MS COCO (Common Objects in Context) ranks higher at 61/100 vs Meshy at 55/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Meshy | MS COCO (Common Objects in Context) |
|---|---|---|
| Type | Product | Dataset |
| UnfragileRank | 55/100 | 61/100 |
| Adoption | 1 | 1 |
| Quality |
| 1 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | $16/mo | — |
| Capabilities | 14 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Converts a single 2D image (PNG, JPG, JPEG, WebP; max 25MB) into a fully textured 3D mesh with PBR materials in approximately 1 minute. The system processes the image server-side using proprietary Meshy generative models (v4, v5, or v6 selectable), inferring 3D geometry, topology, and physically-based rendering textures (Diffuse, Roughness, Metallic, Normal maps) from 2D visual information. Output is available in multiple formats (GLB, OBJ, FBX, USDZ, STL, BLEND) with configurable polygon density up to ~600K faces.
Unique: Generates fully textured 3D meshes with PBR materials in a single pass from 2D images using proprietary diffusion-based or neural rendering models (architecture unspecified), eliminating the need for separate texture baking or material assignment steps that traditional 3D pipelines require. Selectable model versions (v4/v5/v6) allow users to choose between quality/speed trade-offs without leaving the platform.
vs alternatives: Faster than manual 3D modeling (hours to minutes) and includes PBR textures automatically, whereas competitors like Nomad Sculpt or Blender require separate texture baking; simpler than Kaedim or Loom3D because it requires no multi-view image capture or manual pose annotation.
Processes up to 10 images in a single batch operation, generating a separate 3D model for each input image sequentially or in parallel depending on tier-level concurrent task limits. The system queues each image through the single-image-to-3D pipeline and returns all completed models together, with progress tracking for each asset. Batch processing respects tier-based concurrency limits: Free (1 concurrent task), Pro (10 concurrent), Studio (20 concurrent).
Unique: Implements tier-based concurrency control (1/10/20 concurrent tasks) that allows Pro and Studio users to parallelize image-to-3D generation across multiple images simultaneously, reducing total wall-clock time for large batches. Free tier users are serialized to 1 concurrent task, creating a hard bottleneck that incentivizes upgrade.
vs alternatives: Supports up to 10 images per batch with tier-based parallelization, whereas most competitors (Kaedim, Loom3D) require individual submissions; however, the 10-image limit is smaller than enterprise solutions like Unreal Metahuman or custom pipelines that can handle unlimited batch sizes.
Integrates with the Model Context Protocol (MCP) standard, enabling AI agents and LLM-based applications to invoke Meshy's 3D generation capabilities as tools within agentic workflows. MCP is a protocol for standardizing tool/resource access in AI systems, allowing Claude, other LLMs, or custom agents to call Meshy functions (generate 3D from image, generate 3D from text, apply textures, etc.) as part of multi-step reasoning and planning tasks. Specific MCP tool definitions, parameters, and integration examples are undocumented.
Unique: Implements MCP (Model Context Protocol) integration, allowing AI agents and LLMs to invoke 3D generation as a tool within multi-step reasoning workflows. This enables conversational or agentic interfaces where users describe objects and the system generates 3D models as part of a larger creative or design process.
vs alternatives: Enables AI agents to generate 3D assets, which most competitors do not support; however, complete lack of MCP documentation makes it impossible to assess integration quality or feature completeness compared to other MCP-integrated tools.
Implements a credit-based billing system with tier-dependent concurrency limits and queue prioritization to manage resource allocation and monetization. Free tier allows 1 concurrent task with low queue priority; Pro tier allows 10 concurrent tasks with high priority; Studio tier allows 20 concurrent tasks with higher priority. Concurrent task limits directly impact wall-clock time for batch operations: users on Free tier must wait for each task to complete before starting the next, while Pro/Studio users can parallelize up to 10/20 tasks simultaneously.
Unique: Implements tier-based concurrency control (1/10/20 concurrent tasks) that directly impacts batch processing speed, creating a clear performance incentive for tier upgrade. Free tier users are serialized to 1 concurrent task, making batch operations 10x slower than Pro users, which is a hard constraint that drives monetization.
vs alternatives: Transparent tier-based concurrency model is clearer than competitors' opaque queue systems; however, the 1-task Free tier limit is more restrictive than some competitors (e.g., Replicate allows higher concurrency on free tier), creating stronger upgrade pressure.
Implements a credit-based billing system where each generation, texturing, or remeshing operation consumes a fixed number of credits. Monthly credit allocation is tier-dependent: Free (100 credits/month), Pro (1,000 credits/month), Studio (4,000 credits/month). Exact credit costs per operation are not documented, but stated allocations imply ~10 credits per asset (100 credits = ~10 assets for Free, 1,000 = ~100 for Pro, 4,000 = ~400 for Studio). Unused credits do not roll over; allocation resets monthly.
Unique: Implements a simple credit-based billing model with tier-dependent monthly allocations, eliminating per-operation pricing complexity. Credits are consumed uniformly across all operations (generation, texturing, remeshing), simplifying cost prediction. However, exact credit costs are not documented, and pricing display errors obscure actual tier costs.
vs alternatives: Simpler than pay-as-you-go pricing (Replicate, Hugging Face) because users know their monthly budget upfront; however, less flexible than usage-based pricing for variable workloads, and pricing opacity (display errors, undocumented credit costs) makes cost comparison difficult.
Manages intellectual property and usage rights through tier-dependent licensing: Free tier assets are licensed under CC BY 4.0 (non-commercial use only, attribution required), while Pro and Studio tier assets are licensed under a private commercial license (commercial use permitted, no attribution required). License type is automatically assigned based on tier at generation time. All generated assets are owned by the user; Meshy retains no rights to generated content.
Unique: Implements tier-based licensing that automatically assigns CC BY 4.0 (non-commercial) to Free tier and private commercial license to Pro/Studio, creating a clear monetization boundary. Users retain full ownership of generated assets; Meshy claims no rights. This is a common SaaS pattern but the CC BY 4.0 restriction on Free tier is a strong incentive for commercial users to upgrade.
vs alternatives: Clearer than competitors' licensing (many competitors do not explicitly document IP ownership); however, the CC BY 4.0 restriction on Free tier is more restrictive than some competitors (e.g., Replicate allows commercial use on free tier with usage limits), creating stronger upgrade pressure for commercial users.
Automatically generates multiple synthetic viewing angles from a single input image before or during 3D mesh generation, improving geometric inference by providing the model with implicit multi-view context. The system uses AI to synthesize additional viewpoints (front, side, back, top, bottom, etc.) from the single 2D input, then feeds these synthetic views into the 3D generation pipeline to improve mesh quality and consistency. This preprocessing step is optional and can be toggled per-generation.
Unique: Uses AI-based view synthesis to generate synthetic multi-view context from a single image, improving 3D inference without requiring the user to capture multiple reference photos. This is a preprocessing step that feeds into the core 3D generation model, distinguishing it from post-hoc multi-view reconstruction methods.
vs alternatives: Eliminates the need for users to capture multiple reference images (as required by Loom3D or Kaedim), making it faster for single-image inputs; however, the synthetic views are not user-controllable or inspectable, unlike manual multi-view capture which gives explicit control over viewpoints.
Generates 3D models directly from natural language text prompts describing the desired object, style, and properties. The system processes text input through a proprietary language-to-3D generative model (architecture and training data unspecified) and outputs a fully textured 3D mesh with PBR materials. This capability bypasses the need for reference images entirely, enabling creative generation from pure text description.
Unique: Implements a text-to-3D pipeline that generates 3D geometry and textures directly from natural language descriptions, using an undocumented proprietary model. This bypasses image-based inference entirely, enabling generation of objects without reference photography or existing visual references.
vs alternatives: Faster than manual 3D modeling from text descriptions and requires no reference images, unlike image-to-3D competitors; however, the approach is less documented and likely less stable than image-to-3D, and no comparison data is provided on quality or consistency vs. text-to-3D alternatives like DreamFusion or Point-E.
+6 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 Meshy at 55/100.
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