Allocate
ModelsSign inTalk to sales
Comparisons

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.

Meta Llama 3.3 70B Instruct TurboG GLM 5.2
LabMetaZai Org
AccessOpen weightsOpen weights
Context window128K tokens256K tokens
List price, input$1.04 / M tokens$1.4 / M tokens
List price, output$1.04 / M tokens$4.4 / M tokens
Cached inputn/a$0.26 / M tokens
LicenseLlama communityNot listed
Fine-tunableYesYes

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.

Meta Llama 3.3 70B Instruct Turbo$1.04$1.04
GLM 5.2$1.40$4.40
InputOutput

Choose Meta Llama 3.3 70B Instruct Turbo for

  • The lower list price ($1.04 in / $1.04 out per M tokens)
Meta Llama 3.3 70B Instruct Turbo details

Choose GLM 5.2 for

  • Agents on open weights
  • Code and structured outputs
  • Fine-tuning toward an owned model
GLM 5.2 details

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.

Related comparisons

Meta Llama 3.3 70B Instruct Turbo vs GLM 4.7 FP8GLM 5.2 vs GLM 4.7 FP8GLM 5.2 vs GLM 5.1 FP4Meta Llama 3.3 70B Instruct Turbo vs Meta Llama 3.1 405B InstructGLM 5.2 vs Meta Llama 3.1 405B InstructMeta Llama 3.3 70B Instruct Turbo vs Llama 4 Scout Instruct (17Bx16E)

Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3.3 70B Instruct TurboGLM 5.2

Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.

Route both modelsTalk to sales