AllocateQwen 3.5 vs GLM 4.7 FP8
On provider list prices, GLM 4.7 FP8 costs $0.45 per million input tokens against $0.60 for Qwen 3.5: 1.3x apart. Output is $2 against $3.60 (1.8x). On Allocate both bill at list plus the 7% transaction fee.
Qwen 3.5G 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 $1,980 on Qwen 3.5 at list: a gap of $740, or 1.6x.
Qwen 3.5 reads 256K tokens per request against 198K for GLM 4.7 FP8, 1.3x the window. That decides which one can take whole documents without splitting them.
Choose Qwen 3.5 for
- Multilingual support agents
- Translation-adjacent workflows
- Fine-tuning under Apache 2.0
Choose GLM 4.7 FP8 for
- The lower list price ($0.45 in / $2 out per M tokens)
- Fine-tuning under a permissive license (MIT)
Common questions
Which is cheaper, Qwen 3.5 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 $0.60/$3.60 for Qwen 3.5. Billed on Allocate: $0.48/$2.14 against $0.64/$3.85, list plus 7%.
Which has the bigger context window?
Qwen 3.5: 262,144 tokens (256K) against 202,752 (198K) for GLM 4.7 FP8.
Can I fine-tune Qwen 3.5 or GLM 4.7 FP8?
Both publish open weights (Qwen 3.5: Apache 2.0; 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.