Last refreshed 2026-07-09. Next refresh: weekly.
Why use CodeLlama 13B on Fireworks AI?
Fireworks AI offers CodeLlama 13B 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 CodeLlama 13B across 4 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/code-llama-13bRequest 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-13b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/code-llama-13b", not the LLMReference slug "codellama-13b".
- 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 CodeLlama 13B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.30 | $0.30 |
| Fireworks AI | $0.20 | $0.20 |
| Microsoft Foundry | $0.81 | $0.94 |
| Replicate API | $0.10 | $0.50 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About CodeLlama 13B
CodeLlama 13B is a state-of-the-art generative text model developed by Meta, specifically designed for code synthesis and understanding tasks. Released on August 24, 2023, this 13-billion-parameter model excels in general code generation and comprehension, making it suitable for a wide range of programming tasks, including code completion, infilling, and instruction following. It utilizes an optimized transformer architecture and has been trained on a diverse dataset similar to Llama 2, ensuring robust understanding of programming languages and coding practices.