Pricing, context window, capabilities, and release date — pulled from each provider's public docs. Both are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.
Both models stream in parallel through your own AIgateway key. Tokens, latency, and cost update as they arrive.
| Llama-2-7b-Chat-HF-Lora meta-llama/llama-2-7b-chat-hf-lora | Llama-2-7b-Chat-Int8 meta/llama-2-7b-chat-int8 | |
|---|---|---|
| Provider | Meta | Meta |
| Family | Llama 2 | Llama 2 |
| Modality | text | text |
| Context window | 8,192 tok | 8,192 tok |
| Max output | 4,096 tok | 4,096 tok |
| Released | 2024-04-02 | 2023-09-25 |
| Input price | $0.040 /1M | $0.040 /1M |
| Output price | $0.080 /1M | $0.080 /1M |
| Cache read | — | — |
| Tools | — | — |
| Streaming | yes | yes |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
This is a Llama2 base model that Cloudflare dedicated for inference with LoRA adapters. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format.
Quantized (int8) generative text model with 7 billion parameters from Meta
# pip install aigateway-py openai
# aigateway-py: sub-accounts, evals, replays, jobs, webhook verify.
# openai SDK: chat/embeddings/images/audio — drop-in compat per our SDK's own guidance.
from openai import OpenAI
client = OpenAI(
base_url="https://api.aigateway.sh/v1",
api_key="sk-aig-...",
)
# Try Llama-2-7b-Chat-HF-Lora
client.chat.completions.create(
model="meta-llama/llama-2-7b-chat-hf-lora",
messages=[{"role":"user","content":"hello"}],
)
# Try Llama-2-7b-Chat-Int8 — same client, same key
client.chat.completions.create(
model="meta/llama-2-7b-chat-int8",
messages=[{"role":"user","content":"hello"}],
)