Qwen3.5 9b vs SAM 3
Compare Qwen3.5 9b and SAM 3 side-by-side.
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.
Models in this comparison
Qwen3.5 9b vs SAM 3 Comparison Table
Evals updated September 22, 2026Pricing updated September 24, 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 | |
| License | Apache 2.0 | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | |
| Output $/1M | $0.150 | |
| Vision Tasks | ||
| Object Detection | Demo | |
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Instance Segmentation | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Open Vocabulary Object Detection | ||
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
| Zero-shot Detection | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | 64.4% | 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 | 84.2% ±0.9, Mean of 3 runs, range 83.0 to 84.9 | – |
| Data Extraction | 78.3% ±2.1, Mean of 3 runs, range 76.3 to 80.4 | – |
| 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. 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.
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.
Frequently Asked Questions
SAM 3 has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Qwen3.5 9b is released under Apache 2.0, while SAM 3 uses Custom. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.
