WizardCoder Python 34B on Replicate API

WizardCoder · WizardLM Team

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Last refreshed 2026-06-29. Next refresh: weekly.

Why use WizardCoder Python 34B on Replicate API?

Replicate API offers WizardCoder Python 34B with pay-as-you-go pricing at $0.20/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.

Compare WizardCoder Python 34B across 2 providers to find the best fit for your use case
Input / 1M
$0.20
Output / 1M
$1.00
Cache
Not sourced
Batch
Not sourced

Setup recipe

Python + curl
Install
pip install replicate
Auth
export REPLICATE_API_TOKEN=...
Call
import replicate
output = replicate.run(
    "lucataco/wizardcoder-python-34b-v1.0",
    input={"prompt": "Hello"}
Model ID
lucataco/wizardcoder-python-34b-v1.0

Request example

import replicate

# reads REPLICATE_API_TOKEN from env
# lucataco/wizardcoder-python-34b-v1.0 format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
    "lucataco/wizardcoder-python-34b-v1.0",
    input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))

Gotchas

  • Use provider model ID "lucataco/wizardcoder-python-34b-v1.0", not the LLMReference slug "wizardcoder-python-34b".
  • 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 WizardCoder Python 34B Across Providers

ProviderInput (per 1M)Output (per 1M)
Together AI$0.80$0.80
Replicate API$0.20$1.00

Pricing

TypePrice (per 1M)
Input tokens$0.20
Output tokens$1.00

Capabilities

Structured Outputs

About WizardCoder Python 34B

WizardCoder Python 34B is a large language model (LLM) tailored for code generation and comprehension, primarily focusing on Python. Harnessing a Transformer-based structure with 34 billion parameters, it was refined using the Evol-Instruct method to enhance its instruction-following skills. This model excels in generating accurate and context-aware code, offering functionalities like code generation, completion, summarization, and translation across languages. It has achieved notable performance in benchmarks such as HumanEval, even outperforming certain versions of GPT-4 in specific tests.

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Model Specs

Released2024-01-29
Parameters34B
Context100k
ArchitectureDecoder Only

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