{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-llm-info","slug":"llm-info","name":"llm-info","type":"webapp","url":"https://eval.16x.engineer/","page_url":"https://unfragile.ai/llm-info","categories":["research-search"],"tags":["llm","language model","model","info","eval","prompt","token","context-window","pricing","token-limit","provider","gpt","claude","gemini","deepseek","openai","anthropic","azure-openai","deepseek","openrouter"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-llm-info__cap_0","uri":"capability://data.processing.analysis.multi.provider.llm.model.metadata.aggregation","name":"multi-provider llm model metadata aggregation","description":"Aggregates and normalizes model information across 7+ LLM providers (OpenAI, Anthropic, Google, DeepSeek, Azure OpenAI, OpenRouter, etc.) into a unified schema. Implements a provider-agnostic data model that maps heterogeneous API responses and documentation into consistent fields, enabling cross-provider comparison without manual lookups or API calls to each provider individually.","intents":["Compare context window sizes and token limits across different LLM providers to choose the best model for my use case","Quickly look up the current pricing per 1K input/output tokens for a specific model without visiting multiple websites","Build a model selection tool that can recommend models based on context window requirements and budget constraints","Maintain an up-to-date reference of model capabilities without manually tracking provider documentation changes"],"best_for":["LLM application developers building multi-provider abstractions or model selection logic","AI product teams evaluating models across providers for cost-performance tradeoffs","Prompt engineers and researchers comparing model specifications during experimentation","Teams building LLM evaluation frameworks that need standardized model metadata"],"limitations":["Data freshness depends on manual updates — no automatic sync with provider APIs, so pricing and model availability may lag behind real-time changes","Limited to models explicitly added to the dataset; emerging or niche models may not be included until manually curated","No real-time token counting or validation — metadata is static and doesn't account for tokenizer variations between providers","Schema normalization may lose provider-specific nuances (e.g., Azure OpenAI deployment-specific settings, OpenRouter routing parameters)"],"requires":["Node.js 14+ or browser JavaScript runtime","npm package manager or direct JSON file access","No API keys required — all data is pre-computed and bundled"],"input_types":["model identifier string (e.g., 'gpt-4-turbo', 'claude-3-opus')","provider name string (e.g., 'openai', 'anthropic')","filter criteria (context window range, pricing range, provider list)"],"output_types":["structured JSON object with model metadata","array of models matching filter criteria","normalized pricing data (cost per 1K tokens)","context window and token limit specifications"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-info__cap_1","uri":"capability://data.processing.analysis.context.window.and.token.limit.lookup","name":"context window and token limit lookup","description":"Provides direct access to model-specific context window sizes (max input tokens) and output token limits for any supported LLM. Implements a key-value lookup pattern where model identifiers map to token specifications, enabling developers to validate prompt lengths and plan token budgets before API calls without trial-and-error or documentation hunting.","intents":["Check if my prompt fits within a model's context window before sending it to avoid token limit errors","Calculate the maximum number of tokens I can use for output given my input prompt length","Programmatically select a model that has a large enough context window for my document processing task","Validate user input or document size against model constraints in my application before making API calls"],"best_for":["Backend developers building LLM API wrappers or prompt management systems","RAG (Retrieval-Augmented Generation) pipeline builders who need to chunk documents based on model limits","LLM application developers implementing token budget validation and error handling","Teams building cost optimization tools that need to match document sizes to appropriate models"],"limitations":["Token counts are approximate and may not match exact tokenizer behavior — different models use different tokenization algorithms (BPE variants, SentencePiece, etc.)","Does not account for system prompts, function definitions, or other overhead that consumes tokens in real API calls","Context window data is static and doesn't reflect dynamic model updates or fine-tuned variants with different limits","No support for custom or fine-tuned models that may have different token limits than base models"],"requires":["Model identifier string matching provider naming convention","Knowledge of which provider the model belongs to (or ability to search across providers)"],"input_types":["model identifier string (e.g., 'gpt-4-turbo', 'claude-3-sonnet')","optional provider filter to disambiguate models with similar names"],"output_types":["context window size (integer, max input tokens)","output token limit (integer, max completion tokens)","total model capacity (context + output)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-info__cap_2","uri":"capability://data.processing.analysis.cross.provider.pricing.lookup.and.cost.calculation","name":"cross-provider pricing lookup and cost calculation","description":"Stores and retrieves pricing information (cost per 1K input tokens, cost per 1K output tokens) for models across all supported providers. Implements a pricing schema that normalizes different provider billing models (per-token, per-request, tiered pricing) into a common format, enabling cost comparison and budget calculations without visiting provider pricing pages or maintaining spreadsheets.","intents":["Compare the cost of running my prompt on different models to optimize for budget","Calculate the total API cost for processing a batch of documents across different model options","Build a cost-aware model selection algorithm that recommends the cheapest model meeting my quality requirements","Track and forecast LLM API spending by model and provider for billing and budgeting purposes"],"best_for":["Cost-conscious teams building LLM applications with tight budgets or large-scale deployments","Product managers evaluating model economics for feature decisions","DevOps and platform engineers implementing cost monitoring and optimization","Researchers comparing model cost-effectiveness across providers for benchmarking studies"],"limitations":["Pricing data is manually curated and updated infrequently — real-time pricing changes from providers are not reflected immediately","Does not account for volume discounts, enterprise agreements, or region-specific pricing variations","Simplified pricing model may not capture complex billing scenarios (e.g., batch processing discounts, reserved capacity pricing, tiered rate structures)","No support for dynamic pricing or promotional pricing that varies by time or customer segment"],"requires":["Model identifier and provider name","Estimated input and output token counts for cost calculation"],"input_types":["model identifier string","provider name","input token count (integer)","output token count (integer)"],"output_types":["pricing per 1K input tokens (decimal, USD)","pricing per 1K output tokens (decimal, USD)","total estimated cost for given token counts (decimal, USD)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-info__cap_3","uri":"capability://data.processing.analysis.model.capability.and.feature.metadata.lookup","name":"model capability and feature metadata lookup","description":"Provides structured metadata about model capabilities beyond token limits, including support for function calling, vision/image understanding, JSON mode, streaming, and other feature flags. Implements a capability matrix that maps model identifiers to boolean or enum flags indicating which advanced features are supported, enabling feature-aware model selection and graceful degradation when features are unavailable.","intents":["Check if a model supports vision/image understanding before attempting to send images in my prompt","Verify that a model supports function calling before building a tool-use workflow around it","Select a model that supports JSON mode for structured output requirements","Build a feature detection system that automatically adapts my application to model capabilities"],"best_for":["LLM application developers building feature-rich systems that leverage advanced model capabilities","Teams implementing multi-modal applications that need to verify vision support before processing images","Developers building tool-use and function-calling systems that depend on specific model features","Framework and SDK authors implementing model-agnostic abstractions with feature detection"],"limitations":["Feature metadata is binary or categorical and doesn't capture nuanced differences in capability quality (e.g., vision accuracy, function calling reliability)","Manually curated data means new features may not be documented until after provider announcements","Does not include performance characteristics or quality metrics for features (e.g., JSON mode reliability, streaming latency)","Feature support may vary by region, deployment type (Azure vs OpenAI), or API version, which is not captured in the dataset"],"requires":["Model identifier string","Provider name to disambiguate models"],"input_types":["model identifier string","optional feature name to filter for specific capabilities"],"output_types":["capability flags object (boolean or enum values)","array of supported features","feature availability status (supported, experimental, deprecated)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-info__cap_4","uri":"capability://tool.use.integration.npm.package.distribution.and.versioning","name":"npm package distribution and versioning","description":"Distributes model metadata as an npm package with semantic versioning, enabling developers to install, update, and pin specific versions of the model database in their projects. Implements a standard npm package structure with package.json, exports, and version management, allowing integration into Node.js projects via npm install and enabling dependency management alongside other project dependencies.","intents":["Install the latest model metadata into my Node.js project without manually downloading or maintaining data files","Pin a specific version of the model database to ensure consistent behavior across environments and deployments","Update model metadata by running npm update and get the latest pricing, token limits, and capabilities","Include model metadata as a dependency in my package.json for reproducible builds and dependency tracking"],"best_for":["Node.js and JavaScript developers building LLM applications","Teams using npm-based dependency management and CI/CD pipelines","Developers who want to avoid manual data file management and prefer package manager distribution","Projects that need reproducible builds with pinned versions of model metadata"],"limitations":["npm-only distribution — no native Python, Go, or other language packages (though data can be consumed via JSON files)","Package size grows with each new model addition, potentially increasing bundle size for browser-based applications","Requires npm or compatible package manager (yarn, pnpm) — not suitable for environments without package manager support","Update frequency depends on maintainer activity — may lag behind real-time provider changes"],"requires":["Node.js 14+ runtime","npm 6+ or compatible package manager (yarn, pnpm)","Internet connection for initial npm install and updates"],"input_types":["package.json dependency declaration","npm install command with optional version specifier"],"output_types":["installed npm package in node_modules/","importable JavaScript/TypeScript modules","JSON data files containing model metadata"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-info__cap_5","uri":"capability://data.processing.analysis.model.identifier.normalization.and.aliasing","name":"model identifier normalization and aliasing","description":"Handles multiple naming conventions and aliases for the same model across providers and API versions. Implements a normalization layer that maps common aliases (e.g., 'gpt-4' vs 'gpt-4-turbo' vs 'gpt-4-0125-preview') to canonical model identifiers, reducing lookup failures due to naming inconsistencies and enabling fuzzy matching for user-provided model names.","intents":["Look up model metadata using a short alias (e.g., 'gpt-4') instead of the full versioned identifier","Handle user input that may use outdated or alternative model names without failing","Map between different naming conventions used by different providers for the same underlying model","Build a model search feature that handles typos and partial matches in model names"],"best_for":["CLI tools and user-facing applications that accept model names as input","LLM application developers building flexible model selection interfaces","Teams migrating between model versions and needing to support both old and new names","Developers building model recommendation systems that need to handle user input gracefully"],"limitations":["Aliasing is manually maintained and may not cover all historical model names or regional variants","Fuzzy matching adds complexity and may produce unexpected results for ambiguous inputs","Does not handle provider-specific deployment names (e.g., Azure OpenAI custom deployment names)","Normalization rules may become outdated as providers deprecate old models and introduce new naming schemes"],"requires":["Model identifier or alias string","Optional provider context to disambiguate between providers"],"input_types":["model identifier string (canonical or alias)","optional provider name for disambiguation"],"output_types":["canonical model identifier","provider name","model metadata object"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-info__cap_6","uri":"capability://data.processing.analysis.structured.data.export.and.format.conversion","name":"structured data export and format conversion","description":"Exports model metadata in multiple formats (JSON, CSV, TypeScript types, etc.) to support integration with different tools and workflows. Implements serialization patterns that convert the internal model database into various output formats, enabling use cases like spreadsheet analysis, type-safe TypeScript development, and data pipeline integration without requiring custom parsing or transformation code.","intents":["Export model metadata to CSV for analysis in Excel or Google Sheets","Generate TypeScript type definitions from model metadata for type-safe model selection in my application","Convert model data to a format compatible with my data analysis or BI tool","Create a snapshot of model metadata at a specific point in time for reproducibility or auditing"],"best_for":["Data analysts and product managers who want to analyze model data in spreadsheet tools","TypeScript developers building type-safe LLM applications","Teams integrating model metadata into data pipelines or BI systems","Researchers and benchmarking teams who need to export and analyze model specifications"],"limitations":["Format conversion may lose information or require custom post-processing for specific use cases","TypeScript type generation is static and doesn't capture runtime constraints or validation rules","CSV export flattens nested data structures, potentially losing hierarchical relationships","No support for custom export formats — limited to pre-defined formats"],"requires":["Access to the model metadata dataset","Target format specification (JSON, CSV, TypeScript, etc.)"],"input_types":["format specifier (json, csv, typescript, etc.)","optional filter criteria to export subset of models"],"output_types":["JSON object or array","CSV text with headers and rows","TypeScript type definitions (.d.ts)","other structured formats"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or browser JavaScript runtime","npm package manager or direct JSON file access","No API keys required — all data is pre-computed and bundled","Model identifier string matching provider naming convention","Knowledge of which provider the model belongs to (or ability to search across providers)","Model identifier and provider name","Estimated input and output token counts for cost calculation","Model identifier string","Provider name to disambiguate models","Node.js 14+ runtime"],"failure_modes":["Data freshness depends on manual updates — no automatic sync with provider APIs, so pricing and model availability may lag behind real-time changes","Limited to models explicitly added to the dataset; emerging or niche models may not be included until manually curated","No real-time token counting or validation — metadata is static and doesn't account for tokenizer variations between providers","Schema normalization may lose provider-specific nuances (e.g., Azure OpenAI deployment-specific settings, OpenRouter routing parameters)","Token counts are approximate and may not match exact tokenizer behavior — different models use different tokenization algorithms (BPE variants, SentencePiece, etc.)","Does not account for system prompts, function definitions, or other overhead that consumes tokens in real API calls","Context window data is static and doesn't reflect dynamic model updates or fine-tuned variants with different limits","No support for custom or fine-tuned models that may have different token limits than base models","Pricing data is manually curated and updated infrequently — real-time pricing changes from providers are not reflected immediately","Does not account for volume discounts, enterprise agreements, or region-specific pricing variations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.18049567998574917,"quality":0.24,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.652Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":3191,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llm-info","compare_url":"https://unfragile.ai/compare?artifact=llm-info"}},"signature":"JcZ8oJiXjpxucFxCddWDg919QvOmcczUZ/OovirIEUIZfTrvH0RBEkzA43LLyYA7CtbYOUTyiR744FAYlsK/BQ==","signedAt":"2026-06-22T07:15:24.828Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-info","artifact":"https://unfragile.ai/llm-info","verify":"https://unfragile.ai/api/v1/verify?slug=llm-info","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}