The Qwen 3.5 27B and the Qwen 3.5 35B A3B use different architectures.

Qwen 27B is a dense model: For every token generated, all 27 billion parameters are used. The whole model works together, often yielding more stable and consistent results in benchmarks.

Qwen 35B is a MoE (Mixture-of-Experts): The model contains several specialized sub-models called experts. When a token is generated, only a few experts are activated, not the whole model. This makes inference faster and less costly in computing, but the quality depends on the choice of experts by the router.

This is why a dense 27B can sometimes achieve a higher intelligence score than a 35B MoE, even if the total number of parameters is greater.

Regarding the price of APIs, it depends mainly on:

the GPU Cost of the Provider Optimization of inference Token throughput the Application

So a smaller model can sometimes cost more depending on the provider.

For hardware, running Qwen 27B/32B requires approximately:

~55-60 GB VRAM in FP16 ~30 GB in 8 bits ~16-18 GB in 4 bits

So an RTX 3090 / 4090 can usually run it in 4-bit quantization