qwen3.5-35b-a3b vs gpt-5.4-mini: which model should you use?

at a glance

benchmarks

what are these models?

Qwen3.5-35B-A3B is a Mixture-of-Experts (MoE) model from Alibaba’s Qwen3.5 series. It has 35 billion total parameters but activates only 3 billion per forward pass, making it significantly faster and cheaper to run than a dense 35B model. It is open-weight under Apache 2.0.

GPT-5.4-mini is OpenAI’s mid-tier model in the GPT-5.4 family — faster and cheaper than the full GPT-5.4, targeting deployments that need strong capability without flagship pricing. It is closed-source and accessed via OpenAI’s API.

benchmark breakdown

Qwen3.5-35B-A3B beats GPT-5.4-mini on HLE with tools. This is the standout result: 47.4% vs 41.5% on Humanity’s Last Exam with tool use. HLE is designed to be near-impossible even for expert humans, making this a meaningful win for the open-weight model on the hardest knowledge benchmark.

GPT-5.4-mini leads on agentic tasks. Terminal Bench 2 (60.0% vs 40.5%) and OSWorld-Verified (72.1% vs 54.5%) show GPT-5.4-mini is substantially stronger at operating computers and shell environments.

TAU2-Bench gap is large. 93.4% vs 81.2% — for high-reliability multi-step tool workflows, GPT-5.4-mini wins clearly.

MMMU-Pro is nearly tied. 76.6% vs 75.1% — for multimodal reasoning tasks, both models are effectively equivalent.

what people are saying

when to use Qwen3.5-35B-A3B

• you want a fast, cheap MoE model that runs at 3B active-parameter cost
• your task is knowledge-intensive — especially where HLE scores matter
• you need to self-host or fine-tune on domain data
• you want open weights for licensing flexibility or privacy

when to use GPT-5.4-mini

• you need strong agentic and computer-use performance out of the box
• your use case involves terminal tasks, shell automation, or OS-level agents
• you want minimal infrastructure overhead via hosted API
• TAU2-Bench-style multi-step tool workflows are your core use case

compounding efficiency with fine-tuning

Qwen3.5-35B-A3B’s MoE architecture makes it uniquely efficient to fine-tune and deploy: you get ~35B-level knowledge capacity at roughly 3B inference cost. That efficiency compounds with fine-tuning — especially on knowledge-intensive tasks where it already outperforms GPT-5.4-mini on HLE. Training on your domain data can push that lead even further while keeping costs low.

For agentic and computer-use tasks, the baseline gap is larger — but also highly tunable. Fine-tuning on your own tool-calling trajectories and workflows can fundamentally reshape behavior, turning Qwen3.5-35B-A3B into a specialized agent that performs far beyond its base capabilities.

frequently asked questions

what does “35b-a3b” mean?

it’s a mixture-of-experts model with 35b total parameters but only 3b active per token. the router selects which expert weights to use for each token, so inference costs match a ~3b dense model while the model retains the knowledge capacity of the full 35b.

is qwen3.5-35b-a3b as good as gpt-5.4-mini?

on pure knowledge tasks: competitive, and it wins on hle with tools. on agentic and computer-use tasks: gpt-5.4-mini has a clear lead out of the box. for most production knowledge workloads, fine-tuning qwen3.5-35b-a3b will close the gap or exceed it.

can i self-host qwen3.5-35b-a3b?

yes. it’s open-weight under apache 2.0. thanks to the moe architecture, the active-parameter footprint is close to a 3b dense model, so it can run on modest hardware. full model weights require more vram to load, but inference is fast.

should i fine-tune or use the base model?

if you have a high-volume task with domain-specific requirements, fine-tuning is almost always worth it. the moe architecture is particularly attractive here — you get specialist performance at low inference cost.