Head-to-head
Qwen3.8-27B vs GLM-5.3-Flash
A 3.2-point composite gap — but the real story is the dimension mix. Same weights, unpacked: who leads where, and whether the premium is justified.
The short answer
The composites differ by 3.2 points, and their strengths do not overlap.
Stronger overall: GLM-5.3-Flash. If you only run one workload, read the dimension table instead.
Overlaid radar
Same weights, same scale; overlap means parity
- Qwen3.8-27B90.5
- GLM-5.3-Flash93.7
Dimension deltas
A on the left, B on the right; 100 max per row
Qwen3.8-27B leads 0, GLM-5.3-Flash leads 8.
Spec sheet
Hard specs in one table
| Item | Qwen3.8-27B | GLM-5.3-Flash |
|---|---|---|
| Composite | 90.5 | 93.7 |
| Tier | A+ | S |
| Visual perception & grounding | 94 | 95WIN |
| Document parsing & Chinese OCR | 94 | 96WIN |
| Chart & document reasoning | 91 | 93WIN |
| Video & long-horizon reasoning | 88 | 90WIN |
| GUI agents & tool use | 88 | 94WIN |
| Generation & editing | 80 | 86WIN |
| Chinese context & culture | 92 | 94WIN |
| Ecosystem, cost & availability | 88 | 98WIN |
| Output price | ¥1 / M | ¥3 / M |
| Context | 262K (up to 1M) | 1M |
| Parameters | 27B dense | 320B total / 18B active (MoE) |
| Licence | Open weights (Apache-2.0) | Open weights (MIT) |
| Native multimodal | Yes | Yes |
| Evidence grade | A | B |
How to choose
Choose Qwen3.8-27B if…
- Single-machine or small-cluster on-prem
- Domain document fine-tuning
- Cost-controlled image-text batch work
- You cannot accept its limitation: Generation 80 and video 88: creative work and long video still need a larger model.
How to choose
Choose GLM-5.3-Flash if…
- Domestic-chip and on-prem projects on a budget
- Document batches and receipt extraction
- Commercial integration that needs an MIT licence
- You cannot accept its limitation: Video 90 and generation 86: neither long-video QA nor creative generation is its scenario.
Scenario check
Five scenarios, who holds up
| Scenario | Qwen3.8-27B | GLM-5.3-Flash | Better |
|---|---|---|---|
| Chinese receipt OCR | 94 | 95 | GLM-5.3-Flash |
| Chart data extraction | 91 | 92 | GLM-5.3-Flash |
| 30-min video QA | 86 | 88 | GLM-5.3-Flash |
| Mobile GUI control | 86 | 94 | GLM-5.3-Flash |
| Chinese cultural context | 92 | 94 | GLM-5.3-Flash |
Comparison FAQ
Qwen3.8-27B vs GLM-5.3-Flash: which is stronger?
Composite: Qwen3.8-27B 90.5, GLM-5.3-Flash 93.7. Qwen3.8-27B leads on none; GLM-5.3-Flash leads on Visual perception & grounding, Document parsing & Chinese OCR, Chart & document reasoning, Video & long-horizon reasoning, GUI agents & tool use, Generation & editing, Chinese context & culture, Ecosystem, cost & availability. The stronger overall is GLM-5.3-Flash.
How much more expensive is Qwen3.8-27B than GLM-5.3-Flash?
Qwen3.8-27B is ¥1/1M output tokens; GLM-5.3-Flash is ¥3/1M. On output price, GLM-5.3-Flash is pricier; real cost also depends on image tokens and input share.
Can GLM-5.3-Flash replace Qwen3.8-27B?
It depends on your primary workload. If it sits in GLM-5.3-Flash’s strengths (Domestic-chip and on-prem projects on a budget, Document batches and receipt extraction, Commercial integration that needs an MIT licence), yes. If you need Single-machine or small-cluster on-prem, switching will cost quality.
Personal ranking · Not an official score
Every ranking, score and comparison on this page is an independent personal ranking, not an official score, and represents no vendor, institution or regulator. Scores are a subjective mapping of public evidence under our own weights — change the weights and the order changes. Disagree, or spotted a factual error? Contact the site owner: jizhi2024@foxmail.com — we check each report and log corrections in the changelog.