# Deepseek 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.

## Specifications

| | Deepseek V3.1 NVFP4 | Meta Llama 3.3 70B Instruct Turbo |
| --- | --- | --- |
| Lab | DeepSeek | Meta |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 128K tokens |
| List price, input | $0.60 / M tokens | $1.04 / M tokens |
| List price, output | $1.70 / M tokens | $1.04 / M tokens |
| Cached input | n/a | n/a |
| License | MIT | Llama community |
| Fine-tunable | Yes | Yes |

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 $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.

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[HTML page](https://allocate.network/compare/deepseek-deepseek-v3-1-vs-meta-llama-3-3-70b-instruct-turbo) · [Deepseek V3.1 NVFP4](https://allocate.network/models/deepseek-deepseek-v3-1.md) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Machine-readable catalog](https://allocate.network/catalog.json)