Last refreshed 2026-04-19. Next refresh: weekly.
Why use Nous Capybara 7B V1.9 on Fireworks AI?
Fireworks AI offers Nous Capybara 7B V1.9 with pay-as-you-go pricing at $0.20/1M input tokens. Fireworks AI offers a generative AI platform as a service, focusing on rapid product iteration and cost-efficient AI deployment.
Compare Nous Capybara 7B V1.9 across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport FIREWORKS_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],accounts/fireworks/models/nous-capybara-7b-v1p9Request example
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="accounts/fireworks/models/nous-capybara-7b-v1p9",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/nous-capybara-7b-v1p9", not the LLMReference slug "nous-capybara-7b-v1.9".
- Fireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
- The examples expect FIREWORKS_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.