Side-by-side comparison
DeepSeek V3.1 Nex N1 vs Llama 3.3 Nemotron Super 49B V1.5
Nex AGI
DeepSeek V3.1 Nex N1
🔧 Tools
{} JSON
Input / 1M
$0.1350
Output / 1M
$0.5000
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.4000
| DeepSeek V3.1 Nex N1 | Llama 3.3 Nemotron Super 49B V1.5 | |
|---|---|---|
| Provider | Nex AGI | 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 → | 0.1350 | 0.1000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.5000 | 0.4000 |
Frequently asked questions
Which is cheaper, DeepSeek V3.1 Nex N1 or Llama 3.3 Nemotron Super 49B V1.5?
Llama 3.3 Nemotron Super 49B V1.5 is cheaper than DeepSeek V3.1 Nex N1 on a 50/50 input/output blend by about $0.0675 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 DeepSeek V3.1 Nex N1 and Llama 3.3 Nemotron Super 49B V1.5?
DeepSeek V3.1 Nex N1 comes from Nex AGI; 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.