qwen3.5-9b vs gpt-5.4-mini: which mid-size model should you use?

at a glance

benchmarks

what are these models?

Qwen3.5-9B is Alibaba’s 9-billion-parameter language model from the Qwen3.5 series. It is open-weight and deployable on a single GPU, making it a practical choice for teams that need self-hosted inference without large infrastructure. It sits in the mid-tier of the Qwen3.5 family between the 4B and 27B variants.

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, but significantly more capable than GPT-5.4-nano. It is closed-source and accessed via OpenAI’s API.

benchmark breakdown

GPT-5.4-mini has a commanding lead on computer use. The OSWorld-Verified gap is 30+ points (72.1% vs 41.8%). For AI agent tasks that require operating a desktop — clicking, typing, navigating apps — GPT-5.4-mini is substantially more capable at this tier.

The agentic tool-use gap is also large. TAU2-Bench shows 93.4% vs 79.1% — a 14-point advantage. Multi-step tool-calling workflows favor GPT-5.4-mini clearly.

Graduate-level science and multimodal reasoning are closer. The GPQA Diamond gap is 6.3 points; MMMU-Pro is 6.5 points. These gaps are real but smaller, suggesting Qwen3.5-9B is more competitive on knowledge-intensive tasks than on agentic ones.

what people are saying

when to use Qwen3.5-9B

• cost is a constraint and you’re running high-volume inference
• you need to self-host for data privacy or regulatory compliance
• you want to fine-tune on domain-specific data
• your workload is knowledge or reasoning-heavy rather than agentic
• you need Apache 2.0 licensing for commercial use

when to use GPT-5.4-mini

• you need strong agentic and computer-use capabilities out of the box
• your use case involves multi-step tool calling with high reliability requirements
• you want a hosted API with minimal infrastructure overhead
• you need the best multimodal reasoning in the mini-tier price range

fine-tuning turns gaps into advantages

Generic benchmarks capture average performance — not how models behave inside your workflows. With fine-tuning, those gaps become highly tractable.

For agentic tasks, targeted training on your own tool-calling traces and computer-use trajectories can dramatically improve planning and execution. A model like Qwen3.5-9B can close a substantial portion of the gap while running on your own infrastructure with full control.

For knowledge-heavy tasks (GPQA, MMMU-Pro), the margin is already narrow. Fine-tuning on domain-specific data is often enough for Qwen3.5-9B to match or exceed GPT-5.4-mini where it matters. In practice, smaller open models consistently outperform larger closed ones once they’re specialized — turning general capability into targeted advantage.

frequently asked questions

is qwen3.5-9b as good as gpt-5.4-mini?

on knowledge benchmarks: close. on agentic and computer-use tasks: gpt-5.4-mini has a clear lead out of the box. fine-tuning qwen3.5-9b on your specific task typically closes much of the gap.

can i self-host qwen3.5-9b?

yes. it’s open-weight under apache 2.0. at 9B parameters, it runs on a single A10G or equivalent with reasonable throughput. quantized versions run on consumer hardware.

which is faster?

self-hosted qwen3.5-9b at 9B parameters is generally faster than mid-size closed models at comparable hardware. gpt-5.4-mini via api is optimized for latency, but self-hosted qwen gives you full control over batching.

should i fine-tune or use the base model?

if you have a well-defined, high-volume task — especially a knowledge or reasoning task where the gap is already small — fine-tuning qwen3.5-9b is almost always worth it.