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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.

Meta Llama 3.3 70B Instruct TurboG GLM 4.7 FP8
LabMetaZai Org
AccessOpen weightsOpen weights
Context window128K tokens198K tokens
List price, input$1.04 / M tokens$0.45 / M tokens
List price, output$1.04 / M tokens$2 / M tokens
Cached inputn/an/a
LicenseLlama communityMIT
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 $1,240 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.

GLM 4.7 FP8$0.45$2
Meta Llama 3.3 70B Instruct Turbo$1.04$1.04
InputOutput

Choose Meta Llama 3.3 70B Instruct Turbo for

  • Training toward a model you own
Meta Llama 3.3 70B Instruct Turbo details

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)
GLM 4.7 FP8 details

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.

Related comparisons

GLM 4.7 FP8 vs GLM 4.6 Fp8Meta Llama 3.3 70B Instruct Turbo vs Llama 4 Scout Instruct (17Bx16E)GLM 4.7 FP8 vs Llama 4 Scout Instruct (17Bx16E)Meta Llama 3.3 70B Instruct Turbo vs GLM 5.2GLM 4.7 FP8 vs GLM 5.2Meta Llama 3.3 70B Instruct Turbo vs Meta Llama 3.1 405B Instruct

Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3.3 70B Instruct TurboGLM 4.7 FP8

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.

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