
<Note>
Setting `"enable_thinking": False` (or omitting it) will result in `reasoning_content` being `None`. Qwen3-Thinking models always generate reasoning content and don't support the `enable_thinking` parameter.
</Note>

#### Logit Bias Support

SGLang supports the `logit_bias` parameter for both chat completions and completions APIs. This parameter allows you to modify the likelihood of specific tokens being generated by adding bias values to their logits. The bias values can range from -100 to 100, where:

- **Positive values** (0 to 100) increase the likelihood of the token being selected
- **Negative values** (-100 to 0) decrease the likelihood of the token being selected
- **-100** effectively prevents the token from being generated

The `logit_bias` parameter accepts a dictionary where keys are token IDs (as strings) and values are the bias amounts (as floats).

#### Getting Token IDs

To use `logit_bias` effectively, you need to know the token IDs for the words you want to bias. Here's how to get token IDs:

```python Example
# Get tokenizer to find token IDs
import tiktoken

# For OpenAI models, use the appropriate encoding
tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo")  # or your model

# Get token IDs for specific words
word = "sunny"
token_ids = tokenizer.encode(word)
print(f"Token IDs for '{word}': {token_ids}")

# For SGLang models, you can access the tokenizer through the client
# and get token IDs for bias
```
<Tip>
**Important:** The `logit_bias` parameter uses token IDs as string keys, not the actual words.
</Tip>

#### Example: DeepSeek-V3 Models

DeepSeek-V3 models support thinking mode through the `thinking` parameter:

```python Example
# Launch server:
# python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.1 --tp 8  --reasoning-parser deepseek-v3

from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url=f"http://127.0.0.1:30000/v1",
)

model = "deepseek-ai/DeepSeek-V3.1"
messages = [{"role": "user", "content": "How many r's are in 'strawberry'?"}]

response = client.chat.completions.create(
    model=model,
    messages=messages,
    extra_body={
        "chat_template_kwargs": {"thinking": True},
        "separate_reasoning": True
    }
)

print("Reasoning:", response.choices[0].message.reasoning_content)
print("-"*100)
print("Answer:", response.choices[0].message.content)
```

**Example Output:**
```text Output
Reasoning: First, the question is: "How many r's are in 'strawberry'?"

I need to count the number of times the letter 'r' appears in the word "strawberry".

Let me write out the word: S-T-R-A-W-B-E-R-R-Y.

Now, I'll go through each letter and count the 'r's.
...
So, I have three 'r's in "strawberry".

I should double-check. The word is spelled S-T-R-A-W-B-E-R-R-Y. The letters are at positions: 3, 8, and 9 are 'r's. Yes, that's correct.

Therefore, the answer should be 3.
----------------------------------------------------------------------------------------------------
Answer: The word "strawberry" contains **3** instances of the letter "r". Here's a breakdown for clarity:

- The word is spelled: S-T-R-A-W-B-E-R-R-Y
- The "r" appears at the 3rd, 8th, and 9th positions.
```
<Note>
DeepSeek-V3 models use the `thinking` parameter (not `enable_thinking`) to control reasoning output.
</Note>

```python Example
# Example with logit_bias parameter
# Note: You need to get the actual token IDs from your tokenizer
# For demonstration, we'll use some example token IDs
response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {"role": "user", "content": "Complete this sentence: The weather today is"}
    ],
    temperature=0.7,
    max_tokens=20,
    logit_bias={
        "12345": 50,  # Increase likelihood of token ID 12345
        "67890": -50,  # Decrease likelihood of token ID 67890
        "11111": 25,  # Slightly increase likelihood of token ID 11111
    },
)

print_highlight(f"Response with logit bias: {response.choices[0].message.content}")
```

### Parameters

The chat completions API accepts OpenAI Chat Completions API's parameters. Refer to [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat/create) for more details.

SGLang extends the standard API with the `extra_body` parameter, allowing for additional customization. One key option within `extra_body` is `chat_template_kwargs`, which can be used to pass arguments to the chat template processor.

```python Example
response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are a knowledgeable historian who provides concise responses.",
        },
        {"role": "user", "content": "Tell me about ancient Rome"},
        {
            "role": "assistant",
            "content": "Ancient Rome was a civilization centered in Italy.",
        },
        {"role": "user", "content": "What were their major achievements?"},
    ],
    temperature=0.3,  # Lower temperature for more focused responses
    max_tokens=128,  # Reasonable length for a concise response
    top_p=0.95,  # Slightly higher for better fluency
    presence_penalty=0.2,  # Mild penalty to avoid repetition
    frequency_penalty=0.2,  # Mild penalty for more natural language
    n=1,  # Single response is usually more stable
    seed=42,  # Keep for reproducibility
)

print_highlight(response.choices[0].message.content)
```

Streaming mode is also supported.

#### Logit Bias Support

The completions API also supports the `logit_bias` parameter with the same functionality as described in the chat completions section above.

```python Example
stream = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[{"role": "user", "content": "Say this is a test"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")
```

#### Returning Routed Experts (MoE Models)

For MoE models, set `return_routed_experts: true` in `extra_body` to return expert routing data. Requires `--enable-return-routed-experts` server flag. The `routed_experts` field will be returned in the `sgl_ext` object on each choice, containing base64-encoded int32 expert IDs as a flattened array with logical shape `[num_tokens, num_layers, top_k]`. By default this returns `[0, seqlen - 1)`, the full available sequence, because RL workflows need routed experts for the full sequence. Set `routed_experts_start_len` in `extra_body` to an absolute prefix length to return only `[routed_experts_start_len, seqlen - 1)`. For example, in multi-turn RL rollouts, routed experts for tokens from previous turns have already been collected, so setting this value avoids unnecessary transfer that cause bottlenecks.

```python Example
# Example with logit_bias parameter for completions API
# Note: You need to get the actual token IDs from your tokenizer
# For demonstration, we'll use some example token IDs
response = client.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    prompt="The best programming language for AI is",
    temperature=0.7,
    max_tokens=20,
    logit_bias={
        "12345": 75,  # Strongly favor token ID 12345
        "67890": -100,  # Completely avoid token ID 67890
        "11111": -25,  # Slightly discourage token ID 11111
    },
)

print_highlight(f"Response with logit bias: {response.choices[0].text}")
```

## Completions

### Usage
Completions API is similar to Chat Completions API, but without the `messages` parameter or chat templates.

```python Example
response = client.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    prompt="List 3 countries and their capitals.",
    temperature=0,
    max_tokens=64,
    n=1,
    stop=None,
)

print_highlight(f"Response: {response}")
```

### Parameters

The completions API accepts OpenAI Completions API's parameters.  Refer to [OpenAI Completions API](https://platform.openai.com/docs/api-reference/completions/create) for more details.

Here is an example of a detailed completions request:

```python Example
response = client.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    prompt="Write a short story about a space explorer.",
    temperature=0.7,  # Moderate temperature for creative writing
    max_tokens=150,  # Longer response for a story
    top_p=0.9,  # Balanced diversity in word choice
    stop=["\n\n", "THE END"],  # Multiple stop sequences
    presence_penalty=0.3,  # Encourage novel elements
    frequency_penalty=0.3,  # Reduce repetitive phrases
    n=1,  # Generate one completion
    seed=123,  # For reproducible results
)

print_highlight(f"Response: {response}")
```

#### Returning Routed Experts (MoE Models)

For MoE models, set `return_routed_experts: true` in `extra_body` to return expert routing data. Requires `--enable-return-routed-experts` server flag. The `routed_experts` field will be returned in the `sgl_ext` object on each choice, containing base64-encoded int32 expert IDs as a flattened array with logical shape `[num_tokens, num_layers, top_k]`. By default this returns `[0, seqlen - 1)`, the full available sequence, because RL workflows need routed experts for the full sequence. Set `routed_experts_start_len` in `extra_body` to an absolute prefix length to return only `[routed_experts_start_len, seqlen - 1)`. For example, in multi-turn RL rollouts, routed experts for tokens from previous turns have already been collected, so setting this value avoids unnecessary transfer that cause bottlenecks.

## Structured Outputs (JSON, Regex, EBNF)

For OpenAI compatible structured outputs API, refer to [Structured Outputs](../advanced_features/structured_outputs) for more details.

## Using LoRA Adapters

SGLang supports LoRA (Low-Rank Adaptation) adapters with OpenAI-compatible APIs. You can specify which adapter to use directly in the `model` parameter using the `base-model:adapter-name` syntax.

**Server Setup:**
```bash Command
python -m sglang.launch_server \
    --model-path qwen/qwen2.5-0.5b-instruct \
    --enable-lora \
    --lora-paths adapter_a=/path/to/adapter_a adapter_b=/path/to/adapter_b
```

For more details on LoRA serving configuration, see the [LoRA documentation](../advanced_features/lora).

**API Call:**

(Recommended) Use the `model:adapter` syntax to specify which adapter to use:
```python Example
response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct:adapter_a",  # ← base-model:adapter-name
    messages=[{"role": "user", "content": "Convert to SQL: show all users"}],
    max_tokens=50,
)
```

**Backward Compatible: Using `extra_body`**

The old `extra_body` method is still supported for backward compatibility:
```python Example
# Backward compatible method
response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[{"role": "user", "content": "Convert to SQL: show all users"}],
    extra_body={"lora_path": "adapter_a"},  # ← old method
    max_tokens=50,
)
```
**Note:** When both `model:adapter` and `extra_body["lora_path"]` are specified, the `model:adapter` syntax takes precedence.

```python Example
terminate_process(server_process)
```
