AllocateDeepseek V3.1 NVFP4 vs OpenAI GPT-OSS 120B
On provider list prices, OpenAI GPT-OSS 120B costs $0.15 per million input tokens against $0.60 for Deepseek V3.1 NVFP4: 4.0x apart. Output is $0.60 against $1.70 (2.8x). On Allocate both bill at list plus the 7% transaction fee.
Deepseek V3.1 NVFP4
OpenAI GPT-OSS 120BSpecifications 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 $390 a month on OpenAI GPT-OSS 120B and $1,315 on Deepseek V3.1 NVFP4 at list: a gap of $925, or 3.4x.
Choose Deepseek V3.1 NVFP4 for
- Fine-tuning under a permissive license (MIT)
Choose OpenAI GPT-OSS 120B for
- The lower list price ($0.15 in / $0.60 out per M tokens)
Common questions
Which is cheaper, Deepseek V3.1 NVFP4 or OpenAI GPT-OSS 120B?
OpenAI GPT-OSS 120B, on this workload shape. At list prices it is $0.15/$0.60 per million tokens in and out against $0.60/$1.70 for Deepseek V3.1 NVFP4. Billed on Allocate: $0.16/$0.64 against $0.64/$1.82, list plus 7%.
Which has the bigger context window?
They match: both read 131,072 tokens (128K) per request.
Can I fine-tune Deepseek V3.1 NVFP4 or OpenAI GPT-OSS 120B?
Both publish open weights (Deepseek V3.1 NVFP4: MIT; OpenAI GPT-OSS 120B: 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.