Side-by-side comparison
Llama 3.3 Nemotron Super 49B V1.5 vs DeepSeek V3.2
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.4000
| Llama 3.3 Nemotron Super 49B V1.5 | DeepSeek V3.2 | |
|---|---|---|
| Provider | NVIDIA | DeepSeek |
| 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 | 0.2520 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.4000 | 0.3780 |
Frequently asked questions
Which is cheaper, Llama 3.3 Nemotron Super 49B V1.5 or DeepSeek V3.2?
Llama 3.3 Nemotron Super 49B V1.5 is cheaper than DeepSeek V3.2 on a 50/50 input/output blend by about $0.065 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 DeepSeek V3.2?
Llama 3.3 Nemotron Super 49B V1.5 comes from NVIDIA; DeepSeek V3.2 comes from DeepSeek. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.