Side-by-side comparison
Llama 3.3 70B Instruct vs Llama 3.3 Nemotron Super 49B V1.5
Meta
Llama 3.3 70B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.3200
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.4000
| Llama 3.3 70B Instruct | Llama 3.3 Nemotron Super 49B V1.5 | |
|---|---|---|
| Provider | Meta | NVIDIA |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 131,072 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.1000 | 0.1000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.3200 | 0.4000 |
Frequently asked questions
Which is cheaper, Llama 3.3 70B Instruct or Llama 3.3 Nemotron Super 49B V1.5?
Llama 3.3 70B Instruct is cheaper than Llama 3.3 Nemotron Super 49B V1.5 on a 50/50 input/output blend by about $0.04 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
What is the difference between Llama 3.3 70B Instruct and Llama 3.3 Nemotron Super 49B V1.5?
Llama 3.3 70B Instruct comes from Meta; Llama 3.3 Nemotron Super 49B V1.5 comes from NVIDIA. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.