# 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.

## Specifications

| | Deepseek V3.1 NVFP4 | Meta Llama 3.1 405B Instruct |
| --- | --- | --- |
| Lab | DeepSeek | Meta |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 4K tokens |
| List price, input | $0.60 / M tokens | $3.50 / M tokens |
| List price, output | $1.70 / M tokens | $3.50 / 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 $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.

## 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)

## Choose Meta Llama 3.1 405B Instruct for

- Training toward a model you own

## 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.

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[HTML page](https://allocate.network/compare/deepseek-deepseek-v3-1-vs-meta-llama-3-1-405b-instruct) · [Deepseek V3.1 NVFP4](https://allocate.network/models/deepseek-deepseek-v3-1.md) · [Meta Llama 3.1 405B Instruct](https://allocate.network/models/meta-llama-3-1-405b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)