Side-by-side comparison
Qwen3 8B vs Llama 3.3 Nemotron Super 49B V1.5
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.4000
| Qwen3 8B | 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 → | 40,960 | 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 → | 0.0500 | 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 8B or Llama 3.3 Nemotron Super 49B V1.5?
Qwen3 8B is cheaper than Llama 3.3 Nemotron Super 49B V1.5 on a 50/50 input/output blend by about $0.025 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, Qwen3 8B or Llama 3.3 Nemotron Super 49B V1.5?
Llama 3.3 Nemotron Super 49B V1.5 has the larger context window at 131k tokens versus 41k tokens for Qwen3 8B. 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 Qwen3 8B and Llama 3.3 Nemotron Super 49B V1.5?
Qwen3 8B 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.