| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Automatically extracts structured data from arbitrary web pages without requiring CSS selectors, regex patterns, or manual rules. Uses computer vision to identify and classify page elements (text blocks, tables, images, metadata) and NLP to map them to domain-specific schemas (articles, products, organizations, events, discussions). Processes one page per API call, consuming 1 credit per extraction or 2 credits when routed through datacenter proxies for geo-spoofing or IP rotation.
Unique: Uses computer vision (image analysis) + NLP jointly to identify page structure without CSS selectors or regex, enabling extraction from pages with dynamic or non-standard HTML. Automatically detects content type (article vs. product vs. organization) and applies type-specific schema extraction in a single API call.
vs alternatives: Faster to deploy than Selenium/Puppeteer + regex pipelines because it requires no rule maintenance; more flexible than CSS-selector-based tools (Scrapy, Beautiful Soup) when page structure varies across domains.
Crawlbot spiders websites across 50 to 50,000+ URLs, automatically following links and discovering pages within a domain or URL pattern. Applies the Extract API to each crawled page, returning structured data for all discovered pages. Crawling itself consumes zero credits; only the extraction of crawled pages consumes credits (1 per page). Supports configurable crawl depth, URL filtering, and crawl scheduling via the dashboard or API.
Unique: Decouples crawling (free) from extraction (paid), allowing users to discover site structure without cost and then selectively extract high-value pages. Combines web spidering with rule-less extraction, eliminating the need to maintain separate crawl rules and extraction rules.
vs alternatives: More cost-efficient than Scrapy + regex pipelines for large sites because crawling is free and extraction is pay-per-page; more maintainable than custom crawlers because extraction rules adapt automatically to page structure changes.
Knowledge Graph indexes entities (organizations, articles, products, discussions, events) across multiple languages and regions. Article/News index (1.6B+ records) includes content from global news sources in multiple languages. Organization index (246M+ records) includes companies from multiple regions with localized data (e.g., revenue in local currency, regional employee counts). Product index (3M+ records) includes products from global e-commerce sites. No explicit documentation of supported languages or regions, but scale suggests broad coverage.
Unique: Knowledge Graph indexes 1.6B+ articles in multiple languages and 246M+ organizations across regions, enabling global entity search without requiring separate language-specific APIs or manual translation.
vs alternatives: More comprehensive than single-language APIs (e.g., English-only news APIs) because it covers global content; more cost-effective than building separate language-specific crawlers because data is pre-indexed.
Natural Language API extracts named entities (people, organizations, locations, products), relationships between entities (e.g., 'person works at organization'), and topic-level sentiment from raw text documents (1–10,000 characters). Uses NLP models to identify entity types, resolve entity references, and infer relationships without requiring labeled training data or custom entity definitions. Each document consumes 1 credit regardless of length (within the 1–10k character range).
Unique: Combines entity extraction, relationship inference, and sentiment analysis in a single API call without requiring separate models or training data. Automatically links extracted entities to Diffbot's 10B+ entity Knowledge Graph for entity resolution and enrichment.
vs alternatives: Simpler to integrate than spaCy + custom relationship extraction models because it requires no training data or model fine-tuning; more comprehensive than regex-based entity extraction because it infers relationships and resolves entity references.
Knowledge Graph API provides query access to Diffbot's pre-indexed database of 10B+ entities across six types: Organizations (246M+ records with 50+ fields), Articles/News (1.6B+ records), Products (3M+ pre-crawled retail products), Discussions (forum/review data with entity matching), Events (23k+ normalized records), and People (scale unknown). Queries use Diffbot Query Language (DQL), a custom SQL-like syntax. Each entity record export consumes 25 credits. Supports filtering, sorting, and aggregation across entity types.
Unique: Pre-indexed 10B+ entity database with cross-entity relationships (e.g., people linked to organizations, organizations linked to news articles and funding events) enables multi-hop queries without requiring external knowledge base construction. DQL query language provides SQL-like filtering and aggregation without requiring REST API pagination loops.
vs alternatives: More comprehensive than single-source APIs (e.g., LinkedIn API for people, Crunchbase for companies) because it integrates data across news, products, discussions, and events; cheaper than building custom web crawlers to index equivalent data, though per-entity export cost is high for bulk operations.
Enhance API enriches existing person or organization records by querying the Knowledge Graph and appending additional fields (revenue, locations, employees, funding, executives for organizations; employment history, education, social profiles for people). Input is a person name/email or organization name/domain; output is enriched record with 50+ fields for organizations or equivalent for people. Each enrichment consumes 1 credit (same as Natural Language API). Integrations available via Excel, Google Sheets, and Zapier for non-technical users.
Unique: Provides low-code enrichment via Excel/Sheets/Zapier integrations, enabling non-technical users to enrich datasets without API integration. Leverages pre-indexed Knowledge Graph to avoid real-time web scraping, providing faster enrichment with consistent data quality.
vs alternatives: Faster and cheaper than building custom web scrapers for company intelligence; more comprehensive than single-source APIs (e.g., Clearbit, Hunter) because it aggregates data across news, funding, products, and discussions; easier to integrate for non-technical users via Sheets/Excel.
Diffbot uses a credit-based billing model where each API operation consumes a fixed number of credits: Extract (1 credit), Extract with proxy (2 credits), Natural Language (1 credit), Knowledge Graph export (25 credits), Enhance (1 credit). Monthly plans (Free, Startup, Plus, Enterprise) provide credit allotments at different per-credit rates ($0.001–$0.0009). Overage charges apply at the plan's per-credit rate. Free tier (10,000 credits/month, 5 calls/min) is perpetual with no trial expiration. No long-term contracts required; monthly billing.
Unique: Credit-based model decouples API operations from pricing, allowing different operations (Extract, Natural Language, Knowledge Graph export) to have different credit costs. Perpetual free tier with no trial expiration or credit card requirement lowers barrier to entry for small projects.
vs alternatives: More transparent than per-request pricing because credit costs are fixed and documented; more flexible than subscription-only models because overage charges allow usage to scale beyond monthly allotment without contract renegotiation.
Diffbot provides native integrations with Microsoft Excel and Google Sheets, allowing non-technical users to enrich datasets without API integration. Excel integration includes a visual query editor for Knowledge Graph searches and data enrichment. Google Sheets integration supports custom Diffbot Query Language (DQL) formulas for entity lookups and enrichment. Zapier integration enables trigger-based enrichment workflows (e.g., enrich new Salesforce leads with company data). All integrations consume credits at the same rate as direct API calls.
Unique: Brings Knowledge Graph enrichment to non-technical users via familiar tools (Excel, Sheets) without requiring API integration or custom code. Visual query editor in Excel abstracts DQL syntax, lowering barrier to entry for business users.
vs alternatives: More accessible than direct API integration for non-technical users; faster to deploy than building custom Python/Node.js scripts; integrates with existing Zapier workflows for teams already using no-code automation.
+3 more capabilities
GPT-4o processes text, images, and audio through a single transformer architecture with shared token representations, eliminating separate modality encoders. Images are tokenized into visual patches and embedded into the same vector space as text tokens, enabling seamless cross-modal reasoning without explicit fusion layers. Audio is converted to mel-spectrogram tokens and processed identically to text, allowing the model to reason about speech content, speaker characteristics, and emotional tone in a single forward pass.
Unique: Single unified transformer processes all modalities through shared token space rather than separate encoders + fusion layers; eliminates modality-specific bottlenecks and enables emergent cross-modal reasoning patterns not possible with bolted-on vision/audio modules
vs alternatives: Faster and more coherent multimodal reasoning than Claude 3.5 Sonnet or Gemini 2.0 because unified architecture avoids cross-encoder latency and modality mismatch artifacts
GPT-4o implements a 128,000-token context window using optimized attention patterns (likely sparse or grouped-query attention variants) that reduce memory complexity from O(n²) to near-linear scaling. This enables processing of entire codebases, long documents, or multi-turn conversations without truncation. The model maintains coherence across the full context through learned positional embeddings that generalize beyond training sequence lengths.
Unique: Achieves 128K context with sub-linear attention complexity through architectural optimizations (likely grouped-query attention or sparse patterns) rather than naive quadratic attention, enabling practical long-context inference without prohibitive memory costs
vs alternatives: Longer context window than GPT-4 Turbo (128K vs 128K, but with faster inference) and more efficient than Anthropic Claude 3.5 Sonnet (200K context but slower) for most production latency requirements
GPT-4o scores higher at 84/100 vs Diffbot at 56/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
GPT-4o includes built-in safety mechanisms that filter harmful content, refuse unsafe requests, and provide explanations for refusals. The model is trained to decline requests for illegal activities, violence, abuse, and other harmful content. Safety filtering operates at inference time without requiring external moderation APIs. Applications can configure safety levels or override defaults for specific use cases.
Unique: Safety filtering is integrated into the model's training and inference, not a post-hoc filter; the model learns to refuse harmful requests during pretraining, resulting in more natural refusals than external moderation systems
vs alternatives: More integrated safety than external moderation APIs (which add latency and may miss context-dependent harms) because safety reasoning is part of the model's core capabilities
GPT-4o supports batch processing through OpenAI's Batch API, where multiple requests are submitted together and processed asynchronously at lower cost (50% discount). Batches are processed in the background and results are retrieved via polling or webhooks. Ideal for non-time-sensitive workloads like data processing, content generation, and analysis at scale.
Unique: Batch API is a first-class API tier with 50% cost discount, not a workaround; enables cost-effective processing of large-scale workloads by trading latency for savings
vs alternatives: More cost-effective than real-time API for bulk processing because 50% discount applies to all batch requests; better than self-hosting because no infrastructure management required
GPT-4o can analyze screenshots of code, whiteboards, and diagrams to understand intent and generate corresponding code. The model extracts code from images, understands handwritten pseudocode, and generates implementation from visual designs. Enables workflows where developers can sketch ideas visually and have them converted to working code.
Unique: Vision-based code understanding is native to the unified architecture, enabling the model to reason about visual design intent and generate code directly from images without separate vision-to-text conversion
vs alternatives: More integrated than separate vision + code generation pipelines because the model understands design intent and can generate semantically appropriate code, not just transcribe visible text
GPT-4o maintains conversation state across multiple turns, preserving context and building coherent narratives. The model tracks conversation history, remembers user preferences and constraints mentioned earlier, and generates responses that are consistent with prior exchanges. Supports up to 128K tokens of conversation history without losing coherence.
Unique: Context preservation is handled through explicit message history in the API, not implicit server-side state; gives applications full control over context management and enables stateless, scalable deployments
vs alternatives: More flexible than systems with implicit state management because applications can implement custom context pruning, summarization, or filtering strategies
GPT-4o includes built-in function calling via OpenAI's function schema format, where developers define tool signatures as JSON schemas and the model outputs structured function calls with validated arguments. The model learns to map natural language requests to appropriate functions and generate correctly-typed arguments without additional prompting. Supports parallel function calls (multiple tools invoked in single response) and automatic retry logic for invalid schemas.
Unique: Native function calling is deeply integrated into the model's training and inference, not a post-hoc wrapper; the model learns to reason about tool availability and constraints during pretraining, resulting in more natural tool selection than prompt-based approaches
vs alternatives: More reliable function calling than Claude 3.5 Sonnet (which uses tool_use blocks) because GPT-4o's schema binding is tighter and supports parallel calls natively without workarounds
GPT-4o's JSON mode constrains the output to valid JSON matching a provided schema, using constrained decoding (token-level filtering during generation) to ensure every output is parseable and schema-compliant. The model generates JSON directly without intermediate text, eliminating parsing errors and hallucinated fields. Supports nested objects, arrays, enums, and type constraints (string, number, boolean, null).
Unique: Uses token-level constrained decoding during inference to guarantee schema compliance, not post-hoc validation; the model's probability distribution is filtered at each step to only allow tokens that keep the output valid JSON, eliminating hallucinated fields entirely
vs alternatives: More reliable than Claude's tool_use for structured output because constrained decoding guarantees validity at generation time rather than relying on the model to self-correct
+6 more capabilities