Qwen3.5 9b vs SAM 3
Qwen3.5 9b: 56.0% average across four Vision Evals tasks (#44 of 65 models). We have not run SAM 3 through Roboflow's Vision Evals, so there is no head-to-head score.
Compare Qwen3.5 9b vs SAM 3 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Qwen3.5 9b vs SAM 3 Comparison Table
Evals updated October 26, 2026Pricing updated November 6, 2026
| Property | Qwen3.5 9b | SAM 3 |
|---|---|---|
| Organization | Qwen | Meta |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Nov 2025 |
| Context Window | 262K | — |
| Parameters | 9B | Unknown |
| License | Apache 2.0 | Custom |
| Commercial use | free for commercial use | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.170 | No published price |
| Output $/1M | $0.250 | No published price |
| Vision Tasks | ||
| Object Detection | Supported | Demo |
| Captioning | Demo | Not listed |
| Chart Question Answering | Supported | Not listed |
| Classification | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Instance Segmentation | Not listed | Supported |
| Multi-Label Classification | Supported | Not listed |
| OCR | Demo | Not listed |
| Open Vocabulary Object Detection | Not listed | Supported |
| Promptable Concept Segmentation | Not listed | Demo |
| Video Object Tracking | Not listed | Supported |
| Vision Language | Supported | Not listed |
| Visual Question Answering | Demo | Not listed |
| Zero Shot Segmentation | Not listed | Supported |
| Model Features | ||
| LLMs with Vision Capabilities | Supported | Not listed |
| Zero-shot Detection | Not listed | Supported |
Both support 2 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 56.0%* | Not evaluated |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0021 | – |
| Avg speed / sample | 41.36s | – |
| By task | ||
| Object Detection | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 | – |
| Counting | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 | – |
| Identification | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | – |
| OCR | – | – |
| by category | ||
| Reasoning | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 | – |
Qwen3.5 9b vs SAM 3: 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.
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.
Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.
Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.
