Side-by-side comparison
Llama 3.3 Nemotron Super 49B V1.5 vs Llama 3.3 70B Instruct
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.4000
Meta
Llama 3.3 70B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.3200
| Llama 3.3 Nemotron Super 49B V1.5 | Llama 3.3 70B Instruct | |
|---|---|---|
| Provider | NVIDIA | Meta |
| 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 → | 0.4000 | 0.3200 |
Frequently asked questions
Which is cheaper, Llama 3.3 Nemotron Super 49B V1.5 or Llama 3.3 70B Instruct?
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 Nemotron Super 49B V1.5 and Llama 3.3 70B Instruct?
Llama 3.3 Nemotron Super 49B V1.5 comes from NVIDIA; Llama 3.3 70B Instruct comes from Meta. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.