AllocateQwen3 235B A22B Instruct 2507 FP8 Throughput vs GLM 5.2
On provider list prices, Qwen3 235B A22B Instruct 2507 FP8 Throughput costs $0.20 per million input tokens against $1.40 for GLM 5.2: 7.0x apart. Output is $0.60 against $4.40 (7.3x). On Allocate both bill at list plus the 7% transaction fee.
Qwen3 235B A22B Instruct 2507 FP8 ThroughputG GLM 5.2Specifications 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 $450 a month on Qwen3 235B A22B Instruct 2507 FP8 Throughput and $3,220 on GLM 5.2 at list: a gap of $2,770, or 7.2x.
Choose Qwen3 235B A22B Instruct 2507 FP8 Throughput for
- The lower list price ($0.20 in / $0.60 out per M tokens)
- Fine-tuning under a permissive license (Apache 2.0)
Choose GLM 5.2 for
- Agents on open weights
- Code and structured outputs
- Fine-tuning toward an owned model
Common questions
Which is cheaper, Qwen3 235B A22B Instruct 2507 FP8 Throughput or GLM 5.2?
Qwen3 235B A22B Instruct 2507 FP8 Throughput, on this workload shape. At list prices it is $0.20/$0.60 per million tokens in and out against $1.40/$4.40 for GLM 5.2. Billed on Allocate: $0.21/$0.64 against $1.50/$4.71, list plus 7%.
Which has the bigger context window?
They match: both read 262,144 tokens (256K) per request.
Can I fine-tune Qwen3 235B A22B Instruct 2507 FP8 Throughput or GLM 5.2?
Both publish open weights (Qwen3 235B A22B Instruct 2507 FP8 Throughput: Apache 2.0; 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
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.