# Meta Llama 3.3 70B Instruct Turbo vs GLM 5.2

On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $1.40 for GLM 5.2: 1.3x apart. Output is $1.04 against $4.40 (4.2x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.3 70B Instruct Turbo | GLM 5.2 |
| --- | --- | --- |
| Lab | Meta | Zai Org |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 256K tokens |
| List price, input | $1.04 / M tokens | $1.40 / M tokens |
| List price, output | $1.04 / M tokens | $4.40 / M tokens |
| Cached input | n/a | $0.26 / M tokens |
| License | Llama community | 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.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $1,612 a month on Meta Llama 3.3 70B Instruct Turbo and $3,220 on GLM 5.2 at list: a gap of $1,608, or 2.0x.

GLM 5.2 reads 256K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 2.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.3 70B Instruct Turbo for

- The lower list price ($1.04 in / $1.04 out per M tokens)

## Choose GLM 5.2 for

- Agents on open weights
- Code and structured outputs
- Fine-tuning toward an owned model

## Common questions

### Which is cheaper, Meta Llama 3.3 70B Instruct Turbo or GLM 5.2?

Meta Llama 3.3 70B Instruct Turbo, on this workload shape. At list prices it is $1.04/$1.04 per million tokens in and out against $1.40/$4.40 for GLM 5.2. Billed on Allocate: $1.11/$1.11 against $1.50/$4.71, list plus 7%.

### Which has the bigger context window?

GLM 5.2: 262,144 tokens (256K) 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 5.2?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: Llama community; GLM 5.2: 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/meta-llama-3-3-70b-instruct-turbo-vs-z-ai-glm-5-2) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [GLM 5.2](https://allocate.network/models/z-ai-glm-5-2.md) · [Machine-readable catalog](https://allocate.network/catalog.json)