Side-by-side comparison
Llama 3.2 1B Instruct vs ERNIE 4.5 21B A3B Thinking
Baidu Qianfan
ERNIE 4.5 21B A3B Thinking
Input / 1M
$0.0700
Output / 1M
$0.2800
| Llama 3.2 1B Instruct | ERNIE 4.5 21B A3B Thinking | |
|---|---|---|
| Provider | Meta | Baidu Qianfan |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 60,000 | 131,072 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | text-only | text-only |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.0270 | 0.0700 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.2000 | 0.2800 |
Frequently asked questions
Which is cheaper, Llama 3.2 1B Instruct or ERNIE 4.5 21B A3B Thinking?
Llama 3.2 1B Instruct is cheaper than ERNIE 4.5 21B A3B Thinking on a 50/50 input/output blend by about $0.0615 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, Llama 3.2 1B Instruct or ERNIE 4.5 21B A3B Thinking?
ERNIE 4.5 21B A3B Thinking has the larger context window at 131k tokens versus 60k tokens for Llama 3.2 1B Instruct. That means ERNIE 4.5 21B A3B Thinking can ingest about 2.2x as much text per request.
What is the difference between Llama 3.2 1B Instruct and ERNIE 4.5 21B A3B Thinking?
Llama 3.2 1B Instruct comes from Meta; ERNIE 4.5 21B A3B Thinking comes from Baidu Qianfan. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.