Qwen3.5 9b vs Qwen3.7 Plus
Compare Qwen3.5 9b and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Models in this comparison
Qwen3.5 9b vs Qwen3.7 Plus on Vision Evals
Qwen3.5 9b scores higher on 2 of the 4 Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 38.1%.
Overall, Qwen3.5 9b averages 56.0% (#40 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0021 per sample) and faster (7.8s vs 41.4s per sample).
Qwen3.5 9b vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 9, 2026
| Property | Qwen3.5 9b | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Mar 2026 | Jun 2026 |
| Context Window | 262K | — |
| Parameters | 9B | Unknown |
| License | Apache 2.0 | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.320 |
| Output $/1M | $0.150 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | 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 | 56.0% 4/5 tasks | 58.9% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0021 | $0.0008 |
| Avg speed / sample | 41.36s | 7.77s |
| By task | ||
| Object Detection | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 | 60.1% |
| Counting | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 | 50.0% |
| Identification | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 84.4% |
| OCR (low) | – | 60.3% |
| by category |
| |
| OCR (high) | – | 65.5% |
| by category |
| |
| Reasoning (low) | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 | 39.7% |
| Reasoning (high) | – | 68.2% |
Qwen3.5 9b vs Qwen3.7 Plus: 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed slightly better overall. The two split the 4 vision tasks 2 to 2, but Qwen3.7 Plus averages 58.9% (#35 of 61) against 56.0% (#40 of 61) for Qwen3.5 9b. 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 38.1%. 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 $0.0021. 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 41.4s. 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.