# DeepSeek R1 0528 NVFP4 vs Llama 4 70B

Llama 4 70B is not currently in the Allocate serving catalog, so this page lists no prices for it: every price on this site comes from the live catalog.

## Specifications

| | DeepSeek R1 0528 NVFP4 | Llama 4 70B |
| --- | --- | --- |
| Lab | Deepseek | Meta |
| Access | Open weights | Not served on Allocate |
| Context window | 160K tokens | n/a |
| List price, input | $3 / M tokens | Not served |
| List price, output | $7 / M tokens | Not served |
| Cached input | n/a | n/a |
| License | MIT | Not listed |
| 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.

## Choose DeepSeek R1 0528 NVFP4 for

- Fine-tuning under a permissive license (MIT)

## Choose Llama 4 70B for

- First private fine-tunes
- Classification and extraction
- On-boundary deployments

## Common questions

### Which has the bigger context window?

DeepSeek R1 0528 NVFP4: 163,840 tokens (160K) against an unlisted window for Llama 4 70B.

### Can I fine-tune DeepSeek R1 0528 NVFP4 or Llama 4 70B?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; Llama 4 70B: Not listed), 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-r1-0528-vs-llama-4-70b) · [DeepSeek R1 0528 NVFP4](https://allocate.network/models/deepseek-deepseek-r1-0528.md) · [Llama 4 70B](https://allocate.network/models/llama-4-70b.md) · [Machine-readable catalog](https://allocate.network/catalog.json)