AllocateDeepSeek R1 Distill Qwen 1.5B vs Nvidia Nemotron Nano 9B V2
On provider list prices, Nvidia Nemotron Nano 9B V2 costs $0.06 per million input tokens against $0.18 for DeepSeek R1 Distill Qwen 1.5B: 3.0x apart. Output is $0.25 against $0.18. On Allocate both bill at list plus the 7% transaction fee.
DeepSeek R1 Distill Qwen 1.5B
Nvidia Nemotron Nano 9B V2Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.
What the numbers say
Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $159.50 a month on Nvidia Nemotron Nano 9B V2 and $279 on DeepSeek R1 Distill Qwen 1.5B at list: a gap of $119.50, or 1.7x.
Choose DeepSeek R1 Distill Qwen 1.5B for
- Fine-tuning under a permissive license (MIT)
Choose Nvidia Nemotron Nano 9B V2 for
- The lower list price ($0.06 in / $0.25 out per M tokens)
Common questions
Which is cheaper, DeepSeek R1 Distill Qwen 1.5B or Nvidia Nemotron Nano 9B V2?
Nvidia Nemotron Nano 9B V2, on this workload shape. At list prices it is $0.06/$0.25 per million tokens in and out against $0.18/$0.18 for DeepSeek R1 Distill Qwen 1.5B. Billed on Allocate: $0.064/$0.27 against $0.19/$0.19, list plus 7%.
Which has the bigger context window?
They match: both read 131,072 tokens (128K) per request.
Can I fine-tune DeepSeek R1 Distill Qwen 1.5B or Nvidia Nemotron Nano 9B V2?
Both publish open weights (DeepSeek R1 Distill Qwen 1.5B: MIT; Nvidia Nemotron Nano 9B V2: Custom license), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.
Related comparisons
Run the numbers on your workload
Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.