CodeLlama 7B on Fireworks AI

Code Llama · AI at Meta

ProvisionedOpen Weights

Last refreshed 2026-07-09. Next refresh: weekly.

Why use CodeLlama 7B on Fireworks AI?

Fireworks AI offers CodeLlama 7B 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 7B across 5 providers to find the best fit for your use case
Input / 1M
$0.20
Output / 1M
$0.20
Cache
Not sourced
Batch
Not sourced

Setup recipe

Python + curl
Install
pip install openai
Auth
export FIREWORKS_API_KEY=...
Call
import os
from openai import OpenAI
client = OpenAI(
    api_key=os.environ["FIREWORKS_API_KEY"],
Model ID
accounts/fireworks/models/code-llama-7b

Request 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-7b",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Gotchas

  • Use provider model ID "accounts/fireworks/models/code-llama-7b", not the LLMReference slug "codellama-7b".
  • 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 7B Across Providers

ProviderInput (per 1M)Output (per 1M)
Baseten API——
Together AI$0.20$0.20
Fireworks AI$0.20$0.20
Microsoft Foundry$0.52$0.67
Replicate API$0.05$0.25

Pricing

TypePrice (per 1M)
Input tokens$0.20
Output tokens$0.20

Capabilities

Structured Outputs

About CodeLlama 7B

CodeLlama 7B is a specialized code generation model released by Meta on August 24, 2023. With 7 billion parameters, it excels in code completion, infilling, and instruction-following tasks. Built on an optimized transformer architecture, it's designed for both commercial and research applications, particularly in English and various programming languages. The model offers robust capabilities for AI engineers looking to enhance coding workflows or develop code generation applications. More details can be found on the official Hugging Face page .

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