Side-by-side comparison
Qwen3 30B A3B Thinking 2507 vs Llama 3.3 Nemotron Super 49B V1.5
Qwen
Qwen3 30B A3B Thinking 2507
🔧 Tools
{} JSON
Input / 1M
$0.0800
Output / 1M
$0.4000
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.4000
| Qwen3 30B A3B Thinking 2507 | Llama 3.3 Nemotron Super 49B V1.5 | |
|---|---|---|
| Provider | Qwen | 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 → | 0.0800 | 0.1000 |
| 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, Qwen3 30B A3B Thinking 2507 or Llama 3.3 Nemotron Super 49B V1.5?
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 Qwen3 30B A3B Thinking 2507 and Llama 3.3 Nemotron Super 49B V1.5?
Qwen3 30B A3B Thinking 2507 comes from Qwen; 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.