What can llm-info do?

multi-provider llm model metadata aggregation, context window and token limit lookup, cross-provider pricing lookup and cost calculation, model capability and feature metadata lookup, npm package distribution and versioning, model identifier normalization and aliasing, structured data export and format conversion

llm-info

RepositoryFree

Information on LLM models, context window token limit, output token limit, pricing and more

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

multi-provider llm model metadata aggregation

Medium confidence

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.

Solves for

Compare context window sizes and token limits across different LLM providers to choose the best model for my use caseQuickly look up the current pricing per 1K input/output tokens for a specific model without visiting multiple websitesBuild a model selection tool that can recommend models based on context window requirements and budget constraintsMaintain 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

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

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

What makes it unique

Provides a unified, curated dataset of LLM model specifications across 7+ providers in a single npm package, eliminating the need to query multiple provider APIs or documentation sites; implements a normalized schema that maps provider-specific naming conventions and pricing structures into consistent fields for programmatic comparison

vs alternatives

Faster and simpler than building custom provider API integrations or web scraping documentation, and more comprehensive than single-provider SDKs because it covers OpenAI, Anthropic, Google, DeepSeek, Azure, and OpenRouter in one dependency

context window and token limit lookup

Medium confidence

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.

Solves for

Check if my prompt fits within a model's context window before sending it to avoid token limit errorsCalculate the maximum number of tokens I can use for output given my input prompt lengthProgrammatically select a model that has a large enough context window for my document processing taskValidate 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

Requires

Model identifier string matching provider naming convention

Knowledge of which provider the model belongs to (or ability to search across providers)

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

What makes it unique

Centralizes token limit data across multiple providers in a single queryable dataset, eliminating the need to maintain separate lookups for OpenAI's context windows, Anthropic's token limits, Google's specifications, etc.; uses a normalized integer representation that abstracts away provider-specific terminology differences

vs alternatives

More convenient than checking each provider's documentation individually or making test API calls to discover limits; more reliable than hardcoding limits in application code because updates are centralized and versioned

cross-provider pricing lookup and cost calculation

Medium confidence

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.

Solves for

Compare the cost of running my prompt on different models to optimize for budgetCalculate the total API cost for processing a batch of documents across different model optionsBuild a cost-aware model selection algorithm that recommends the cheapest model meeting my quality requirementsTrack 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

Requires

Model identifier and provider name

Estimated input and output token counts for cost calculation

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)

What makes it unique

Aggregates pricing data from 7+ providers into a single normalized schema with per-token costs, enabling direct cost comparison without manual spreadsheet maintenance or visiting multiple pricing pages; implements a calculation pattern that supports both input and output token pricing for accurate cost estimation

vs alternatives

Faster than manually checking provider websites for pricing updates; more accurate than hardcoded pricing in application code because it's centralized and versioned; enables programmatic cost optimization that would be tedious to implement with scattered pricing data

model capability and feature metadata lookup

Medium confidence

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.

Solves for

Check if a model supports vision/image understanding before attempting to send images in my promptVerify that a model supports function calling before building a tool-use workflow around itSelect a model that supports JSON mode for structured output requirementsBuild 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

Requires

Model identifier string

Provider name to disambiguate models

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)

What makes it unique

Maintains a structured capability matrix across providers that goes beyond token limits to include feature flags (vision, function calling, JSON mode, streaming, etc.), enabling programmatic feature detection without parsing provider documentation or making test API calls

vs alternatives

More comprehensive than provider SDKs alone because it provides cross-provider feature comparison; more reliable than hardcoding feature support because it's centralized and can be updated as providers add or deprecate features

npm package distribution and versioning

Medium confidence

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.

Solves for

Install the latest model metadata into my Node.js project without manually downloading or maintaining data filesPin a specific version of the model database to ensure consistent behavior across environments and deploymentsUpdate model metadata by running npm update and get the latest pricing, token limits, and capabilitiesInclude 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

Requires

Node.js 14+ runtime

npm 6+ or compatible package manager (yarn, pnpm)

Internet connection for initial npm install and updates

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

What makes it unique

Packages model metadata as a standard npm module with semantic versioning and standard npm distribution, making it a first-class dependency in Node.js projects rather than a separate data file or API service; enables version pinning and reproducible builds

vs alternatives

More convenient than maintaining a separate JSON file or API endpoint because it integrates with standard npm workflows; more reliable than web-based lookups because data is bundled locally and doesn't depend on external service availability

model identifier normalization and aliasing

Medium confidence

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.

Solves for

Look up model metadata using a short alias (e.g., 'gpt-4') instead of the full versioned identifierHandle user input that may use outdated or alternative model names without failingMap between different naming conventions used by different providers for the same underlying modelBuild 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

Requires

Model identifier or alias string

Optional provider context to disambiguate between providers

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)

What makes it unique

Implements a normalization layer that maps multiple naming conventions and aliases to canonical model identifiers, reducing lookup failures and enabling flexible user input handling without requiring exact model name matches

vs alternatives

More user-friendly than requiring exact model identifiers because it handles common aliases and variations; more robust than simple string matching because it understands model versioning and provider-specific naming conventions

structured data export and format conversion

Medium confidence

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.

Solves for

Export model metadata to CSV for analysis in Excel or Google SheetsGenerate TypeScript type definitions from model metadata for type-safe model selection in my applicationConvert model data to a format compatible with my data analysis or BI toolCreate 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

Requires

Access to the model metadata dataset

Target format specification (JSON, CSV, TypeScript, etc.)

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

What makes it unique

Provides multi-format export capabilities (JSON, CSV, TypeScript types) from a single model metadata source, enabling integration with diverse tools and workflows without requiring custom transformation code for each use case

vs alternatives

More flexible than single-format APIs because it supports multiple output formats; more convenient than manual data transformation because export logic is built-in and handles format-specific details

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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multi-provider llm abstraction with model switching

1 shared capability

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
✓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

Known 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)
⚠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

Requirements

Node.js 14+ or browser JavaScript runtimenpm package manager or direct JSON file accessNo API keys required — all data is pre-computed and bundledModel identifier string matching provider naming conventionKnowledge of which provider the model belongs to (or ability to search across providers)Model identifier and provider nameEstimated input and output token counts for cost calculationModel identifier string

Input / Output

Accepts: 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), model identifier string (e.g., 'gpt-4-turbo', 'claude-3-sonnet'), optional provider filter to disambiguate models with similar names, model identifier string, provider name, input token count (integer), output token count (integer), optional feature name to filter for specific capabilities, package.json dependency declaration, npm install command with optional version specifier, model identifier string (canonical or alias), optional provider name for disambiguation, format specifier (json, csv, typescript, etc.), optional filter criteria to export subset of models

Produces: 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, context window size (integer, max input tokens), output token limit (integer, max completion tokens), total model capacity (context + output), pricing per 1K input tokens (decimal, USD), pricing per 1K output tokens (decimal, USD), total estimated cost for given token counts (decimal, USD), capability flags object (boolean or enum values), array of supported features, feature availability status (supported, experimental, deprecated), installed npm package in node_modules/, importable JavaScript/TypeScript modules, JSON data files containing model metadata, canonical model identifier, provider name, model metadata object, JSON object or array, CSV text with headers and rows, TypeScript type definitions (.d.ts), other structured formats

UnfragileRank

Adoption18%(35% weight)

Quality16%(20% weight)

Ecosystem60%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Repository

7 capabilities

Visit llm-info→

Repository Details

Package Details

npm

Registry

1.0.69

Version

3,191

Weekly Downloads

About

Information on LLM models, context window token limit, output token limit, pricing and more

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Capabilities7 decomposed

multi-provider llm model metadata aggregation

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

context window and token limit lookup

Medium confidence

Solves for

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

Requires

Model identifier string matching provider naming convention

Knowledge of which provider the model belongs to (or ability to search across providers)

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

What makes it unique

vs alternatives

cross-provider pricing lookup and cost calculation

Medium confidence

Solves for

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

Requires

Model identifier and provider name

Estimated input and output token counts for cost calculation

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)

What makes it unique

vs alternatives

model capability and feature metadata lookup

Medium confidence

Solves for

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

Requires

Model identifier string

Provider name to disambiguate models

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)

What makes it unique

vs alternatives

npm package distribution and versioning

Medium confidence

Solves for

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

Requires

Node.js 14+ runtime

npm 6+ or compatible package manager (yarn, pnpm)

Internet connection for initial npm install and updates

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

What makes it unique

vs alternatives

model identifier normalization and aliasing

Medium confidence

Solves for

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

Requires

Model identifier or alias string

Optional provider context to disambiguate between providers

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)

What makes it unique

vs alternatives

structured data export and format conversion

Medium confidence

Solves for

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

Requires

Access to the model metadata dataset

Target format specification (JSON, CSV, TypeScript, etc.)

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

What makes it unique

vs alternatives

More flexible than single-format APIs because it supports multiple output formats; more convenient than manual data transformation because export logic is built-in and handles format-specific details

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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llm-info

Capabilities7 decomposed

multi-provider llm model metadata aggregation

context window and token limit lookup

cross-provider pricing lookup and cost calculation

model capability and feature metadata lookup

npm package distribution and versioning

model identifier normalization and aliasing

structured data export and format conversion

Related Artifactssharing capabilities

llm-zoo

AgentOps

llm-cost

sim

Langfuse

openclaw-superpowers

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to llm-info

Are you the builder of llm-info?

Get the weekly brief

Data Sources

llm-info

Capabilities7 decomposed

multi-provider llm model metadata aggregation

context window and token limit lookup

cross-provider pricing lookup and cost calculation

model capability and feature metadata lookup

npm package distribution and versioning

model identifier normalization and aliasing

structured data export and format conversion

Related Artifactssharing capabilities

llm-zoo

AgentOps

llm-cost

sim

Langfuse

openclaw-superpowers

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to llm-info

Are you the builder of llm-info?

Get the weekly brief

Data Sources