Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 2 7B Chat on Together AI?
Together AI offers Llama 2 7B Chat 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 Llama 2 7B Chat across 10 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="llama2-7b-chat",llama2-7b-chatRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="llama2-7b-chat",
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 Llama 2 7B Chat Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Alibaba Cloud PAI-EAS | — | — |
| Baseten API | — | — |
| Fireworks AI | $0.20 | $0.20 |
| Microsoft Foundry | $0.52 | $0.67 |
| GCP Vertex AI | $0.08 | $0.24 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About Llama 2 7B Chat
The Llama 2 7B Chat model is a fine-tuned variant of Meta's Llama 2 series, optimized for conversational AI applications. Built on an auto-regressive transformer architecture, it boasts 7 billion parameters and has been trained on a diverse dataset of 2 trillion tokens. The model underwent supervised fine-tuning and reinforcement learning with human feedback to enhance its performance in dialogue scenarios. It demonstrates competitive capabilities in terms of helpfulness and safety compared to both open-source and closed-source alternatives like ChatGPT and PaLM.