GLM 5.3 Flash vs Qwen3.7 Plus
Compare GLM 5.3 Flash 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 5.3 Flash vs Qwen3.7 Plus on Vision Evals
GLM 5.3 Flash scores higher on 2 of the five Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 33.1%.
Overall, GLM 5.3 Flash averages 55.8% (#42 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
GLM 5.3 Flash is cheaper ($0.0006 vs $0.0008 per sample), while Qwen3.7 Plus is faster (7.8s vs 9.3s per sample).
GLM 5.3 Flash vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 9, 2026
| Property | GLM 5.3 Flash | Qwen3.7 Plus |
|---|---|---|
| Organization | Z.ai | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Aug 2026 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | 320B total, 18B active | Unknown |
| License | MIT | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.150 | $0.320 |
| Output $/1M | $0.500 | $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 | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 55.8% | 58.9% |
| Avg cost / sample | $0.0006 | $0.0008 |
| Avg speed / sample | 9.29s | 7.77s |
| By task | ||
| Object Detection | 33.1% | 60.1% |
| Counting | 55.4% | 50.0% |
| Identification | 84.4% | 84.4% |
| OCR (low) | 55.4% | 60.3% |
| by category |
|
|
| OCR (high) | 55.3% | 65.5% |
| by category |
|
|
| Reasoning (low) | 51.0% | 39.7% |
| Reasoning (high) | 59.6% | 68.2% |
GLM 5.3 Flash vs Qwen3.7 Plus: Overview
GLM-5.3-Flash is the first natively multimodal model in Z.ai's GLM-5 series, a mixture-of-experts transformer with roughly 320 billion total parameters and 18 billion activated per token. It routes each token through 8 of 288 experts across 45 language layers that interleave KDA linear attention with sparse multi-head latent attention, and pairs them with a 24-layer vision encoder that handles image and video input. The checkpoint declares a maximum context length of 1,048,576 tokens, ships in native FP8, and includes a multi-token prediction draft layer for speculative decoding. Z.ai reports that the hybrid attention design reduces attention computation by 3.01x and KV cache size by 4.44x relative to GLM-5.3.
The model starts from a newly trained base built on a 30 trillion token multimodal pre-training corpus and adopts Manifold-Constrained Hyper-Connections to improve scaling efficiency. Vision is integrated into the coding and agent loop, so the model can inspect interfaces, rendered output, and images while operating across code, browsers, and graphical user interfaces. Z.ai reports scores of 84.3 on Terminal-Bench 2.1, 63.4 on DeepSWE 1.1, 55.3 on Humanity's Last Exam with tools, and 48.8 on AutomationBench, and the model exposes low, high, and max thinking modes.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed slightly better overall. The two split the five vision tasks 2 to 2, but Qwen3.7 Plus averages 58.9% (#35 of 61) against 55.8% (#42 of 61) for GLM 5.3 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.7 Plus leads with 60.1% against 33.1%. This is the widest gap between the two models across the benchmark's tasks.
GLM 5.3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0006 per sample against $0.0008. GLM 5.3 Flash is priced at $0.15 per 1M input tokens and $0.50 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.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 9.3s. 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.