# Meta Llama 3.1 405B Instruct vs GLM 4.7 FP8

On provider list prices, GLM 4.7 FP8 costs $0.45 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 7.8x apart. Output is $2 against $3.50 (1.8x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.1 405B Instruct | GLM 4.7 FP8 |
| --- | --- | --- |
| Lab | Meta | Zai Org |
| Access | Open weights | Open weights |
| Context window | 4K tokens | 198K tokens |
| List price, input | $3.50 / M tokens | $0.45 / M tokens |
| List price, output | $3.50 / M tokens | $2 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | 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 $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $4,185, or 4.4x.

GLM 4.7 FP8 reads 198K tokens per request against 4K for Meta Llama 3.1 405B Instruct, 49.5x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.1 405B Instruct for

- Training toward a model you own

## Choose GLM 4.7 FP8 for

- The lower list price ($0.45 in / $2 out per M tokens)
- The longer context window (198K vs 4K tokens)
- Fine-tuning under a permissive license (MIT)

## Common questions

### Which is cheaper, Meta Llama 3.1 405B Instruct 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 $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $0.48/$2.14 against $3.75/$3.75, list plus 7%.

### Which has the bigger context window?

GLM 4.7 FP8: 202,752 tokens (198K) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

### Can I fine-tune Meta Llama 3.1 405B Instruct or GLM 4.7 FP8?

Both publish open weights (Meta Llama 3.1 405B Instruct: Llama community; GLM 4.7 FP8: MIT), 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/meta-llama-3-1-405b-instruct-vs-z-ai-glm-4-7) · [Meta Llama 3.1 405B Instruct](https://allocate.network/models/meta-llama-3-1-405b-instruct.md) · [GLM 4.7 FP8](https://allocate.network/models/z-ai-glm-4-7.md) · [Machine-readable catalog](https://allocate.network/catalog.json)