# Meta Llama 3.3 70B Instruct Turbo vs GLM 4.7 FP8

On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $0.45 for GLM 4.7 FP8: effectively level. Output is $1.04 against $2 (1.9x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.3 70B Instruct Turbo | GLM 4.7 FP8 |
| --- | --- | --- |
| Lab | Meta | Zai Org |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 198K tokens |
| List price, input | $1.04 / M tokens | $0.45 / M tokens |
| List price, output | $1.04 / 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 $1,612 on Meta Llama 3.3 70B Instruct Turbo at list: a gap of $372, or 1.3x.

GLM 4.7 FP8 reads 198K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 1.5x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.3 70B Instruct Turbo 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 128K tokens)
- Fine-tuning under a permissive license (MIT)

## Common questions

### Which is cheaper, Meta Llama 3.3 70B Instruct Turbo 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 $1.04/$1.04 for Meta Llama 3.3 70B Instruct Turbo. Billed on Allocate: $0.48/$2.14 against $1.11/$1.11, list plus 7%.

### Which has the bigger context window?

GLM 4.7 FP8: 202,752 tokens (198K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

### Can I fine-tune Meta Llama 3.3 70B Instruct Turbo or GLM 4.7 FP8?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: 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.

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[HTML page](https://allocate.network/compare/meta-llama-3-3-70b-instruct-turbo-vs-z-ai-glm-4-7) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [GLM 4.7 FP8](https://allocate.network/models/z-ai-glm-4-7.md) · [Machine-readable catalog](https://allocate.network/catalog.json)