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DeepSeek R1 0528 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 $3 for DeepSeek R1 0528 NVFP4: 2.9x apart. Output is $1.04 against $7 (6.7x). On Allocate both bill at list plus the 7% transaction fee.

DeepSeek R1 0528 NVFP4 Meta Llama 3.3 70B Instruct Turbo
LabDeepseekMeta
AccessOpen weightsOpen weights
Context window160K tokens128K tokens
List price, input$3 / M tokens$1.04 / M tokens
List price, output$7 / M tokens$1.04 / 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 $1,612 a month on Meta Llama 3.3 70B Instruct Turbo and $6,050 on DeepSeek R1 0528 NVFP4 at list: a gap of $4,438, or 3.8x.

DeepSeek R1 0528 NVFP4 reads 160K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 1.3x the window. That decides which one can take whole documents without splitting them.

Meta Llama 3.3 70B Instruct Turbo$1.04$1.04
DeepSeek R1 0528 NVFP4$3$7
InputOutput

Choose DeepSeek R1 0528 NVFP4 for

  • The longer context window (160K vs 128K tokens)
  • Fine-tuning under a permissive license (MIT)
DeepSeek R1 0528 NVFP4 details

Choose Meta Llama 3.3 70B Instruct Turbo for

  • The lower list price ($1.04 in / $1.04 out per M tokens)
Meta Llama 3.3 70B Instruct Turbo details

Common questions

Which is cheaper, DeepSeek R1 0528 NVFP4 or Meta Llama 3.3 70B Instruct Turbo?

Meta Llama 3.3 70B Instruct Turbo, on this workload shape. At list prices it is $1.04/$1.04 per million tokens in and out against $3/$7 for DeepSeek R1 0528 NVFP4. Billed on Allocate: $1.11/$1.11 against $3.21/$7.49, list plus 7%.

Which has the bigger context window?

DeepSeek R1 0528 NVFP4: 163,840 tokens (160K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

Can I fine-tune DeepSeek R1 0528 NVFP4 or Meta Llama 3.3 70B Instruct Turbo?

Both publish open weights (DeepSeek R1 0528 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

DeepSeek R1 0528 NVFP4 vs Deepseek V4 ProMeta Llama 3.3 70B Instruct Turbo vs Deepseek V4 ProDeepSeek R1 0528 NVFP4 vs Meta Llama 3.1 405B InstructMeta Llama 3.3 70B Instruct Turbo vs Meta Llama 3.1 405B InstructDeepSeek R1 0528 NVFP4 vs Llama 4 Scout Instruct (17Bx16E)Meta Llama 3.3 70B Instruct Turbo vs Llama 4 Scout Instruct (17Bx16E)

Run the numbers on your workload

LLM price comparisonAgent cost calculatorDeepSeek R1 0528 NVFP4Meta Llama 3.3 70B Instruct Turbo

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.

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