# DeepSeek R1 0528 NVFP4 vs LFM2.5 8B A1B

LFM2.5 8B A1B 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 | LFM2.5 8B A1B |
| --- | --- | --- |
| Lab | Deepseek | Liquid AI |
| 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 2026-09-12.

## Choose DeepSeek R1 0528 NVFP4 for

- Fine-tuning under a permissive license (MIT)

## Choose LFM2.5 8B A1B for

- High-volume extraction
- Latency-sensitive routes
- Edge deployments

## Common questions

### Which has the bigger context window?

DeepSeek R1 0528 NVFP4: 163,840 tokens (160K) against an unlisted window for LFM2.5 8B A1B.

### Can I fine-tune DeepSeek R1 0528 NVFP4 or LFM2.5 8B A1B?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; LFM2.5 8B A1B: Not listed), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

---

[HTML page](https://allocate.network/compare/deepseek-deepseek-r1-0528-vs-liquid-lfm2-5-8b-a1b) · [DeepSeek R1 0528 NVFP4](https://allocate.network/models/deepseek-deepseek-r1-0528.md) · [LFM2.5 8B A1B](https://allocate.network/models/liquid-lfm2-5-8b-a1b.md) · [Machine-readable catalog](https://allocate.network/catalog.json)