qwen3.5-122b-a10b vs gpt-5.4: which model should you use?

at a glance

benchmarks

what are these models?

Qwen3.5-122B-A10B is a Mixture-of-Experts (MoE) model from Alibaba’s Qwen3.5 series. It has 122 billion total parameters but activates only 10 billion per forward pass, giving it the knowledge capacity of a large dense model at roughly 10B inference cost. It is open-weight under Apache 2.0.

GPT-5.4 is OpenAI’s flagship model in the GPT-5.4 family — the highest-capability tier, aimed at tasks requiring maximum reasoning, agentic reliability, and multimodal understanding. It is closed-source and accessed via OpenAI’s API.

benchmark breakdown

Qwen3.5-122B-A10B beats GPT-5.4 on HLE with tools. At 47.5% vs 41.5%, the open MoE model outperforms OpenAI’s flagship on Humanity’s Last Exam — the hardest knowledge benchmark available. This is a striking result for an open-weight model running at 10B active parameters.

GPT-5.4 leads on agentic tasks. TAU2-Bench (93.4% vs 79.5%) and Terminal Bench 2 (60.0% vs 49.4%) show clear advantages for agentic and shell-based workflows.

OSWorld gap is 14 points. For desktop automation — clicking, navigating, and operating apps — GPT-5.4 is substantially stronger.

MMMU-Pro is a virtual tie. 76.9% vs 76.6% — for multimodal reasoning, both models are effectively equivalent.

GPQA Diamond gap is only 1.4 points. For graduate-level science, the two models are nearly indistinguishable.

what people are saying

when to use Qwen3.5-122B-A10B

• you need near-frontier knowledge reasoning at low inference cost (10B active params)
• you want open weights for self-hosting, fine-tuning, or licensing flexibility
• your task is knowledge-intensive and HLE-type difficulty is relevant
• data privacy or compliance requirements prevent using external APIs
• cost at scale is a concern — MoE inference is dramatically cheaper than a dense large model

when to use GPT-5.4

• you need maximum agentic reliability for computer use and tool-calling workflows
• you want the highest terminal task automation performance
• you want a zero-config hosted API at the frontier
• your use case requires OpenAI’s safety and reliability guarantees

scaling performance with efficient fine-tuning

The MoE architecture makes Qwen3.5-122B-A10B uniquely powerful for fine-tuning: you get ~122B-level capacity at roughly 10B serving cost. That means you can fine-tune once and deploy a high-capability specialist model without incurring frontier-level inference costs.

On knowledge-intensive tasks — where it already outperforms GPT-5.4 on HLE — domain-specific fine-tuning compounds the advantage, pushing performance even further on your data.

For agentic use cases, the gap to GPT-5.4 is real but highly tractable. Fine-tuning on your own tool-calling trajectories and interaction patterns can close much of that gap, especially in stable environments — turning the model into a deeply optimized, workflow-specific agent.

frequently asked questions

what does “122b-a10b” mean?

it’s a mixture-of-experts model. 122b total parameters, but only 10b are active per token — the router selects which expert layers to use. inference cost and speed match a ~10b dense model. knowledge capacity reflects the full 122b.

is qwen3.5-122b-a10b as good as gpt-5.4?

on pure knowledge tasks: yes — it beats gpt-5.4 on hle with tools. on agentic and computer-use tasks: gpt-5.4 has a clear lead. for knowledge workloads, fine-tuned qwen3.5-122b-a10b will typically win.

can i self-host qwen3.5-122b-a10b?

yes — and the moe architecture helps here. active compute per forward pass is ~10b, so inference is much cheaper than loading a dense 122b model. full weights require significant vram, but quantized versions reduce this substantially.

should i fine-tune or use the base model?

for high-volume, domain-specific tasks, fine-tuning is almost always worth it. the moe architecture gives you specialist-level output at low inference cost — an unusually good combination for production use.