CodeLlama 13B on Together AI

Code Llama · AI at Meta

ServerlessOpen Weights

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

Why use CodeLlama 13B on Together AI?

Together AI offers CodeLlama 13B with pay-as-you-go pricing at $0.30/1M 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 CodeLlama 13B across 4 providers to find the best fit for your use case
Input / 1M
$0.30
Output / 1M
$0.30
Cache
Not sourced
Batch
Not sourced

Setup recipe

Python + curl
Install
pip install together
Auth
export TOGETHER_API_KEY=...
Call
from together import Together
client = Together()  # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
    model="codellama-13b",
Model ID
codellama-13b

Request example

from together import Together

client = Together()  # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
    model="codellama-13b",
    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 CodeLlama 13B Across Providers

ProviderInput (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

TypePrice (per 1M)
Input tokens$0.30
Output tokens$0.30

Capabilities

Structured Outputs

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.

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