Side-by-side comparison
Llama 3.3 Nemotron Super 49B V1.5 vs Qwen3 30B A3B
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 | Qwen3 30B A3B | |
|---|---|---|
| Provider | NVIDIA | Qwen |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 40,960 |
| 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 | 0.0900 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.4000 | 0.4500 |
Frequently asked questions
Which is cheaper, Llama 3.3 Nemotron Super 49B V1.5 or Qwen3 30B A3B?
Llama 3.3 Nemotron Super 49B V1.5 is cheaper than Qwen3 30B A3B on a 50/50 input/output blend by about $0.02 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 3.3 Nemotron Super 49B V1.5 or Qwen3 30B A3B?
Llama 3.3 Nemotron Super 49B V1.5 has the larger context window at 131k tokens versus 41k tokens for Qwen3 30B A3B. That means Llama 3.3 Nemotron Super 49B V1.5 can ingest about 3.2x as much text per request.
What is the difference between Llama 3.3 Nemotron Super 49B V1.5 and Qwen3 30B A3B?
Llama 3.3 Nemotron Super 49B V1.5 comes from NVIDIA; Qwen3 30B A3B comes from Qwen. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.