Comparisons

Deepseek V3.1 NVFP4 vs Meta Llama 3.1 405B Instruct

On provider list prices, Deepseek V3.1 NVFP4 costs $0.60 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 5.8x apart. Output is $1.70 against $3.50 (2.1x). On Allocate both bill at list plus the 7% transaction fee.

Deepseek V3.1 NVFP4 Meta Llama 3.1 405B Instruct
LabDeepSeekMeta
AccessOpen weightsOpen weights
Context window128K tokens4K tokens
List price, input$0.6 / M tokens$3.5 / M tokens
List price, output$1.7 / M tokens$3.5 / M tokens
Cached inputn/an/a
LicenseMITLlama community
Fine-tunableYesYes

Specifications 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 $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $4,110, or 4.1x.

Deepseek V3.1 NVFP4 reads 128K tokens per request against 4K for Meta Llama 3.1 405B Instruct, 32.0x the window. That decides which one can take whole documents without splitting them.

Deepseek V3.1 NVFP4$0.60$1.70
Meta Llama 3.1 405B Instruct$3.50$3.50
InputOutput

Choose Deepseek V3.1 NVFP4 for

  • The lower list price ($0.60 in / $1.70 out per M tokens)
  • The longer context window (128K vs 4K tokens)
  • Fine-tuning under a permissive license (MIT)
Deepseek V3.1 NVFP4 details

Choose Meta Llama 3.1 405B Instruct for

  • Training toward a model you own
Meta Llama 3.1 405B Instruct details

Common questions

Which is cheaper, Deepseek V3.1 NVFP4 or Meta Llama 3.1 405B Instruct?

Deepseek V3.1 NVFP4, on this workload shape. At list prices it is $0.60/$1.70 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $0.64/$1.82 against $3.75/$3.75, list plus 7%.

Which has the bigger context window?

Deepseek V3.1 NVFP4: 131,072 tokens (128K) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

Can I fine-tune Deepseek V3.1 NVFP4 or Meta Llama 3.1 405B Instruct?

Both publish open weights (Deepseek V3.1 NVFP4: MIT; Meta Llama 3.1 405B Instruct: 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.