Last refreshed 2026-09-18. Next refresh: weekly.
Why use CodeLlama 34B Instruct on Fireworks AI?
Fireworks AI offers CodeLlama 34B Instruct with pay-as-you-go pricing at $0.90/1M input tokens. Fireworks AI offers a generative AI platform as a service, focusing on rapid product iteration and cost-efficient AI deployment.
Setup 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/code-llama-34b-instructRequest 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/code-llama-34b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/code-llama-34b-instruct", not the LLMReference slug "codellama-34b-instruct".
- 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.
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.90 |
| Output tokens | $0.90 |
Capabilities
No model capability flags are currently sourced.
About CodeLlama 34B Instruct
CodeLlama 34B Instruct is a powerful generative text model developed by Meta, specifically designed for code synthesis and understanding tasks. With 34 billion parameters, it excels in code completion, infilling, and instruction following, making it ideal for AI engineers working on coding assistants and automated code generation tools. While supporting multiple languages, it has a particular focus on Python. Trained between January and July 2023 on a dataset similar to Llama 2, this model offers state-of-the-art performance for commercial and research applications in code-related tasks. For more details, visit the model's page on Hugging Face .