Using DBRX Instruct on Fireworks AI
Implementation guide · DBRX · Databricks Mosaic
Fireworks AI exposes DBRX Instruct through model ID dbrx-instruct. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-04-19. Next refresh: weekly.
Quick Start
- 1
- 2Use the Fireworks AI SDK or REST API to call
dbrx-instruct— see the documentation for request format. - 3
Code Examples
pip install openaiFIREWORKS_API_KEYdbrx-instructFireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.chat.completions.create(
model="dbrx-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on Fireworks AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.20 |
| Output tokens | $1.20 |
Capabilities
About DBRX Instruct
DBRX Instruct, developed by Databricks, is a cutting-edge large language model designed for various natural language processing tasks. It excels in text summarization, question answering, information extraction, and code generation, utilizing a fine-grained mixture-of-experts architecture with 132 billion parameters. With advanced features like rotary position encodings, gated linear units, and grouped query attention, it performs exceptionally across multiple benchmarks, even outperforming some closed-source models. Trained on a vast 12 trillion token dataset, it supports contexts up to 32,000 tokens. Although primarily effective in English, its multilingual strength isn't fully explored.