Last refreshed 2026-07-09. Next refresh: weekly.
Why use CodeLlama 13B on Replicate API?
Replicate API offers CodeLlama 13B with pay-as-you-go pricing at $0.10/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare CodeLlama 13B across 4 providers to find the best fit for your use caseSetup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"meta/codellama-13b",
input={"prompt": "Hello"}meta/codellama-13bRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# meta/codellama-13b format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"meta/codellama-13b",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "meta/codellama-13b", not the LLMReference slug "codellama-13b".
- Replicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
- The examples expect REPLICATE_API_TOKEN; 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 | $0.10 |
| Output tokens | $0.50 |
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.