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-2-13b-Chat-Awq hf/thebloke/llama-2-13b-chat-awq | Llama-2-7b-Chat-Int8 meta/llama-2-7b-chat-int8 | |
|---|---|---|
| Provider | Hugging Face | Meta |
| Family | Llama 2 | Llama 2 |
| Modality | text | text |
| Context window | 4,096 tok | 8,192 tok |
| Max output | 4,096 tok | 4,096 tok |
| Released | 2023-07-18 | 2023-09-25 |
| Input price | $0.070 /1M | $0.040 /1M |
| Output price | $0.140 /1M | $0.080 /1M |
| Cache read | — | — |
| Tools | — | — |
| Streaming | yes | yes |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
Llama 2 13B Chat AWQ is an efficient, accurate and blazing-fast low-bit weight quantized Llama 2 variant.
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-2-13b-Chat-Awq
client.chat.completions.create(
model="hf/thebloke/llama-2-13b-chat-awq",
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"}],
)