Last refreshed 2026-06-15. Next refresh: weekly.
Why use OLMo 7B on Together AI?
Together AI offers OLMo 7B with pay-as-you-go pricing at $0.20/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare OLMo 7B across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install togetherexport TOGETHER_API_KEY=...from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="olmo-7b",olmo-7bRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="olmo-7b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_API_KEY; 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 | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
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.