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.
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Usage
Past 30 DaysVision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across six vision tasks, and answers are scored against ground truth.
Evals updated September 3, 2026Pricing updated September 3, 2026
Qwen3.5 9b averages 66.1% across the six Vision Evals tasks, ranking #33 of 52 models overall.
Its weakest relative showing is OCR, ranking #48 of 52 at 79.1%.
At $0.0008 per sample it is the 21st cheapest of the 52 benchmarked models, and its average inference time of 31.3s per sample makes it the 47th fastest.
Field medians: Object Detection 54.1%, Counting 56.8%, Identification 84.4%, OCR 88.1%, Data Extraction 84.5%, Reasoning 53.6%.
| Task | Score | Field (0 to 100) | Rank | Cost / sample | Speed |
|---|---|---|---|---|---|
| Object Detection | 45.8% | #32 of 52 | $0.0008 | 27.53s | |
| Counting | 54.0% | #31 of 52 | $0.0009 | 43.37s | |
| Identification | 84.4% | #21 of 52 | $0.0005 | 19.31s | |
| OCR | 79.1% | #48 of 52 | $0.0006 | 28.92s | |
| Data Extraction | 82.7% | #33 of 52 | $0.0004 | 13.72s | |
| Reasoning | 50.3% | #29 of 52 | $0.0010 | 46.90s |
Overall benchmark score against estimated cost per sample, on a log scale. Upper-left is the sweet spot: high quality at low cost.
51 models on the current benchmark · scores and efficiency pooled across all six tasks at low effort · Qwen3.5 9B highlighted
Qwen3.5 9b scores are from a single run per task; a three-run re-run under the current protocol is pending · Methodology
View all Vision Evals →3 quantizations of Qwen3.5 9B, served with vLLM from the published weights and scored on the same tasks as the hosted models. Expand a row for the GPUs it was measured on.
Self-hosted rows run with thinking on at low effort, the same setting as the hosted frontier models. Some hosted open-weight rows still run without thinking, so a self-hosted quant can score above its own hosted API. Quantization still costs a little precision, and scores vary between runs.
Qwen3.5 9b costs $0.100 per 1M input tokens and $0.150 per 1M output tokens.
Pricing updated Sep 3, 2026
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Qwen3.5 9b is released under Apache-2.0, a permissive license. The Qwen3.5 9b license lets you run, fine-tune, and redistribute the model in commercial products with no obligation to open-source related code changes, so no separate commercial license is required.
Apache-2.0 grants an express patent license that terminates if you bring a patent claim over the work, and it disclaims warranties. Validate Qwen3.5 9b on your own data before you depend on it in production.
Read the full Apache 2.0 license ↗This is the straightforward case: a permissive license is the best technical solution and you are free to deploy Qwen3.5 9b commercially without open-sourcing your own code.
Do not hesitate to reach out with questions for your commercial project — our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.
Talk to salesThis model is released under the Apache License 2.0, a permissive open-source license that allows commercial use, modification, distribution, and patent use.
Yes. Under the terms of the Apache 2.0 license, you can freely use this model for commercial purposes, including in proprietary products. You must retain the copyright notice and disclaimers when redistributing.
License information is provided as a guide and is not legal advice.
Yes. Qwen3.5 9b accepts image input and handles OCR, data extraction, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Identification at 84.4% (#21 of 52 at low effort). You can test it on your own image in the demo above.
Yes. its transcriptions match the ground truth 79.1% on average (#48 of 52 at low effort) on Vision Evals OCR. Pulling specific fields out of documents (data extraction) scores 82.7%.
Not its strength. On Vision Evals, Qwen3.5 9b scores 45.8% mAP@50 on object detection (#32 of 52 at low effort) and 54.1% judge-graded accuracy on object counting. For production counting or precise localization, pairing it with a specialized detector like RF-DETR or your own trained model in a Roboflow Workflow is usually more reliable: detect the objects, then count the detections.
On our benchmark's task mix, Qwen3.5 9b averages $0.0008 per sample at $0.10 per 1M input and $0.15 per 1M output tokens (#21 of 52 on cost), with an average speed of 31.3s per sample across the benchmark. Actual cost depends on your images and prompts.
On the overall Vision Evals ranking, Qwen3.5 9b sits #33 of 52 at 66.1%, just behind GLM 5.3 Flash (66.3%) and just ahead of Gemini 2.5 Pro (66%). See the full side-by-side: Qwen3.5 9b vs GLM 5.3 Flash.