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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Past 30 DaysVision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across five vision tasks, and answers are scored against ground truth.
Evals updated October 13, 2026Pricing updated October 19, 2026
Qwen3.5 9b averages 56.0% across the 4 of 5 Vision Evals tasks it has results for, ranking #42 of 63 models overall.
Its weakest relative showing is Object Detection, ranking #48 of 63 at 38.1%.
At $0.0008 per sample it is the 26th cheapest of the 63 benchmarked models, and its average inference time of 41.4s per sample makes it the 61st fastest.
Field medians: Object Detection 54.4%, Counting 63.5%, Identification 84.4%, Reasoning 57.6%.
| Task | Score | Field (0 to 100) | Rank | Cost / sample | Speed |
|---|---|---|---|---|---|
| Object Detection | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 | #48 of 63 | $0.0008 | 32.56s | |
| Counting | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 | #37 of 63 | $0.0011 | 60.48s | |
| Identification | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | #38 of 63 | $0.0004 | 18.37s | |
| Reasoning | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 | #42 of 63 | $0.0012 | 66.01s |
Overall benchmark score against estimated cost per sample, on a log scale. Upper-left is the sweet spot: high quality at low cost.
62 models on the current benchmark · scores and efficiency pooled across all five tasks at low effort · Qwen3.5 9B highlighted
Qwen3.5 9b scores are the mean of 3 runs per task at low effort; this model has no separate high-effort tier because its chat template only switches thinking on or off · 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.
Rows are ordered by overall score. Every score is the mean of three runs per task under the benchmark protocol (a row marked single run has one), and the gap between quantizations is usually inside the run-to-run range, so read the order as a guide rather than a verdict. Self-hosted rows run with thinking on, 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.
Qwen3.5 9b costs $0.100 per 1M input tokens and $0.150 per 1M output tokens.
Pricing updated Oct 19, 2026
Other models worth comparing for similar use cases.
Qwen3.5 9b is released under the Apache 2.0 license, so it's commercially usable out of the box: you can ship it in closed-source, commercial products, including self-hosted deployments, without paying a license fee or open-sourcing your code. The Qwen3.5 9b license is permissive, 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, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Counting at 56.8% (#37 of 63 at low effort). You can test it on your own image in the demo above.
Not its strength. On Vision Evals, Qwen3.5 9b scores 38.1% mAP@50 on object detection (#48 of 63 at low effort) and 56.8% 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 (#26 of 63 on cost), with an average speed of 41.4s per sample across the benchmark. Actual cost depends on your images and prompts.
On the overall Vision Evals ranking, Qwen3.5 9b sits #42 of 63 at 56%, just behind Mistral Large 4 (56.8%) and just ahead of Kimi K3 (56%). See the full side-by-side: Qwen3.5 9b vs Mistral Large 4.