OLMo 7B Twin-2T on Together AI
OLMo · Allen Institute for Artificial Intelligence (AI2)
Last refreshed 2026-06-15. Next refresh: weekly.
Why use OLMo 7B Twin-2T on Together AI?
Together AI offers OLMo 7B Twin-2T 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.
Setup 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-twin-2t",olmo-7b-twin-2tRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="olmo-7b-twin-2t",
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.
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About OLMo 7B Twin-2T
The OLMo 7B Twin-2T is a robust open-source large language model that implements a decoder-only transformer architecture with enhancements for greater stability and performance. It features non-parametric layer normalization and SwiGLU activation functions, along with Rotary positional embeddings for better sequence handling. The model, comprising 32 layers and 32 attention heads, was trained on approximately 2 trillion tokens and supports a context length of 2048. It is notable for its transparency in AI research, as all training data, code, and evaluations are publicly accessible, promoting collaborative advancements.