Last refreshed 2026-06-15. Next refresh: weekly.
Why use Llama 2 7B 32K on Together AI?
Together AI offers Llama 2 7B 32K 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="llama-2-7b-32k",llama-2-7b-32kRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="llama-2-7b-32k",
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 Llama 2 7B 32K
LLaMA-2-7B-32K is an open-source language model engineered by Together, derived from Meta's LLaMA-2 7B. It boasts a unique extended context length of up to 32,000 tokens, which enhances its ability to tackle tasks involving long-range context, such as multi-document question answering and lengthy text summarization. The model integrates optimizations, including FlashAttention-2, to boost inference and training efficiency. It combines pre-training with instruction tuning data for improved task performance and offers fine-tuning examples for specialized applications, like book summarization or multi-document Q&A.