OLMo 7B on Replicate API

Name: OLMo 7B on Replicate API
Brand: Allen Institute for Artificial Intelligence (AI2)

OLMo · Allen Institute for Artificial Intelligence (AI2)

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Last refreshed 2026-04-19. Next refresh: weekly.

Why use OLMo 7B on Replicate API?

Replicate API offers OLMo 7B with competitive pricing. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.

Compare OLMo 7B across 2 providers to find the best fit for your use case

Input / 1M

Output / 1M

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(
    "olmo-7b",
    input={"prompt": "Hello"}

Model ID

olmo-7b

Request example

import replicate

# reads REPLICATE_API_TOKEN from env
# olmo-7b format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
    "olmo-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 OLMo 7B Across Providers

Provider	Input (per 1M)	Output (per 1M)
Together AI	$0.20	$0.20
Replicate API	—	—

Capabilities

Structured Outputs

About OLMo 7B

OLMo 7B is a large language model created by the Allen Institute for Artificial Intelligence (AI2), characterized by its open-source nature where model weights, training data, code, and evaluation tools have been publicly released. It utilizes a decoder-only transformer architecture, featuring 32 layers, a hidden size of 4096, and 32 attention heads, among other features. Trained on 2.5 trillion tokens from the Dolma dataset, this model excels in text generation, question answering, and language understanding, with performance metrics often comparable to or exceeding those of similar-sized models. It also boasts various architectural advancements such as SwiGLU activation functions and rotary positional embeddings. Despite its capabilities, users should be aware of its limitations concerning factual accuracy, bias, and context length.