Side-by-side comparison
Llama 3.3 Nemotron Super 49B V1.5 vs Qwen3 30B A3B Thinking 2507
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.4000
Qwen
Qwen3 30B A3B Thinking 2507
🔧 Tools
{} JSON
Input / 1M
$0.0800
Output / 1M
$0.4000
| Llama 3.3 Nemotron Super 49B V1.5 | Qwen3 30B A3B Thinking 2507 | |
|---|---|---|
| Provider | NVIDIA | Qwen |
| 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 | 0.0800 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.4000 | 0.4000 |
Frequently asked questions
Which is cheaper, Llama 3.3 Nemotron Super 49B V1.5 or Qwen3 30B A3B Thinking 2507?
Qwen3 30B A3B Thinking 2507 is cheaper than Llama 3.3 Nemotron Super 49B V1.5 on a 50/50 input/output blend by about $0.01 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 Qwen3 30B A3B Thinking 2507?
Llama 3.3 Nemotron Super 49B V1.5 comes from NVIDIA; Qwen3 30B A3B Thinking 2507 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.