Last refreshed 2026-09-30. Next refresh: weekly.
Why use Zephyr 7B Alpha on Replicate API?
Replicate API offers Zephyr 7B Alpha with pay-as-you-go pricing at $0.05/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare Zephyr 7B Alpha across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"joehoover/zephyr-7b-alpha",
input={"prompt": "Hello"}joehoover/zephyr-7b-alphaRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# joehoover/zephyr-7b-alpha format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"joehoover/zephyr-7b-alpha",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "joehoover/zephyr-7b-alpha", not the LLMReference slug "zephyr-7b-alpha".
- 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 Zephyr 7B Alpha Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Baseten API | — | — |
| Replicate API | $0.05 | $0.25 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.05 |
| Output tokens | $0.25 |
Capabilities
No model capability flags are currently sourced.
About Zephyr 7B Alpha
The Zephyr 7B Alpha is a 7-billion parameter language model fine-tuned from the Mistral-7B-v0.1 framework. It serves as an AI assistant, primarily optimizing its performance using Direct Preference Optimization. Although it excels in English text generation and conversational tasks, its training with a mix of public and synthetic datasets—like UltraChat and UltraFeedback—brings a higher risk of generating problematic content due to lesser alignment with human safety standards compared to models like ChatGPT. The model's architecture is GPT-like, offering several quantized versions such as GPTQ and GGUF, which trade-off model size for performance, but may affect accuracy.