> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Completions API
> Use the completions API for raw text generation with custom prompt templates
The completions API provides raw text generation without automatic message formatting. Use this when you need full control over prompt formatting or when working with base models.
## When to use completions
**Use the completions API for:**
* Custom prompt templates with specific formatting requirements
* Base models (non-instruct/non-chat variants)
* Fine-grained control over token-level formatting
* Legacy applications that depend on raw completion format
**For most use cases, use [chat completions](/guides/querying-text-models) instead.** Chat completions handles message formatting automatically and works better with instruct-tuned models.
## Basic usage
```python theme={null}
from fireworks import Fireworks
client = Fireworks()
response = client.completions.create(
model="accounts/fireworks/models/deepseek-v3p1",
prompt="Once upon a time"
)
print(response.choices[0].text)
```
```python theme={null}
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("FIREWORKS_API_KEY"),
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.completions.create(
model="accounts/fireworks/models/deepseek-v3p1",
prompt="Once upon a time"
)
print(response.choices[0].text)
```
```javascript theme={null}
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.FIREWORKS_API_KEY,
baseURL: "https://api.fireworks.ai/inference/v1",
});
const response = await client.completions.create({
model: "accounts/fireworks/models/deepseek-v3p1",
prompt: "Once upon a time",
});
console.log(response.choices[0].text);
```
```bash theme={null}
curl https://api.fireworks.ai/inference/v1/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $FIREWORKS_API_KEY" \
-d '{
"model": "accounts/fireworks/models/deepseek-v3p1",
"prompt": "Once upon a time"
}'
```
Most models automatically prepend the beginning-of-sequence (BOS) token (e.g., ``) to your prompt. Verify this with the `raw_output` parameter if needed.
## Custom prompt templates
The completions API is useful when you need to implement custom prompt formats:
```python theme={null}
# Custom few-shot prompt template
prompt = """Task: Classify the sentiment of the following text.
Text: I love this product!
Sentiment: Positive
Text: This is terrible.
Sentiment: Negative
Text: The weather is nice today.
Sentiment:"""
response = client.completions.create(
model="accounts/fireworks/models/deepseek-v3p1",
prompt=prompt,
max_tokens=10,
temperature=0
)
print(response.choices[0].text) # Output: " Positive"
```
## Common parameters
All [chat completions parameters](/guides/querying-text-models) work with completions:
* `temperature` - Control randomness (0-2)
* `max_tokens` - Limit output length
* `top_p`, `top_k`, `min_p` - Sampling parameters
* `stream` - Stream responses token-by-token
* `frequency_penalty`, `presence_penalty` - Reduce repetition
See the [API reference](/api-reference/post-completions) for complete parameter documentation.
## Querying deployments
Use completions with [on-demand deployments](/guides/ondemand-deployments) by specifying the deployment identifier:
```python theme={null}
response = client.completions.create(
model="accounts//deployments/",
prompt="Your prompt here"
)
```
## Next steps
Use chat completions for most use cases
Stream responses for real-time UX
Complete API documentation