Last refreshed 2026-09-30. Next refresh: weekly.
Why use Zephyr 7B Beta on Replicate API?
Replicate API offers Zephyr 7B Beta 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 Beta 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(
"nateraw/zephyr-7b-beta",
input={"prompt": "Hello"}nateraw/zephyr-7b-betaRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# nateraw/zephyr-7b-beta format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"nateraw/zephyr-7b-beta",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "nateraw/zephyr-7b-beta", not the LLMReference slug "zephyr-7b-beta".
- 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 Beta Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Fireworks AI | $0.20 | $0.20 |
| 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 Beta
Zephyr 7B Beta is a 7-billion parameter large language model, fine-tuned from the Mistral-7B-v0.1 model. It is tailored to serve as an effective virtual assistant, performing well in generating human-like responses. The model's training involved Direct Preference Optimization (DPO) on a combination of publicly available and synthetic datasets, achieving strong performance on benchmarks like MT-Bench and AlpacaEval, especially for conversational tasks. However, its complexity falls short when compared to proprietary models, especially in tasks involving coding and mathematics. A notable limitation is its insufficient alignment to human safety preferences and the absence of in-the-loop filtering to prevent problematic outputs.