Side-by-side comparison
Qwen2.5 7B Instruct vs Nemotron Nano 9B V2
Qwen
Qwen2.5 7B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0400
Output / 1M
$0.1000
NVIDIA
Nemotron Nano 9B V2
🔧 Tools
{} JSON
Input / 1M
$0.0400
Output / 1M
$0.1600
| Qwen2.5 7B Instruct | Nemotron Nano 9B V2 | |
|---|---|---|
| Provider | Qwen | NVIDIA |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 32,768 | 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 → | 0.0400 | 0.0400 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.1000 | 0.1600 |
Frequently asked questions
Which is cheaper, Qwen2.5 7B Instruct or Nemotron Nano 9B V2?
Qwen2.5 7B Instruct is cheaper than Nemotron Nano 9B V2 on a 50/50 input/output blend by about $0.03 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, Qwen2.5 7B Instruct or Nemotron Nano 9B V2?
Nemotron Nano 9B V2 has the larger context window at 131k tokens versus 33k tokens for Qwen2.5 7B Instruct. That means Nemotron Nano 9B V2 can ingest about 4.0x as much text per request.
What is the difference between Qwen2.5 7B Instruct and Nemotron Nano 9B V2?
Qwen2.5 7B Instruct comes from Qwen; Nemotron Nano 9B V2 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.