| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 10 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Captures images of printed or handwritten math problems using a smartphone camera and converts them into digital format for processing. Uses optical character recognition (OCR) to interpret mathematical notation, equations, and problem text from photos.
Breaks down mathematical problem solutions into sequential, pedagogically-structured steps that explain the reasoning and methodology behind each operation. Provides intermediate calculations and conceptual explanations for how to progress from problem to solution.
Solves mathematical problems across a comprehensive range of difficulty levels and domains, from basic arithmetic through advanced calculus, including algebra, geometry, trigonometry, and statistics. Adapts solution complexity to the problem's mathematical level.
Allows users to manually type or input mathematical equations and problems when camera recognition fails or for problems that are difficult to photograph. Provides a text-based alternative to image capture for problem entry.
Processes recognized or input math problems and generates complete solutions within seconds, leveraging offline computation capabilities for fast response times. Delivers answers immediately without requiring internet connectivity for most operations.
Generates visual representations of mathematical problems and solutions, including graphs, plots, and geometric diagrams. Helps students understand problems visually and see the relationship between equations and their graphical representations.
Manages tiered access to features through a subscription model, offering free basic functionality with limited explanations and premium tiers ($9.99/month or $59.99/year) that unlock full step-by-step solutions and advanced features.
Maintains a record of previously solved problems within the app, allowing students to review past solutions, track their problem-solving progress, and revisit explanations for similar problem types.
+2 more capabilities
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs Photomath at 51/100. Photomath leads on ecosystem, while Pinecone is stronger on adoption and quality. Pinecone also has a free tier, making it more accessible.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities