Last refreshed 2026-09-22. Next refresh: weekly.
Why use Stable LM 7B on Replicate API?
Replicate API offers Stable LM 7B 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 Stable LM 7B 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(
"stable-lm-7b",
input={"prompt": "Hello"}stable-lm-7bRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# stable-lm-7b format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"stable-lm-7b",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- 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 Stable LM 7B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Stability Developer Platform | — | — |
| 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 Stable LM 7B
StableLM 7B, a large language model by Stability AI, features a 7-billion parameter, decoder-only architecture designed to predict subsequent words based on context. Built on the robust NeoX transformer framework, it excels in managing long text sequences with its 4096-token context window. Pre-trained on a substantial dataset of about 1.5 trillion tokens, this model demonstrates strong capabilities in generating human-like text, performing tasks such as summarization and translation. However, it shares common limitations with other LLMs, including the potential for bias and generating inappropriate content.