at a glance
| Qwen3.5-27B | GPT-5.4-mini | |
|---|---|---|
| provider | Alibaba | OpenAI |
| parameters | 27B | ~mid-size (est.) |
| context window | 256k tokens | 400k tokens |
benchmarks
what are these models?
Qwen3.5-27B is Alibaba’s 27-billion-parameter dense language model from the Qwen3.5 series. It is open-weight under Apache 2.0, making it self-hostable and fine-tunable. At 27B parameters, it sits between the agile 9B and the MoE-based larger variants in the Qwen3.5 family.
GPT-5.4-mini is OpenAI’s mid-tier model in the GPT-5.4 family — targeting deployments that need strong capability at lower cost than the full GPT-5.4. It is closed-source and accessed via OpenAI’s API.
benchmark breakdown
Qwen3.5-27B beats GPT-5.4-mini on HLE with tools. At 48.5% vs 41.5%, Qwen3.5-27B outperforms the closed model on Humanity’s Last Exam — the hardest knowledge benchmark available, designed to challenge expert humans. This is a meaningful result.
GPT-5.4-mini leads on computer use. OSWorld-Verified shows 72.1% vs 56.2% — a 16-point gap. For desktop automation and computer-use agents, GPT-5.4-mini has a clear advantage.
The terminal and agentic gaps are substantial. Terminal Bench 2 (60.0% vs 41.6%) and TAU2-Bench (93.4% vs 79.0%) show GPT-5.4-mini is consistently stronger on tool-calling workflows.
GPQA Diamond and MMMU-Pro are close. The science reasoning and multimodal gaps are 2.5 points or less — effectively competitive for most real-world tasks.
what people are saying
when to use Qwen3.5-27B
- you need strong expert-knowledge reasoning and HLE-level tasks matter
- cost is a constraint at scale — self-hosting a 27B model is cheap per token
- you want to fine-tune on domain-specific data with full weight access
- data privacy or regulatory constraints prevent sending data to external APIs
- you need Apache 2.0 licensing for commercial use
when to use GPT-5.4-mini
- you need strong agentic performance: computer use, shell tasks, tool calling
- you want minimal infrastructure overhead via hosted API
- your workload is TAU2-Bench-style multi-step tool orchestration
- you need the best computer-use accuracy in the mini tier
fine-tuning turns general models into specialists
Generic benchmarks reflect average performance — not how models perform on your exact tasks. A model like Qwen3.5-27B, when fine-tuned on your data (document analysis, code generation, structured extraction), will often match or exceed GPT-5.4-mini on that task — while significantly reducing ongoing API cost.
For agentic use cases, the baseline gap is larger, but also highly optimizable. Fine-tuning on your own tool-calling trajectories and workflow-specific interactions can close much of the TAU2 and OSWorld gap, turning a strong general model into a high-performing, domain-specific agent.
frequently asked questions
is qwen3.5-27b as good as gpt-5.4-mini?
on expert knowledge tasks: yes — it actually beats gpt-5.4-mini on hle with tools. on agentic and computer-use tasks: gpt-5.4-mini leads clearly. for knowledge-heavy workloads, qwen3.5-27b fine-tuned on your data will typically win.
can i self-host qwen3.5-27b?
yes. it’s open-weight under apache 2.0. a 27b dense model requires around 2x a100 at fp16, or fits on a single a100-80gb with quantization. inference providers like together.ai and fireworks.ai also offer hosted access.
which is faster?
self-hosted qwen3.5-27b has throughput you control directly. gpt-5.4-mini via api is optimized for low latency. for latency-critical production workloads, benchmark both on your specific request patterns.
should i fine-tune?
if you have a high-volume, well-defined task, fine-tuning qwen3.5-27b is almost always worth it. the 27b parameter count gives it enough capacity to specialize deeply while remaining cost-effective to serve.