multi-provider llm model registry with real-time pricing, context window and capability filtering for model selection, npm package distribution of model metadata, cross-provider model comparison and cost analysis, model capability matrix querying, provider-agnostic model abstraction layer, real-time pricing data aggregation and curation, context window specification and comparison, model release date and version tracking, open-source and self-hosted model identification

llm-zoo

RepositoryFree

100+ LLM models. Pricing, capabilities, context windows. Always current.

Open Source

/ 100

10 capabilities

Capabilities10 decomposed

multi-provider llm model registry with real-time pricing

Medium confidence

Maintains a curated, always-current registry of 100+ LLM models across 15+ providers (OpenAI, Anthropic, Google, DeepSeek, Grok, Qwen, MiniMax, GLM, Moonshot, DashScope, OpenRouter, etc.) with dynamically updated pricing, context window specifications, and capability matrices. The registry is structured as queryable metadata that enables developers to programmatically discover and compare models without manual research or API calls to each provider.

Solves for

I need to find the cheapest LLM model that supports a 128K context window for my use caseI want to compare pricing and capabilities across all major LLM providers in one placeI need to programmatically select the best model for a task based on cost, latency, and context requirementsI'm building a multi-model application and need current pricing to estimate costs

Best for

LLM application developers evaluating model trade-offs

teams building cost-optimized AI systems with provider flexibility

startups prototyping with multiple LLM backends

Requires

Node.js 14+ or browser JavaScript runtime

npm or yarn package manager

No API keys required for registry access (data is static/bundled)

Limitations

Pricing data is point-in-time and may lag actual provider updates by hours to days

Does not include real-time availability or rate-limit information per provider

Context window and capability data is manually curated, subject to human error

What makes it unique

Aggregates 100+ models from 15+ providers into a single queryable registry with real-time pricing updates, rather than requiring developers to check each provider's API or documentation separately. Structured as an npm package for programmatic access rather than a static website.

vs alternatives

More comprehensive and programmatically accessible than provider-specific documentation; more current than static comparison websites; enables cost-aware model selection in code rather than manual research

context window and capability filtering for model selection

Medium confidence

Provides structured filtering and querying across model metadata dimensions including context window size, supported modalities (text, vision, audio), function calling support, fine-tuning availability, and cost per token. Enables developers to programmatically narrow model choices based on technical requirements rather than manually reviewing provider documentation.

Solves for

I need to find all models with at least 200K context windows under $0.01 per 1K tokensWhich models support vision input and function calling simultaneously?I want to filter for models that support fine-tuning within my budgetShow me all open-source or self-hosted alternatives to GPT-4

Best for

developers building constraint-aware model selection logic

teams with specific technical requirements (vision, function calling, context size)

cost-conscious teams optimizing for price-per-capability

Requires

Node.js 14+

npm package installation

Basic JavaScript knowledge for query construction

Limitations

Filtering is based on static metadata; does not reflect real-time model availability or performance

Capability data is binary (supported/not supported) without nuance on quality or performance differences

No filtering by latency, throughput, or availability SLAs

What makes it unique

Exposes a queryable metadata schema that allows developers to filter models by technical capabilities (vision, function calling, fine-tuning) and cost constraints in a single operation, rather than requiring manual cross-referencing of provider documentation.

vs alternatives

Enables programmatic, constraint-based model selection in application code rather than manual research; more flexible than provider-specific SDKs which lock you into one vendor

npm package distribution of model metadata

Medium confidence

Distributes the LLM model registry as a lightweight npm package (1442 downloads) that can be installed as a dependency and imported directly into Node.js or browser applications. The package bundles model metadata as static JSON or JavaScript objects, enabling zero-latency local queries without external API calls or network dependencies.

Solves for

I want to include LLM model metadata in my application without adding external API dependenciesI need to query available models offline or in environments with restricted network accessI want to bundle model information with my application for reproducible deploymentsI need to version-lock model metadata to a specific point in time for consistency

Best for

Node.js application developers

teams building offline-capable AI tools

developers prioritizing minimal external dependencies

Requires

Node.js 14+

npm 6+ or yarn 1.22+

package.json in project root

Limitations

Requires manual package updates to get latest pricing and model additions

No automatic sync with provider changes; pricing may become stale between releases

Package size grows with each new model addition; may impact bundle size for browser applications

What makes it unique

Packages model registry as a lightweight npm dependency with static metadata, enabling zero-latency local access without external API calls or network dependencies, rather than requiring API calls to a central service.

vs alternatives

Faster and more reliable than API-based registries; no network latency or availability risk; can be version-locked for reproducible builds; lighter than maintaining a full database

cross-provider model comparison and cost analysis

Medium confidence

Enables side-by-side comparison of models across multiple providers by normalizing pricing (cost per 1K tokens for input/output), context windows, and capabilities into a unified schema. Developers can programmatically calculate total cost of ownership for different model choices or generate comparison matrices for decision-making.

Solves for

I want to calculate the cost difference between using GPT-4 vs Claude 3 Opus for my workloadShow me a comparison table of all vision-capable models ranked by price per 1K tokensI need to estimate total monthly costs for different model selection strategiesWhich provider offers the best value for long-context applications?

Best for

teams evaluating multi-provider strategies

cost-conscious developers optimizing LLM spend

product managers comparing vendor options

Requires

Node.js 14+

npm package installed

Basic understanding of token economics

Limitations

Pricing comparison is static and does not account for volume discounts or enterprise pricing

Does not include inference latency or throughput in cost calculations

Comparison is based on published pricing; actual costs may vary by region or account tier

What makes it unique

Normalizes pricing across providers with different token accounting methods (some charge per 1K tokens, some per token) into a unified cost schema, enabling apples-to-apples comparison without manual conversion.

vs alternatives

More comprehensive than individual provider pricing pages; enables programmatic cost analysis rather than manual spreadsheet comparison; accounts for input/output token price differences

model capability matrix querying

Medium confidence

Exposes a structured capability matrix for each model including supported modalities (text, vision, audio), function calling support, fine-tuning availability, tool use, streaming, and other technical features. Developers can query this matrix to find models matching specific capability requirements without reading provider documentation.

Solves for

I need a model that supports both vision input and function calling for my agentWhich models support streaming responses for real-time applications?I want to find all models that can be fine-tuned within my budgetShow me models that support tool use and have at least 100K context windows

Best for

developers building multi-modal AI applications

teams implementing function-calling agents

researchers comparing model feature sets

Requires

Node.js 14+

npm package installed

Limitations

Capability data is binary (supported/not supported) without quality or performance metrics

Does not include implementation details (e.g., vision quality, function calling latency)

Capabilities may change with provider updates; registry may lag actual provider changes

What makes it unique

Structures model capabilities as a queryable matrix rather than prose documentation, enabling programmatic matching of technical requirements to models without manual documentation review.

vs alternatives

More discoverable than provider documentation; enables constraint-based model selection in code; supports complex capability queries (AND, OR, NOT combinations)

provider-agnostic model abstraction layer

Medium confidence

Provides a unified metadata schema that abstracts away provider-specific naming conventions, pricing structures, and capability representations. Developers can write model-selection logic once and apply it across providers without conditional logic for each vendor's API or documentation format.

Solves for

I want to build an application that can switch between OpenAI and Anthropic models without code changesI need to abstract model selection logic from provider-specific implementation detailsI want to support multiple providers in my application without duplicating model metadataI need a vendor-neutral way to represent model capabilities in my codebase

Best for

teams building multi-provider LLM applications

developers avoiding vendor lock-in

teams with provider flexibility requirements

Requires

Node.js 14+

npm package installed

Understanding of provider-specific API differences for actual integration

Limitations

Abstraction layer does not include provider-specific features or optimizations

Does not handle provider-specific authentication or API differences

Unified schema may lose nuance in provider-specific capabilities

What makes it unique

Normalizes metadata from 15+ providers into a single schema, enabling developers to write provider-agnostic model selection logic without conditional branches for each vendor.

vs alternatives

Reduces vendor lock-in compared to provider-specific SDKs; enables easier provider switching; supports multi-provider fallback strategies without code duplication

real-time pricing data aggregation and curation

Medium confidence

Continuously monitors and aggregates pricing information from 15+ LLM providers, normalizing different pricing models (per-token, per-1K-tokens, per-request) into a unified cost structure. The registry is manually curated and updated to reflect provider pricing changes, ensuring developers have current cost information for budgeting and model selection.

Solves for

I need current pricing for all major LLM models to estimate my monthly costsI want to be alerted when a model's pricing changes significantlyI need to track pricing trends to optimize my model selection over timeI want to include current pricing in my application's model selection logic

Best for

cost-conscious development teams

teams with tight LLM budgets

applications requiring accurate cost estimation

Requires

Node.js 14+

npm package installed

Regular package updates to stay current with pricing changes

Limitations

Pricing updates are manual and may lag actual provider changes by hours to days

No automatic alerts for pricing changes; requires manual package updates

Does not include volume discounts, enterprise pricing, or regional variations

What makes it unique

Aggregates and normalizes pricing from 15+ providers with different pricing models into a unified per-token cost structure, updated through manual curation rather than automated scraping or API calls.

vs alternatives

More comprehensive than individual provider pricing pages; normalized for easy comparison; bundled with application for offline access; more reliable than web scraping

context window specification and comparison

Medium confidence

Maintains detailed context window specifications for each model including input context limit, output token limit, and any special considerations (e.g., sliding window, context compression). Enables developers to filter models by context requirements and estimate token usage for their workloads.

Solves for

I need a model with at least 200K context window for processing long documentsWhich models support the longest context windows for my use case?I want to estimate how many documents I can fit in a model's context windowI need to find models with sufficient context for my RAG application

Best for

developers building long-context applications (RAG, document analysis)

teams processing large documents or multiple files

researchers working with long-form content

Requires

Node.js 14+

npm package installed

Limitations

Context window specifications are static; actual available context may vary by provider implementation

Does not include information about context compression or sliding window behavior

No performance metrics for how models handle near-maximum context

What makes it unique

Provides queryable context window specifications for 100+ models, enabling programmatic filtering by context requirements rather than manual research across provider documentation.

vs alternatives

More comprehensive than individual provider specs; enables constraint-based model selection for long-context applications; supports context-aware cost estimation

model release date and version tracking

Medium confidence

Tracks release dates and version information for each model, enabling developers to identify the latest versions, understand model lineage (e.g., GPT-4 → GPT-4 Turbo → GPT-4o), and make informed decisions about model stability and feature availability based on release recency.

Solves for

I want to use the latest version of a model for my applicationI need to understand the difference between GPT-4 and GPT-4 TurboI want to track when new model versions are releasedI need to know if a model is stable or recently released

Best for

developers staying current with model releases

teams evaluating model maturity and stability

researchers tracking model evolution

Requires

Node.js 14+

npm package installed

Limitations

Release date data is manually curated and may be incomplete for older models

Does not include detailed changelog information between versions

No automatic notifications for new model releases

What makes it unique

Tracks model release dates and version lineage across providers, enabling developers to understand model maturity and feature availability without checking each provider's release notes separately.

vs alternatives

More discoverable than scattered provider release notes; enables programmatic filtering by model age; supports informed decisions about model stability and feature availability

open-source and self-hosted model identification

Medium confidence

Identifies and catalogs open-source and self-hosted LLM alternatives within the registry, enabling developers to find models they can run locally or deploy on their own infrastructure without vendor dependencies. Includes metadata about model size, quantization options, and deployment requirements.

Solves for

I want to find open-source alternatives to GPT-4 for my applicationI need a model I can run locally without cloud dependenciesI want to compare self-hosted models with commercial optionsI need to find models suitable for on-premises deployment

Best for

teams prioritizing data privacy and on-premises deployment

developers avoiding vendor lock-in

organizations with restricted cloud usage

Requires

Node.js 14+

npm package installed

Infrastructure for running self-hosted models (not provided by this package)

Limitations

Does not include deployment instructions or infrastructure requirements

No performance benchmarks for self-hosted models

Does not track model size, quantization options, or hardware requirements

What makes it unique

Identifies open-source and self-hosted alternatives within a comprehensive registry of 100+ models, enabling developers to compare commercial and open-source options in a single query.

vs alternatives

More comprehensive than open-source-only registries; enables side-by-side comparison with commercial models; supports informed decisions about deployment strategy

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

Related Artifactssharing capabilities

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Repository30

llm-info

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

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2 shared capabilities

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model pricing registry with provider-specific rate structures

1 shared capability

CLI Tool40

GPTScript

Natural language scripting framework.

multi-provider llm orchestration with dynamic model selection

1 shared capability

Framework46

Open WebUI

Self-hosted ChatGPT-like UI — supports Ollama/OpenAI, RAG, web search, multi-user, plugins.

multi-provider llm model aggregation and discovery

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dynamic model discovery and pricing synchronization

1 shared capability

Platform18

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multi-provider llm model selection and routing

1 shared capability

Best For

✓LLM application developers evaluating model trade-offs
✓teams building cost-optimized AI systems with provider flexibility
✓startups prototyping with multiple LLM backends
✓researchers comparing model capabilities across vendors
✓developers building constraint-aware model selection logic
✓teams with specific technical requirements (vision, function calling, context size)
✓cost-conscious teams optimizing for price-per-capability
✓researchers comparing model feature matrices

Known Limitations

⚠Pricing data is point-in-time and may lag actual provider updates by hours to days
⚠Does not include real-time availability or rate-limit information per provider
⚠Context window and capability data is manually curated, subject to human error
⚠No historical pricing trends or cost prediction modeling
⚠Limited to models the maintainers have explicitly added; emerging models may have delays
⚠Filtering is based on static metadata; does not reflect real-time model availability or performance

Requirements

Node.js 14+ or browser JavaScript runtimenpm or yarn package managerNo API keys required for registry access (data is static/bundled)Node.js 14+npm package installationBasic JavaScript knowledge for query constructionnpm 6+ or yarn 1.22+package.json in project root

Input / Output

Accepts: query parameters (model name, provider, min context window, max price), filter criteria (capability flags, pricing tier), filter objects with properties like minContextWindow, maxPricePerToken, supportsVision, supportsFunctionCalling, comparison operators (>, <, ===, includes), import statements, require() calls, model names or provider names, token counts for workload estimation, filter criteria (capability requirements), capability names (vision, functionCalling, streaming, fineTuning, toolUse), boolean filters, arrays of required capabilities, provider names, model identifiers, capability queries, model names, token counts for cost estimation, minimum context window requirement, token count estimates, date ranges for filtering, filter for open-source/self-hosted models, capability requirements, cost constraints

Produces: structured JSON model metadata, filtered arrays of model objects, comparison matrices (pricing, context, capabilities), boolean match results, aggregated statistics (min/max price, average context window), JavaScript objects or JSON, arrays of model metadata, exported functions for filtering/querying, cost comparison objects, ranked arrays by cost metric, total cost of ownership estimates, comparison matrices (CSV, JSON, or formatted text), filtered model arrays, capability matrices (2D arrays or objects), capability intersection/union results, normalized model metadata objects, provider-agnostic capability representations, unified pricing structures, pricing objects (input/output token costs), total cost estimates, cost per model comparisons, pricing matrices, context window specifications, filtered model arrays by context size, context utilization estimates, comparison matrices, release date information, version lineage data, model age/recency metrics, filtered arrays sorted by release date, filtered arrays of open-source models, comparison matrices (open-source vs commercial), model metadata with deployment indicators

UnfragileRank

Adoption15%(35% weight)

Quality20%(20% weight)

Ecosystem70%(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

10 capabilities

Visit llm-zoo→

Repository Details

Package Details

npm

Registry

1.4.0

Version

1,442

Weekly Downloads

About

100+ LLM models. Pricing, capabilities, context windows. Always current.

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Capabilities10 decomposed

multi-provider llm model registry with real-time pricing

Medium confidence

Solves for

Best for

LLM application developers evaluating model trade-offs

teams building cost-optimized AI systems with provider flexibility

startups prototyping with multiple LLM backends

Requires

Node.js 14+ or browser JavaScript runtime

npm or yarn package manager

No API keys required for registry access (data is static/bundled)

Limitations

Pricing data is point-in-time and may lag actual provider updates by hours to days

Does not include real-time availability or rate-limit information per provider

Context window and capability data is manually curated, subject to human error

What makes it unique

vs alternatives

context window and capability filtering for model selection

Medium confidence

Solves for

Best for

developers building constraint-aware model selection logic

teams with specific technical requirements (vision, function calling, context size)

cost-conscious teams optimizing for price-per-capability

Requires

Node.js 14+

npm package installation

Basic JavaScript knowledge for query construction

Limitations

Filtering is based on static metadata; does not reflect real-time model availability or performance

Capability data is binary (supported/not supported) without nuance on quality or performance differences

No filtering by latency, throughput, or availability SLAs

What makes it unique

vs alternatives

Enables programmatic, constraint-based model selection in application code rather than manual research; more flexible than provider-specific SDKs which lock you into one vendor

npm package distribution of model metadata

Medium confidence

Solves for

Best for

Node.js application developers

teams building offline-capable AI tools

developers prioritizing minimal external dependencies

Requires

Node.js 14+

npm 6+ or yarn 1.22+

package.json in project root

Limitations

Requires manual package updates to get latest pricing and model additions

No automatic sync with provider changes; pricing may become stale between releases

Package size grows with each new model addition; may impact bundle size for browser applications

What makes it unique

vs alternatives

Faster and more reliable than API-based registries; no network latency or availability risk; can be version-locked for reproducible builds; lighter than maintaining a full database

cross-provider model comparison and cost analysis

Medium confidence

Solves for

Best for

teams evaluating multi-provider strategies

cost-conscious developers optimizing LLM spend

product managers comparing vendor options

Requires

Node.js 14+

npm package installed

Basic understanding of token economics

Limitations

Pricing comparison is static and does not account for volume discounts or enterprise pricing

Does not include inference latency or throughput in cost calculations

Comparison is based on published pricing; actual costs may vary by region or account tier

What makes it unique

vs alternatives

More comprehensive than individual provider pricing pages; enables programmatic cost analysis rather than manual spreadsheet comparison; accounts for input/output token price differences

model capability matrix querying

Medium confidence

Solves for

Best for

developers building multi-modal AI applications

teams implementing function-calling agents

researchers comparing model feature sets

Requires

Node.js 14+

npm package installed

Limitations

Capability data is binary (supported/not supported) without quality or performance metrics

Does not include implementation details (e.g., vision quality, function calling latency)

Capabilities may change with provider updates; registry may lag actual provider changes

What makes it unique

Structures model capabilities as a queryable matrix rather than prose documentation, enabling programmatic matching of technical requirements to models without manual documentation review.

vs alternatives

More discoverable than provider documentation; enables constraint-based model selection in code; supports complex capability queries (AND, OR, NOT combinations)

provider-agnostic model abstraction layer

Medium confidence

Solves for

Best for

teams building multi-provider LLM applications

developers avoiding vendor lock-in

teams with provider flexibility requirements

Requires

Node.js 14+

npm package installed

Understanding of provider-specific API differences for actual integration

Limitations

Abstraction layer does not include provider-specific features or optimizations

Does not handle provider-specific authentication or API differences

Unified schema may lose nuance in provider-specific capabilities

What makes it unique

Normalizes metadata from 15+ providers into a single schema, enabling developers to write provider-agnostic model selection logic without conditional branches for each vendor.

vs alternatives

Reduces vendor lock-in compared to provider-specific SDKs; enables easier provider switching; supports multi-provider fallback strategies without code duplication

real-time pricing data aggregation and curation

Medium confidence

Solves for

Best for

cost-conscious development teams

teams with tight LLM budgets

applications requiring accurate cost estimation

Requires

Node.js 14+

npm package installed

Regular package updates to stay current with pricing changes

Limitations

Pricing updates are manual and may lag actual provider changes by hours to days

No automatic alerts for pricing changes; requires manual package updates

Does not include volume discounts, enterprise pricing, or regional variations

What makes it unique

vs alternatives

More comprehensive than individual provider pricing pages; normalized for easy comparison; bundled with application for offline access; more reliable than web scraping

context window specification and comparison

Medium confidence

Solves for

Best for

developers building long-context applications (RAG, document analysis)

teams processing large documents or multiple files

researchers working with long-form content

Requires

Node.js 14+

npm package installed

Limitations

Context window specifications are static; actual available context may vary by provider implementation

Does not include information about context compression or sliding window behavior

No performance metrics for how models handle near-maximum context

What makes it unique

Provides queryable context window specifications for 100+ models, enabling programmatic filtering by context requirements rather than manual research across provider documentation.

vs alternatives

More comprehensive than individual provider specs; enables constraint-based model selection for long-context applications; supports context-aware cost estimation

model release date and version tracking

Medium confidence

Solves for

Best for

developers staying current with model releases

teams evaluating model maturity and stability

researchers tracking model evolution

Requires

Node.js 14+

npm package installed

Limitations

Release date data is manually curated and may be incomplete for older models

Does not include detailed changelog information between versions

No automatic notifications for new model releases

What makes it unique

Tracks model release dates and version lineage across providers, enabling developers to understand model maturity and feature availability without checking each provider's release notes separately.

vs alternatives

More discoverable than scattered provider release notes; enables programmatic filtering by model age; supports informed decisions about model stability and feature availability

open-source and self-hosted model identification

Medium confidence

Solves for

Best for

teams prioritizing data privacy and on-premises deployment

developers avoiding vendor lock-in

organizations with restricted cloud usage

Requires

Node.js 14+

npm package installed

Infrastructure for running self-hosted models (not provided by this package)

Limitations

Does not include deployment instructions or infrastructure requirements

No performance benchmarks for self-hosted models

Does not track model size, quantization options, or hardware requirements

What makes it unique

Identifies open-source and self-hosted alternatives within a comprehensive registry of 100+ models, enabling developers to compare commercial and open-source options in a single query.

vs alternatives

More comprehensive than open-source-only registries; enables side-by-side comparison with commercial models; supports informed decisions about deployment strategy

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

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Compare →

vectra41Repository

A lightweight, file-backed vector database for Node.js and browsers with Pinecone-compatible filtering and hybrid BM25 search.

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@tanstack/ai37API

Core TanStack AI library - Open source AI SDK

Compare →

strapi-plugin-embeddings32Repository

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llm-zoo

Capabilities10 decomposed

multi-provider llm model registry with real-time pricing

context window and capability filtering for model selection

npm package distribution of model metadata

cross-provider model comparison and cost analysis

model capability matrix querying

provider-agnostic model abstraction layer

real-time pricing data aggregation and curation

context window specification and comparison

model release date and version tracking

open-source and self-hosted model identification

Related Artifactssharing capabilities

llm-info

llm-cost

GPTScript

Open WebUI

Manifest

Fine Tuner

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to llm-zoo

Are you the builder of llm-zoo?

Get the weekly brief

Data Sources

llm-zoo

Capabilities10 decomposed

multi-provider llm model registry with real-time pricing

context window and capability filtering for model selection

npm package distribution of model metadata

cross-provider model comparison and cost analysis

model capability matrix querying

provider-agnostic model abstraction layer

real-time pricing data aggregation and curation

context window specification and comparison

model release date and version tracking

open-source and self-hosted model identification

Related Artifactssharing capabilities

llm-info

llm-cost

GPTScript

Open WebUI

Manifest

Fine Tuner

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to llm-zoo

Are you the builder of llm-zoo?

Get the weekly brief

Data Sources