Model comparison
Qwen vs GLM API
Qwen 3.7 Plus and GLM 5.2 are both first-tier starting points for structured output, extraction, and OpenAI-compatible Chinese LLM experiments.
Route shortlist
Benchmark comparison
| Model | Total /100 | Extraction | Instructions | Math | Tools | Fully correct | Tokens | Cost (USD) | CPS ($/100 points) |
|---|---|---|---|---|---|---|---|---|---|
GLM 5.2glm-5.2 | 99.58 | 98.33 | 100.00 | 100.00 | 100.00 | 59/60 | 104,610 | $0.165644 | $0.166337 |
Qwen 3.7 Plusqwen3.7-plus | 92.72 | 99.56 | 100.00 | 72.00 | 99.33 | 52/60 | 122,334 | $0.116596 | $0.125748 |
BenchLocal V3.2 · Original 27 models: 2 October 2026; three full-suite supplements: 15 October 2026 · 60 tasks per model. Total = mean of the four category scores. The original models retain 45 V3 tasks and 15 revised tool tasks; the three additional model IDs each completed all 60 identical tasks. Near-ceiling scores do not establish general capability; V3 scores are not directly comparable. CPS = Cost per Score; shown as USD per 100 score points: retained execution cost ÷ unrounded total score × 100. Free models are shown at $0. Paid-model costs are historical charges, excluding failed requests and discarded retests.
Decision guide
Recommended route
Test both on your schema validators; Qwen 3.7 Plus and GLM 5.2 each scored 90 overall in the earlier pre-V3 evaluation in the earlier pre-V3 evaluation in the GPTokens benchmark summary.
Structured output
Both Qwen 3.7 Plus and GLM 5.2 scored 100 on structured output in the benchmark snapshot.
Selection rule
Choose based on your own data extraction, tool calling, latency, and cost checks rather than family name alone.
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