Last refreshed 2026-06-15. Next refresh: weekly.
Why use Nous Capybara 7B V1.9 on Together AI?
Together AI offers Nous Capybara 7B V1.9 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 Nous Capybara 7B V1.9 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="nous-capybara-7b-v1.9",nous-capybara-7b-v1.9Request example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="nous-capybara-7b-v1.9",
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 Nous Capybara 7B V1.9 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Fireworks AI | $0.20 | $0.20 |
| Together AI | $0.20 | $0.20 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About Nous Capybara 7B V1.9
The Nous Capybara 7B V1.9 is a cutting-edge large language model created by NousResearch, featuring 7 billion parameters. It's an advancement in the Capybara series, utilizing the novel Amplify-Instruct data synthesis technique to create a focused training dataset comprising 20,000 curated conversational examples, most of which include new, innovative tokens. Built on the Mistral architecture, it excels in multi-turn conversations, adeptly summarizing complex topics and recalling information up to late 2022. Despite its innovative framework, it faces challenges due to a smaller dataset, which may limit its generalization and introduce uncertainties related to scalability and biases.