Qwen3.7 Flash vs Qwen3.7 Plus
Compare Qwen3.7 Flash and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
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Models in this comparison
Qwen3.7 Flash vs Qwen3.7 Plus on Vision Evals
Qwen3.7 Plus scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 42.8%.
Overall, Qwen3.7 Flash averages 61.7% (#29 of 31) against 67.4% (#18 of 31) for Qwen3.7 Plus.
Qwen3.7 Flash is both cheaper ($0.0001 vs $0.0008 per sample) and faster (6.3s vs 7.0s per sample).
Qwen3.7 Flash vs Qwen3.7 Plus Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Qwen3.7 Flash | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Jul 2026 | — |
| Context Window | 1.0M | — |
| Parameters | ||
| License | Proprietary | |
| Pricing per 1M tokens | ||
| Input $/1M | $0.030 | $0.320 |
| Output $/1M | $0.130 | $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 | 61.7% | 67.4% |
| Avg cost / sample | $0.0001 | $0.0008 |
| Avg speed / sample | 6.32s | 7.01s |
| By task | ||
| Object Detection | 42.8% $0.0001 | 60.1% $0.0013 |
| Counting | 46.0% <$0.0001 | 50.0% $0.0004 |
| Identification | 84.4% <$0.0001 | 84.4% $0.0003 |
| OCR | 84.1% $0.0001 | 86.5% $0.0009 |
| Data Extraction | 78.3% <$0.0001 | 83.5% $0.0004 |
| Reasoning (low) | 34.4% <$0.0001 | 39.7% $0.0003 |
| Reasoning (high) | 60.9% $0.0005 | 68.2% $0.0043 |
Qwen3.7 Flash vs Qwen3.7 Plus: Overview
Qwen3.7 Flash is the low-latency, cost-oriented tier of Alibaba's Qwen3.7 series, a vision-language reasoning model that accepts interleaved text and image input and returns text. It is built as a hybrid thinking model: like the rest of the Qwen3.7, Qwen3.6, and Qwen3.5 families served through Alibaba Cloud Model Studio, it can either emit an explicit reasoning trace before answering or respond directly, with thinking behavior controlled by an enable_thinking switch that defaults to on for the Qwen3.7 generation. The model exposes a context window of roughly one million tokens and a maximum generation length of 65,536 tokens, which allows long multi-image sequences, long documents, and extended agent trajectories to be held in a single request.
Functionally, Qwen3.7 Flash targets multimodal agent workloads rather than pure chat. Reported strengths include object recognition, spatial understanding, and perception of real-world scenes, alongside visual coding, search, and computer-use style interaction where the model reads screen content and reasons over interface state. Weights are not published; the model is a proprietary endpoint positioned below Qwen3.7 Plus and Qwen3.7 Max in the same series, and it supports function calling and tool use for agentic pipelines.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed better. It scores higher on 5 of the six vision tasks and averages 67.4% (#18 of 31) against 61.7% (#29 of 31) for Qwen3.7 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, Qwen3.7 Plus leads with 60.1% against 42.8%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0001 per sample against $0.0008. Qwen3.7 Flash is priced at $0.03 per 1M input tokens and $0.13 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 Flash 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 open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.