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Qwen3.7 Flash vs SAM 3

Compare Qwen3.7 Flash and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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QwenQwen3.7 Flash
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MetaSAM 3
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Models in this comparison

Meta

Qwen3.7 Flash vs SAM 3 Comparison Table

Evals updated August 6, 2026Pricing updated August 10, 2026

PropertyQwen3.7 FlashSAM 3
OrganizationQwenMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJul 2026Nov 2025
Context Window1.0M
Parameters
LicenseProprietaryCustom
Pricing per 1M tokens
Input $/1M$0.030
Output $/1M$0.130
Vision Tasks
Object DetectionDemoDemo
CaptioningDemo
Chart Question Answering
ClassificationDemo
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
61.7%
Not evaluated
Avg cost / sample$0.0001
Avg speed / sample6.32s
By task
Object Detection
42.8%
$0.0001
Counting
46.0%
<$0.0001
Identification
84.4%
<$0.0001
OCR
84.1%
$0.0001
Data Extraction
78.3%
<$0.0001
Reasoning (low)
34.4%
<$0.0001
Reasoning (high)
60.9%
$0.0005

Qwen3.7 Flash vs SAM 3: Overview

Qwen3.7 Flash

Qwen3.7 Flash is the low-latency, cost-oriented tier of Alibaba's Qwen3.7 series, a vision-language reasoning model that accepts interleaved text and image input and returns text. It is built as a hybrid thinking model: like the rest of the Qwen3.7, Qwen3.6, and Qwen3.5 families served through Alibaba Cloud Model Studio, it can either emit an explicit reasoning trace before answering or respond directly, with thinking behavior controlled by an enable_thinking switch that defaults to on for the Qwen3.7 generation. The model exposes a context window of roughly one million tokens and a maximum generation length of 65,536 tokens, which allows long multi-image sequences, long documents, and extended agent trajectories to be held in a single request.

Functionally, Qwen3.7 Flash targets multimodal agent workloads rather than pure chat. Reported strengths include object recognition, spatial understanding, and perception of real-world scenes, alongside visual coding, search, and computer-use style interaction where the model reads screen content and reasons over interface state. Weights are not published; the model is a proprietary endpoint positioned below Qwen3.7 Plus and Qwen3.7 Max in the same series, and it supports function calling and tool use for agentic pipelines.

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.

Qwen3.7 Flash is released under Proprietary, 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.