# DeepSeek V4 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 $1.74 for DeepSeek V4: 1.7x apart. Output is $1.04 against $3.48 (3.3x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | DeepSeek V4 | Meta Llama 3.3 70B Instruct Turbo |
| --- | --- | --- |
| Lab | Deepseek | Meta |
| Access | API only | Open weights |
| Context window | 512K tokens | 128K tokens |
| List price, input | $1.74 / M tokens | $1.04 / M tokens |
| List price, output | $3.48 / M tokens | $1.04 / M tokens |
| Cached input | $0.20 / M tokens | n/a |
| License | Proprietary API | Llama community |
| Fine-tunable | No | 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 $1,612 a month on Meta Llama 3.3 70B Instruct Turbo and $3,306 on DeepSeek V4 at list: a gap of $1,694, or 2.1x.

DeepSeek V4 reads 512K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 3.9x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek V4 for

- Reasoning-heavy agents
- Long-document analysis
- Cost-sensitive production routes

## Choose Meta Llama 3.3 70B Instruct Turbo for

- The lower list price ($1.04 in / $1.04 out per M tokens)
- Open weights you can fine-tune and own

## Common questions

### Which is cheaper, DeepSeek V4 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 $1.74/$3.48 for DeepSeek V4. Billed on Allocate: $1.11/$1.11 against $1.86/$3.72, list plus 7%.

### Which has the bigger context window?

DeepSeek V4: 512,000 tokens (512K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

### Can I fine-tune DeepSeek V4 or Meta Llama 3.3 70B Instruct Turbo?

Meta Llama 3.3 70B Instruct Turbo publishes open weights (Llama community) and can be fine-tuned on your own data. DeepSeek V4 is a closed model served over API; its weights are not available.

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[HTML page](https://allocate.network/compare/deepseek-v4-vs-meta-llama-3-3-70b-instruct-turbo) · [DeepSeek V4](https://allocate.network/models/deepseek-v4.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)