AllocateQwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3-VL-8B-Instruct
On provider list prices, Qwen3 235B A22B Instruct 2507 FP8 Throughput costs $0.20 per million input tokens against $0.18 for Qwen3-VL-8B-Instruct: effectively level. Output is $0.60 against $0.68 (1.1x). On Allocate both bill at list plus the 7% transaction fee.
Qwen3 235B A22B Instruct 2507 FP8 Throughput Qwen3-VL-8B-InstructSpecifications 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 $454 on Qwen3-VL-8B-Instruct at list: a gap of $4.
Choose Qwen3 235B A22B Instruct 2507 FP8 Throughput for
- Fine-tuning under a permissive license (Apache 2.0)
Choose Qwen3-VL-8B-Instruct for
- The lower list price ($0.18 in / $0.68 out per M tokens)
- Fine-tuning under a permissive license (Apache 2.0)
Common questions
Which is cheaper, Qwen3 235B A22B Instruct 2507 FP8 Throughput or Qwen3-VL-8B-Instruct?
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 $0.18/$0.68 for Qwen3-VL-8B-Instruct. Billed on Allocate: $0.21/$0.64 against $0.19/$0.73, 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 Qwen3-VL-8B-Instruct?
Both publish open weights (Qwen3 235B A22B Instruct 2507 FP8 Throughput: Apache 2.0; Qwen3-VL-8B-Instruct: Apache 2.0), 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.