at a glance
| GLM-4.7 Flash | Qwen3.5-27B | |
|---|---|---|
| provider | Zhipu AI | Alibaba |
| parameters | 730B total / 3B active (MoE) | 27B |
| context window | 128k tokens | 256k tokens |
benchmarks
what are these models?
GLM-4.7 Flash is Zhipu AI’s fast inference model from the GLM-4.7 family, optimized for speed and cost-efficiency. It shows notably strong terminal benchmark performance.
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, runnable on a single A100, and competitive across a wide range of tasks.
benchmark breakdown
Qwen3.5-27B leads on knowledge and coding. GPQA Diamond (85.5% vs 75.2%) shows a 10-point gap in scientific reasoning. SWE-bench Verified (72.4% vs 59.2%) shows a 13-point gap in software engineering. For knowledge-intensive tasks, Qwen3.5-27B is clearly stronger.
GLM-4.7 Flash wins on terminal tasks. Terminal Bench 2 shows 64.0%* vs 41.6% — a 22-point gap. For shell and CLI automation, GLM-4.7 Flash is the stronger choice.
Agentic tool use is essentially tied. TAU2-Bench is 79.5% vs 79.0% — functionally equivalent for multi-step tool-calling workflows.
what people are saying
when to use GLM-4.7 Flash
- terminal and CLI automation is your primary use case
- you need fast, cost-efficient inference
- multilingual tasks with Chinese-language focus are relevant
when to use Qwen3.5-27B
- scientific reasoning, coding, or knowledge-intensive tasks are your priority
- you want open weights for self-hosting, fine-tuning, or compliance
- you need Apache 2.0 licensing
- self-hosting on a single A100 is a requirement
maximizing gains with fine-tuning
Qwen3.5-27B’s open weights make it an exceptionally strong foundation for fine-tuning. In areas like knowledge and coding — where it already performs well — domain-specific tuning can further extend that lead and deliver highly specialized performance.
For terminal and CLI workflows, fine-tuning on your actual environment and task patterns can quickly close the gap to GLM-4.7 Flash, turning Qwen3.5-27B into a more unified, high-performing solution across both coding and operational tasks.
frequently asked questions
which model is better for coding?
qwen3.5-27b by a wide margin — 72.4% vs 59.2% on swe-bench verified.
which is better for terminal and shell tasks?
glm-4.7 flash leads substantially — 64.0%* vs 41.6% on terminal bench 2.
what does the asterisk mean on glm’s score?
the * indicates potential evaluation-specific conditions or self-reporting. verify independently if this benchmark is critical for your deployment.
can i fine-tune qwen3.5-27b?
yes — it’s open-weight under apache 2.0. at 27b parameters it fits on a single a100-80gb and can be fine-tuned with standard tools.