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Qwen3 VL 235B A22B Instruct vs SAM 3

Compare Qwen3 VL 235B A22B Instruct and SAM 3 side-by-side.

Compare Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct vs SAM 3 Comparison Table

Evals updated August 6, 2026Pricing updated August 9, 2026

PropertyQwen3 VL 235B A22B InstructSAM 3
OrganizationQwenMeta
Categoryopenopen
Modalitymultimodalmultimodal
Release DateSep 2025Nov 2025
Context Window256K
Parameters235B
LicenseApache 2.0Custom
Pricing per 1M tokens
Input $/1M$0.210
Output $/1M$1.90
Vision Tasks
Object DetectionDemo
CaptioningDemo
Chart Question Answering
Classification
Document Question Answering
Image Tagging
Instance Segmentation
Multi-Label Classification
OCRDemo
Open Vocabulary Object Detection
Promptable Concept SegmentationDemo
Video Object Tracking
Vision Language
Visual Question AnsweringDemo
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
65.8%
Not evaluated
Avg cost / sample$0.0007
Avg speed / sample9.17s
By task
Object Detection
52.2%
$0.0014
Counting
47.3%
$0.0002
Identification
90.6%
$0.0002
OCR
88.1%
$0.0010
Data Extraction
86.6%
$0.0002
Reasoning (low)
29.8%
$0.0002
Reasoning (high)
33.8%
$0.0002

Qwen3 VL 235B A22B Instruct vs SAM 3: Overview

Qwen3 VL 235B A22B Instruct

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.

SAM 3

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.