AllocateDeepseek V3.1 NVFP4 vs Meta Llama 3.3 70B Instruct Turbo
On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $0.60 for Deepseek V3.1 NVFP4: effectively level. Output is $1.04 against $1.70 (1.6x). On Allocate both bill at list plus the 7% transaction fee.
Deepseek V3.1 NVFP4
Meta Llama 3.3 70B Instruct TurboSpecifications 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 $1,315 a month on Deepseek V3.1 NVFP4 and $1,612 on Meta Llama 3.3 70B Instruct Turbo at list: a gap of $297, or 1.2x.
Choose Deepseek V3.1 NVFP4 for
- The lower list price ($0.60 in / $1.70 out per M tokens)
- Fine-tuning under a permissive license (MIT)
Choose Meta Llama 3.3 70B Instruct Turbo for
- Training toward a model you own
Common questions
Which is cheaper, Deepseek V3.1 NVFP4 or Meta Llama 3.3 70B Instruct Turbo?
Deepseek V3.1 NVFP4, on this workload shape. At list prices it is $0.60/$1.70 per million tokens in and out against $1.04/$1.04 for Meta Llama 3.3 70B Instruct Turbo. Billed on Allocate: $0.64/$1.82 against $1.11/$1.11, 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 Meta Llama 3.3 70B Instruct Turbo?
Both publish open weights (Deepseek V3.1 NVFP4: MIT; Meta Llama 3.3 70B Instruct Turbo: Llama community), 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.