Qwen3 VL 235B A22B Instruct vs SAM 3
Compare Qwen3 VL 235B A22B Instruct and SAM 3 side-by-side. See how these vision models stack up in Object Detection.
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Qwen3 VL 235B A22B Instruct vs SAM 3 Comparison Table
Evals updated September 22, 2026Pricing updated September 23, 2026
| Property | Qwen3 VL 235B A22B Instruct | SAM 3 |
|---|---|---|
| Organization | Qwen | Meta |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2025 | Nov 2025 |
| Context Window | 256K | — |
| Parameters | 235B | |
| License | Apache 2.0 | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $0.210 | |
| Output $/1M | $1.90 | |
| Vision Tasks | ||
| Object Detection | Demo | Demo |
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| 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, pooled at low effort | ||
| Overall | 65.9% | Not evaluated |
| Avg cost / sample | $0.0007 | – |
| Avg speed / sample | 9.17s | – |
| By task | ||
| Object Detection | 52.1% | – |
| Counting | 47.3% | – |
| Identification | 90.6% | – |
| OCR | 88.1% | – |
| Data Extraction | 87.6% | – |
| Reasoning (low) | 29.8% | – |
| Reasoning (high) | 33.8% | – |
Qwen3 VL 235B A22B Instruct vs SAM 3: Overview
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.
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 VL 235B A22B Instruct 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.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.
