MS COCO (Common Objects in Context) ranks higher at 61/100 vs Qwen: Qwen3.5 Plus 2026-02-15 at 23/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Qwen: Qwen3.5 Plus 2026-02-15 | MS COCO (Common Objects in Context) |
|---|---|---|
| Type | Model | Dataset |
| UnfragileRank | 23/100 | 61/100 |
| Adoption |
| 0 |
| 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Starting Price | $2.60e-7 per prompt token | — |
| Capabilities | 9 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Processes images, text, and video inputs simultaneously using a hybrid architecture combining linear attention mechanisms with sparse mixture-of-experts routing. Linear attention reduces computational complexity from O(n²) to O(n) while sparse MoE selectively activates expert parameters based on input type and content, enabling efficient processing of high-resolution visual content alongside text without full model activation.
Unique: Hybrid linear attention + sparse MoE architecture reduces inference latency compared to dense transformer vision models while maintaining multimodal reasoning capability. Linear attention mechanism specifically optimized for visual token sequences, avoiding quadratic scaling that limits dense models on high-resolution images.
vs alternatives: Achieves faster inference on image-heavy workloads than GPT-4V or Claude 3.5 Vision due to linear attention complexity, while maintaining competitive accuracy through selective expert activation in MoE layers.
Processes video inputs by decomposing them into frame sequences and applying vision-language understanding across temporal boundaries. The sparse MoE architecture selectively activates video-specialized experts when video tokens are detected, enabling efficient analysis of motion, scene changes, and temporal relationships without processing every frame through the full model capacity.
Unique: Sparse MoE routing specifically activates video-expert parameters when processing frame sequences, avoiding full model computation for each frame while maintaining temporal coherence through attention across frame tokens. Linear attention enables efficient processing of long frame sequences without quadratic memory overhead.
vs alternatives: More efficient than dense video models like GPT-4V for frame-heavy analysis due to selective expert activation, while maintaining temporal reasoning capabilities comparable to specialized video understanding models.
Implements sparse mixture-of-experts routing that dynamically selects which expert parameters activate based on input content type and complexity, reducing per-token computation from full model capacity to a fraction of parameters. The routing mechanism uses learned gating functions to assign tokens to specialized experts (vision, language, multimodal), enabling high-throughput inference without loading all parameters for every request.
Unique: Sparse MoE architecture with learned gating functions routes tokens to specialized experts rather than activating full model capacity, reducing per-token FLOPs while maintaining model quality. Routing decisions are input-aware, allowing different expert combinations for text-only vs. image-heavy vs. video inputs.
vs alternatives: Achieves lower inference cost and latency than dense models like GPT-4 or Claude 3.5 for mixed-modality workloads by selectively activating only necessary expert capacity, while maintaining competitive accuracy through specialized expert training.
Processes high-resolution images using linear attention mechanisms that scale O(n) instead of O(n²), enabling efficient encoding of dense visual tokens without memory explosion. The architecture decomposes image patches into token sequences and applies linear attention transformations, allowing processing of images with significantly more pixels than quadratic-attention models while maintaining spatial reasoning capability.
Unique: Linear attention mechanism reduces image encoding complexity from O(n²) to O(n) where n is the number of image patches, enabling processing of higher-resolution images than quadratic-attention models without memory explosion. Patch-based tokenization combined with linear kernels maintains spatial coherence while scaling efficiently.
vs alternatives: Processes higher-resolution images more efficiently than GPT-4V or Claude 3.5 Vision due to linear attention scaling, enabling detail-preserving analysis of documents and technical diagrams without resolution downsampling penalties.
Generates and understands text across multiple languages using a shared token vocabulary and language-agnostic attention mechanisms. The model applies the same linear attention and sparse MoE routing to all languages, with language-specific expert routing enabling efficient multilingual inference without separate model instances per language.
Unique: Shared token vocabulary and language-agnostic linear attention enable efficient multilingual inference with language-specific expert routing, avoiding separate model instances per language while maintaining language-specific reasoning through MoE expert specialization.
vs alternatives: More efficient than maintaining separate language models or using dense multilingual models, while providing comparable quality to specialized translation models through expert-based language specialization.
Extracts structured information (JSON, tables, key-value pairs) from unstructured text and images using prompt-based schema specification and constrained decoding. The model applies vision-language understanding to identify relevant content regions, then generates structured output conforming to specified schemas, with optional validation against provided JSON schemas.
Unique: Combines vision-language understanding with prompt-based schema specification to extract structured data from both text and images, using sparse MoE routing to activate extraction-specialized experts when processing structured output generation tasks.
vs alternatives: More flexible than rule-based extraction tools (regex, XPath) for handling variable document layouts, while maintaining better accuracy than generic LLMs through schema-aware generation and expert specialization.
Analyzes and generates code across multiple programming languages using vision-language understanding to parse code syntax from images and text, combined with language-specific expert routing in the MoE layer. Supports code completion, explanation, and refactoring by maintaining semantic understanding of code structure and applying language-specific reasoning patterns.
Unique: Combines vision-language understanding to parse code from images and diagrams with language-specific expert routing, enabling code analysis and generation from both textual and visual representations while maintaining semantic correctness through specialized experts.
vs alternatives: Handles code-in-images and technical diagrams better than text-only models like GitHub Copilot, while maintaining competitive code generation quality through language-specific expert activation in the MoE architecture.
Performs multi-step reasoning and problem decomposition using chain-of-thought patterns and planning-aware expert routing. The sparse MoE architecture activates reasoning-specialized experts when processing complex queries, enabling step-by-step problem solving with explicit intermediate reasoning steps that improve accuracy on tasks requiring logical inference.
Unique: Sparse MoE routing activates reasoning-specialized experts when processing complex queries, enabling efficient multi-step reasoning without full model computation. Linear attention mechanisms allow maintaining long reasoning chains without quadratic memory overhead.
vs alternatives: Provides more efficient reasoning than dense models through expert specialization, while maintaining reasoning quality comparable to specialized reasoning models like o1 through planning-aware expert activation.
+1 more capabilities
Provides 2.5 million manually-annotated object instances across 330,000 images with dual segmentation encoding: polygon coordinates for precise boundary definition and RLE (run-length encoding) for efficient storage and computation. Each instance includes bounding box coordinates in [x, y, width, height] format, category label from 80 object classes, and instance-level unique identifiers enabling per-object tracking and evaluation. Annotations are structured in JSON format with hierarchical organization linking images to annotations to categories, supporting both dense object scenes and sparse single-object images.
Unique: Dual segmentation encoding (polygon + RLE) in single dataset enables both precise boundary analysis and efficient computational workflows; 2.5M instances across 330K images provides scale unmatched by contemporaneous datasets (ImageNet had ~1.2M images, PASCAL VOC had ~11K images)
vs alternatives: Larger and more densely annotated than PASCAL VOC (11K images, ~6 objects/image) and more task-diverse than ImageNet (classification-only); RLE encoding enables 10-100x faster mask loading than polygon-only formats
Provides keypoint annotations for all people in images using a standardized 17-joint skeleton model (head, shoulders, elbows, wrists, hips, knees, ankles) with (x, y, visibility) tuples per joint. Visibility flag indicates whether keypoint is annotated (1), occluded (0), or outside image bounds (0). Keypoints are linked to parent person instances via instance ID, enabling pose estimation evaluation at both individual and crowd-level scales. Annotations follow COCO Keypoints task specification with consistent coordinate system across all 330K images.
Unique: Standardized 17-joint skeleton with explicit visibility flags enables robust evaluation of pose estimation under occlusion; linked to instance segmentation masks allows joint-level accuracy analysis within person bounding boxes
MS COCO (Common Objects in Context) scores higher at 61/100 vs Qwen: Qwen3.5 Plus 2026-02-15 at 23/100. MS COCO (Common Objects in Context) also has a free tier, making it more accessible.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
vs alternatives: More comprehensive than OpenPose dataset (no visibility flags) and larger scale than Human3.6M (3.6M frames vs 330K images); visibility annotations enable explicit occlusion handling unlike MPII (which lacks visibility metadata)
COCO ecosystem includes community-created extensions (COCO-Stuff, COCO DensePose, COCO Panoptic) that extend base dataset with additional annotations while maintaining compatibility with COCO API and evaluation infrastructure. Extensions follow COCO format and evaluation standards, enabling seamless integration into existing pipelines. Community contributions are vetted and published as official COCO variants, ensuring quality and standardization. Variant creation process is documented, enabling researchers to create custom extensions.
Unique: Standardized extension process enables community contributions while maintaining compatibility; official variants (Stuff, DensePose, Panoptic) are vetted and published, ensuring quality and discoverability
vs alternatives: More extensible than fixed datasets; community variants enable specialized use cases without forking; standardized format prevents fragmentation unlike ad-hoc dataset variants
Provides 1.65 million image-caption pairs (5 captions × 330K images) with natural language descriptions written by human annotators. Each caption is a free-form English sentence describing objects, actions, and scene context without enforced length limits or structured templates. Captions are stored in JSON format linked to image IDs, enabling training of vision-language models for image captioning, visual question answering, and cross-modal retrieval. Multiple captions per image capture linguistic diversity and alternative descriptions of the same visual content.
Unique: 5 captions per image (vs 1 in most datasets) captures linguistic diversity and enables robust evaluation of caption generation variability; 1.65M caption-image pairs provide scale for training large vision-language models
vs alternatives: 5x more captions per image than Flickr30K (1 caption/image) enabling better linguistic diversity modeling; larger scale than Visual Genome (108K images) while maintaining natural language quality vs automated alt-text
Extends base 80 object categories with 91 additional 'stuff' categories (background materials, textures, regions like sky, grass, wall) enabling dense semantic segmentation of entire images. Stuff categories are annotated as pixel-level masks without instance boundaries — all sky pixels are labeled 'sky' regardless of continuity. COCO-Stuff combines instance segmentation (80 objects) with semantic segmentation (171 total categories including stuff), stored as single-channel PNG masks where pixel value encodes category ID. Enables panoptic segmentation evaluation combining instance and stuff predictions.
Unique: 171-category taxonomy combining 80 instance objects + 91 stuff categories enables panoptic segmentation in single dataset; pixel-level masks for stuff enable dense scene understanding without instance boundaries
vs alternatives: More comprehensive than ADE20K (150 categories) and larger scale than Cityscapes (5K images); unified instance+stuff annotation enables panoptic evaluation unlike separate semantic/instance datasets
Combines instance segmentation (80 object categories with boundaries) and semantic segmentation (171 stuff categories without boundaries) into single panoptic prediction task. Evaluation uses Panoptic Quality (PQ) metric decomposed into Segmentation Quality (SQ — IoU of matched predictions) and Recognition Quality (RQ — detection rate). Panoptic masks encode both category ID and instance ID, enabling evaluation of both 'what' (category) and 'which' (instance identity) predictions. Standardized evaluation protocol with server-side metric computation ensures consistent benchmarking across submissions.
Unique: Panoptic Quality metric with explicit SQ/RQ decomposition enables fine-grained analysis of segmentation vs recognition errors; unified instance+stuff evaluation in single task forces models to handle both prediction types efficiently
vs alternatives: More comprehensive than separate instance/semantic benchmarks; PQ metric better captures real-world scene understanding than independent metrics; standardized evaluation prevents metric gaming unlike custom evaluation scripts
Provides dense 2D-to-3D correspondence maps for human bodies, mapping each pixel in a person instance to a 3D human body model surface. Annotations include UV coordinates (parameterization of 3D body surface) and body part indices enabling pixel-level body surface understanding. DensePose enables training of models that predict where each image pixel corresponds to on a canonical 3D human body, useful for pose transfer, virtual try-on, and detailed human understanding. Available from 2020 dataset version onwards, extends keypoint annotations with dense surface coverage.
Unique: Dense 2D-to-3D surface correspondence enables pixel-level body understanding beyond skeleton keypoints; UV parameterization allows transfer of appearance and shape across different people and poses
vs alternatives: More detailed than keypoint-only annotations (17 joints vs millions of surface points); enables pose transfer unlike keypoint datasets; larger scale than DensePose-specific datasets
Provides standardized evaluation metrics for each task (Average Precision for detection, IoU for segmentation, OKS for keypoints, BLEU/METEOR/CIDEr for captions, PQ for panoptic) computed server-side on held-out test set. Leaderboard system accepts structured JSON result submissions in COCO format, validates format, computes metrics, and ranks submissions by primary metric. Evaluation infrastructure ensures consistent benchmarking across all submissions and prevents metric gaming through standardized computation. Metrics are task-specific: AP/AP50/AP75 for detection, mIoU for segmentation, OKS for keypoints, CIDEr for captions.
Unique: Server-side metric computation prevents metric gaming and ensures consistency; task-specific metrics (AP, OKS, CIDEr, PQ) are standardized across all submissions enabling fair comparison; public leaderboard provides transparency and reproducibility
vs alternatives: More rigorous than self-reported metrics (prevents cherry-picking); standardized evaluation prevents metric implementation variations unlike custom evaluation scripts; public leaderboard enables community comparison unlike proprietary benchmarks
+3 more capabilities