Last refreshed 2026-06-15. Next refresh: weekly.
Why use DBRX Instruct on Together AI?
Together AI offers DBRX Instruct with pay-as-you-go pricing at $1.20/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare DBRX Instruct across 6 providers to find the best fit for your use caseSetup recipe
Python + curlpip install togetherexport TOGETHER_API_KEY=...from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="dbrx-instruct",dbrx-instructRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="dbrx-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_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 | $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.