AllocateMeta 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.
Meta Llama 3.1 405B InstructG GLM 4.7 FP8Specifications 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.
Related comparisons
Run the numbers on your workload
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.