Last refreshed 2026-07-09. Next refresh: weekly.
Why use CodeLlama 70B Python on Replicate API?
Replicate API offers CodeLlama 70B Python with pay-as-you-go pricing at $0.65/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare CodeLlama 70B Python 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-70b-python",
input={"prompt": "Hello"}meta/codellama-70b-pythonRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# meta/codellama-70b-python format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"meta/codellama-70b-python",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "meta/codellama-70b-python", not the LLMReference slug "codellama-70b-python".
- 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 70B Python Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.90 | $0.90 |
| Fireworks AI | $0.90 | $0.90 |
| Microsoft Foundry | $3.78 | $11.34 |
| Replicate API | $0.65 | $2.75 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.65 |
| Output tokens | $2.75 |
Capabilities
About CodeLlama 70B Python
CodeLlama 70B Python is a specialized AI model by Meta, designed for Python code synthesis and understanding. With 70 billion parameters, it excels in code completion, infilling, and instruction following tasks. The model leverages an optimized transformer architecture and has been fine-tuned with up to 16,000 tokens, making it particularly effective for Python-centric development workflows. While it doesn't support long contexts of 100,000 tokens, it offers powerful capabilities for both commercial and research applications in Python programming environments. More details can be found in the research paper "Code Llama: Open Foundation Models for Code" .