Qwen3.5 9b vs Qwen3.7 Flash
Compare Qwen3.5 9b and Qwen3.7 Flash side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
Compare Qwen3.5 9b vs Qwen3.7 Flash live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Extract and compare text from images across multiple models.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Qwen3.5 9b vs Qwen3.7 Flash on Vision Evals
Qwen3.5 9b scores higher on 4 of the six Vision Evals tasks.
The widest gap is Reasoning, where Qwen3.5 9b leads 47.7% to 34.4%.
Overall, Qwen3.5 9b averages 64.3% (#40 of 53) against 61.5% (#45 of 53) for Qwen3.7 Flash.
Qwen3.7 Flash is both cheaper ($0.0001 vs $0.0017 per sample) and faster (6.3s vs 33.6s per sample).
Qwen3.5 9b vs Qwen3.7 Flash Comparison Table
Evals updated September 5, 2026Pricing updated September 11, 2026
| Property | Qwen3.5 9b | Qwen3.7 Flash |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Jul 2026 |
| Context Window | 262K | 1.0M |
| Parameters | 9B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.030 |
| Output $/1M | $0.150 | $0.130 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 64.3% | 61.5% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0017 | $0.0001 |
| Avg speed / sample | 33.65s | 6.28s |
| By task | ||
| Object Detection | 46.6% ±0.6, Mean of 3 runs, range 45.8 to 47.0 | 42.8% |
| Counting | 51.8% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 46.0% |
| Identification | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 84.4% |
| OCR | 77.5% ±7.3, Mean of 3 runs, range 68.4 to 83.0 | 84.1% |
| Data Extraction | 78.7% ±2.1, Mean of 3 runs, range 76.3 to 80.4 | 77.3% |
| Reasoning (low) | 47.7% ±2.3, Mean of 3 runs, range 45.7 to 50.3 | 34.4% |
| Reasoning (high) | – | 61.6% |
Qwen3.5 9b vs Qwen3.7 Flash: Overview
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.
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, Qwen3.5 9b performed better. It scores higher on 4 of the six vision tasks and averages 64.3% (#40 of 53) against 61.5% (#45 of 53) for Qwen3.7 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Qwen3.5 9b leads with 47.7% against 34.4%. 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 $0.0017. 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 33.6s. 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.