# DeepSeek R1 0528 NVFP4 vs GLM 4.7 FP8

On provider list prices, GLM 4.7 FP8 costs $0.45 per million input tokens against $3 for DeepSeek R1 0528 NVFP4: 6.7x apart. Output is $2 against $7 (3.5x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | DeepSeek R1 0528 NVFP4 | GLM 4.7 FP8 |
| --- | --- | --- |
| Lab | Deepseek | Zai Org |
| Access | Open weights | Open weights |
| Context window | 160K tokens | 198K tokens |
| List price, input | $3 / M tokens | $0.45 / M tokens |
| List price, output | $7 / M tokens | $2 / M tokens |
| Cached input | n/a | n/a |
| License | MIT | MIT |
| 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 $1,240 a month on GLM 4.7 FP8 and $6,050 on DeepSeek R1 0528 NVFP4 at list: a gap of $4,810, or 4.9x.

GLM 4.7 FP8 reads 198K tokens per request against 160K for DeepSeek R1 0528 NVFP4, 1.2x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek R1 0528 NVFP4 for

- Fine-tuning under a permissive license (MIT)

## Choose GLM 4.7 FP8 for

- The lower list price ($0.45 in / $2 out per M tokens)
- The longer context window (198K vs 160K tokens)
- Fine-tuning under a permissive license (MIT)

## Common questions

### Which is cheaper, DeepSeek R1 0528 NVFP4 or GLM 4.7 FP8?

GLM 4.7 FP8, on this workload shape. At list prices it is $0.45/$2 per million tokens in and out against $3/$7 for DeepSeek R1 0528 NVFP4. Billed on Allocate: $0.48/$2.14 against $3.21/$7.49, list plus 7%.

### Which has the bigger context window?

GLM 4.7 FP8: 202,752 tokens (198K) against 163,840 (160K) for DeepSeek R1 0528 NVFP4.

### Can I fine-tune DeepSeek R1 0528 NVFP4 or GLM 4.7 FP8?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; GLM 4.7 FP8: MIT), 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-z-ai-glm-4-7) · [DeepSeek R1 0528 NVFP4](https://allocate.network/models/deepseek-deepseek-r1-0528.md) · [GLM 4.7 FP8](https://allocate.network/models/z-ai-glm-4-7.md) · [Machine-readable catalog](https://allocate.network/catalog.json)