GLM 5V Turbo vs Qwen3.7 Plus
Compare GLM 5V Turbo and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.
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Models in this comparison
GLM 5V Turbo vs Qwen3.7 Plus on Vision Evals
Qwen3.7 Plus scores higher on 4 of the six Vision Evals tasks.
The widest gap is Reasoning, where Qwen3.7 Plus leads 39.7% to 31.8%.
Overall, GLM 5V Turbo averages 65.3% (#25 of 33) against 67.4% (#18 of 33) for Qwen3.7 Plus.
Qwen3.7 Plus is cheaper ($0.0008 vs $0.0031 per sample), while GLM 5V Turbo is faster (6.3s vs 7.0s per sample).
GLM 5V Turbo vs Qwen3.7 Plus Comparison Table
Evals updated August 26, 2026Pricing updated August 26, 2026
| Property | GLM 5V Turbo | Qwen3.7 Plus |
|---|---|---|
| Organization | Z.ai | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Apr 2026 | — |
| Context Window | 200K | — |
| Parameters | ||
| License | Proprietary | |
| Pricing per 1M tokens | ||
| Input $/1M | $1.20 | $0.320 |
| Output $/1M | $4.00 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| object-detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Vision Language | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 65.3% | 67.4% |
| Avg cost / sample | $0.0031 | $0.0008 |
| Avg speed / sample | 6.35s | 7.01s |
| By task | ||
| Object Detection | 56.5% $0.0052 | 60.1% $0.0013 |
| Counting | 48.6% $0.0017 | 50.0% $0.0004 |
| Identification | 84.4% $0.0015 | 84.4% $0.0003 |
| OCR | 89.3% $0.0030 | 86.5% $0.0009 |
| Data Extraction | 81.4% $0.0018 | 83.5% $0.0004 |
| Reasoning (low) | 31.8% $0.0017 | 39.7% $0.0003 |
| Reasoning (high) | 49.7% $0.0069 | 68.2% $0.0043 |
GLM 5V Turbo vs Qwen3.7 Plus: Overview
GLM-5V-Turbo is a native multimodal model from Z.ai that extends the GLM family with joint image, video, and text input aimed at vision-centered coding and agent workflows. The model reads screenshots, design drafts, document layouts, and interface captures and generates runnable code from them, covering tasks such as turning a visual design into a working front end, diagnosing rendering and layout defects from screen captures, and operating graphical user interfaces during long-horizon agent runs. It accepts roughly 200,000 input tokens and can emit up to 131,072 output tokens in a single response, which supports sessions that hold specifications, source files, logs, and visual references at the same time.
Training includes a joint reinforcement learning stage spanning more than 30 tasks simultaneously, an approach Z.ai describes as a way to counter the trade-off in which improving visual recognition degrades programming ability and the reverse. Reported evaluations cover pure-text coding on the backend, frontend, and repository exploration tracks of CC-Bench-V2, together with agent execution suites such as PinchBench, ClawEval, and ZClawBench, indicating that text coding behavior is retained after visual input is added.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed better. It scores higher on 4 of the six vision tasks and averages 67.4% (#18 of 33) against 65.3% (#25 of 33) for GLM 5V Turbo. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Qwen3.7 Plus leads with 39.7% against 31.8%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0031. GLM 5V Turbo is priced at $1.20 per 1M input tokens and $4.00 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GLM 5V Turbo is faster. Across Roboflow's Vision Evals it averaged 6.3s per inference against 7.0s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.