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Grounded SAM vs Qwen3.5 27B

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Grounded SAM vs Qwen3.5 27B: Overview

Grounded SAM

Grounded SAM is an open-vocabulary image segmentation model developed by IDEA Research, released in January 2024 under the Apache 2.0 license. It combines Grounding DINO, a zero-shot open-vocabulary object detector, with the Segment Anything Model to produce precise segmentation masks for objects identified through free-form text prompts. The two models are used sequentially: Grounding DINO localizes objects from a text query, and SAM generates the corresponding segmentation masks.

Grounded SAM enables zero-shot instance segmentation without task-specific training data, making it applicable to domains where labeled segmentation data is scarce. It supports arbitrary text queries and can segment objects not represented in standard training sets. The model is commonly used in automated labeling pipelines, robotic perception, and domain-specific vision applications requiring open-vocabulary segmentation.

Qwen3.5 27B

Qwen3.5-27B is a multimodal dense hybrid model developed by Alibaba Cloud’s Qwen team and released in February 2026 as a high-precision entry in the Qwen3.5 "Medium" series. Unlike its Mixture-of-Experts (MoE) siblings, the 27B model utilizes a dense architecture combining Gated Delta Networks with a feed-forward structure, activating its full parameter suite for every inference to maximize reliability. This design provides the highest instruction-following and coding accuracy in its class, with a notable IFEval score of 95.0. The model features a native 262K-token context window, extensible to 1M tokens via YaRN (RoPE scaling), and is released under the Apache-2.0 license.

Optimized for agentic workflows, Qwen3.5-27B employs an early-fusion architecture that treats visual and textual data as a unified stream for deep cross-modal reasoning. This unified approach allows the model to excel in technical analysis and software engineering, matching GPT-5-mini with a 72.4% score on SWE-bench Verified. While the larger MoE variants in the family lead in raw knowledge benchmarks, the 27B model offers a stable and high-density alternative for structured data extraction and spatial perception, contributing to the Qwen3.5 family’s generational leap in OCR accuracy over the previous Qwen3-VL series.

Grounded SAM vs Qwen3.5 27B Comparison Table

PropertyGrounded SAMQwen3.5 27B
OrganizationIDEA ResearchQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJan 2024Feb 2026
Context Window262K
Parameters27B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.195
Output $/1M$1.56
Vision Tasks
Vision Language
CaptioningDemo
Object Detection
OCRDemo
Visual Question AnsweringDemo
Zero Shot Segmentation
Model Features
Multimodal Vision
LLMs with Vision Capabilities
Zero-shot Detection
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
71.64%
Avg Response Time1.98s
Median input tokensincl. image tokens1.2K
Median output tokens7
Est. cost / taskon this benchmark$0.0002
Defect Detection
80%(12/15)
Document Understanding
77.8%(7/9)
Object Counting
40%(4/10)
Object Understanding
78.6%(11/14)
Spatial Understanding
73.7%(14/19)
OCR
Overall Score
85.59%
Avg Response Time8.51s
Median input tokensincl. image tokens126
Median output tokens107
Est. cost / taskon this benchmark$0.0002
Focused Scene OCR
84.8%(84/99)
Handwritten Math
100%(10/10)
License Plate Recognition
93.3%(28/30)
Text Recognition
80%(24/30)
VQA & Extraction
83.3%(50/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology