Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama Guard 7B on Together AI?
Together AI offers Llama Guard 7B with pay-as-you-go pricing at $0.20/1M 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 Llama Guard 7B across 2 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="llama-guard-7b",llama-guard-7bRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="llama-guard-7b",
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 Llama Guard 7B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.20 | $0.20 |
| Fireworks AI | $0.20 | $0.20 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About Llama Guard 7B
Llama Guard 7B is a specialized content moderation model based on the Llama 2 architecture, designed to safeguard AI interactions. With 7 billion parameters, it excels in classifying and moderating both input prompts and output responses from large language models. The model employs a comprehensive risk taxonomy to identify various categories of harmful content, including violence, hate speech, and sexual content. Trained on diverse datasets, including prompts from the Anthropic dataset and in-house generated responses, Llama Guard 7B has demonstrated superior performance compared to industry-standard content moderation APIs.