Last refreshed 2026-06-15. Next refresh: weekly.
Why use DBRX Instruct on DeepInfra?
DeepInfra offers DBRX Instruct with pay-as-you-go pricing at $0.60/1M input tokens. DeepInfra is a cloud inference platform offering cost-effective access to open-source AI models.
Compare DBRX Instruct across 6 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport DEEPINFRA_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["DEEPINFRA_API_KEY"],dbrx-instructRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["DEEPINFRA_API_KEY"],
base_url="https://api.deepinfra.com/v1/openai"
)
response = client.chat.completions.create(
model="dbrx-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- DeepInfra uses "organization/model-name" format, e.g. "meta-llama/Meta-Llama-3-8B-Instruct" or "mistralai/Mistral-7B-Instruct-v0.3". See the DeepInfra model catalog for exact IDs.
- The examples expect DEEPINFRA_API_KEY; rename it only if your application config maps the new variable.
Compare DBRX Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Microsoft Foundry | $2.70 | $2.70 |
| Databricks Foundation Model Serving | $0.75 | $2.25 |
| Together AI | $1.20 | $1.20 |
| NVIDIA NIM | — | — |
| DeepInfra | $0.60 | $1.20 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.60 |
| 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.