Install Jentic One Beta

Connect the Model Price Watch API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Model Price Watch API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmodelpricewatch.com%2Fmodelpricewatch" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmodelpricewatch.com%2Fmodelpricewatch" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Model Price Watch API.

Retrieve every tracked model with its current input and output token prices

Rank models for a use case such as coding, reasoning, long-context, or prompt-caching

Pull published benchmark scores keyed by model id, with null where none exists

Track recorded price changes over time from the price history feed

GET STARTED

Start building with Model Price Watch API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Model Price Watch API for, with concrete tasks.

★ Agent Model Selection Through Jentic

An AI agent choosing which model to route a task to fetches the ranked list for that use case, such as reasoning or cheap-api, and compares current token prices before deciding. Through Jentic the agent finds the best-for operation by intent and calls it with the intent slug, using a stored key if you have one to lift the daily request limit.

Get the ranked models for intent 'coding', then look up each model's current token prices in the models feed and return the three cheapest

LLM Cost Tracking and Budgeting

Finance and platform teams track what inference costs by pulling the price history and changelog feeds to see when providers cut or raised prices, and quote the LLM Price Index when reporting cost trends. Prices are in USD per 1,000,000 tokens, so cost estimates multiply directly by token volume.

Fetch the price history feed and list every recorded price change, then fetch the LLM Price Index and report its value and change

Pricing Pages and Comparison Content

Publishers and tool builders embed current model prices and benchmark scores in comparison tables, citing Model Price Watch with attribution as the free tier allows. The stats feed supplies headline figures such as total models, total providers, open-source and multimodal model counts, and the cheapest input and output prices.

Fetch the stats feed and return total_models, total_providers, cheapest_input, and largest_context

Key Endpoints

10 endpoints — the model price watch api serves read-only json feeds of llm token prices, with every price traced to the provider's official pricing page and re-verified on a schedule.

METHOD

PATH

DESCRIPTION

GET

/api/v1/models.json

Every tracked model with current token prices

GET

/api/v1/best-for/{intent}.json

Ranked models for a use case

GET

/api/v1/benchmarks.json

Benchmark scores keyed by model id

GET

/api/v1/price-history.json

Recorded price changes over time

GET

/api/v1/price-index.json

The LLM Price Index

GET

/api/v1/stats.json

Headline counts and extremes for the dataset

GET

/api/v1/models.json

Every tracked model with current token prices

GET

/api/v1/best-for/{intent}.json

Ranked models for a use case

GET

/api/v1/benchmarks.json

Benchmark scores keyed by model id

GET

/api/v1/price-history.json

Recorded price changes over time

GET

/api/v1/price-index.json

The LLM Price Index

GET

/api/v1/stats.json

Headline counts and extremes for the dataset

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring Model Price Watch by hand is light: the feeds are plain JSON GETs and the X-API-Key header is optional. Through Jentic you install once, import Model Price Watch from the API Directory, optionally store a key once to lift the daily limit, and your agent calls it alongside the model APIs it is choosing between.

Permission scoping

Model Price Watch puts the use-case slug in the URL path (/api/v1/best-for/{intent}.json), so a rule can pin your agent to one ranking such as coding. Every operation is a read, so scoping here is about keeping the agent on the feeds it needs.

Credential isolation

If you use a Model Price Watch key, it is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'compare LLM token prices' or 'find the best model for coding', and Jentic returns the matching Model Price Watch operation with its input schema so the agent calls the right feed without reading the reference docs.

FAQs

Specific to using Model Price Watch API through Jentic.

What authentication does the Model Price Watch API use?

Per its OpenAPI spec, authentication is optional: every operation works with no credential on the free tier, and an API key sent in the `X-API-Key` header raises the daily request limit. The health check never requires a key. If you use a key, Jentic stores it once, encrypted, in your own Jentic One instance and injects it at call time.

Can I find the best model for a specific use case with the Model Price Watch API?

Yes. The best-for operation takes a use-case slug in the path, such as coding, chatbot, cheap-api, prompt-caching, long-context, multimodal, reasoning, open-source, fast-inference, embedding, or enterprise, and returns ranked models for it.

Can I limit what my agent is allowed to do with the Model Price Watch API?

Yes. Every operation is a read, so the useful limit is on which feeds the agent may call: for example, write a rule that allows only the models and best-for operations so the agent sticks to price lookups and rankings, and every call it makes is logged.

What are the rate limits for the Model Price Watch API?

The OpenAPI spec says the free tier needs no key and that adding a key raises the daily request limit, but does not state the numbers; see https://modelpricewatch.com/api-pricing/ for current limits. The health check is not rate limited.

How do I compare LLM prices with Model Price Watch through Jentic?

Search Jentic for the intent 'compare LLM token prices', which returns the Model Price Watch models operation on /api/v1/models.json; the agent calls it and compares current prices in USD per 1,000,000 tokens. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Is there a Model Price Watch MCP server?

You don't need an MCP server to give your agent the Model Price Watch API. Jentic connects it directly from the API Directory: import it, optionally store a key once, and your agent calls the price, benchmark, and index feeds. Operations are discovered on demand, so no extra tool definitions are loaded into the agent's context.

Model Price Watch API

AI ReadinessFoundational (C+)60/100

See full scorecard

Official vendor OpenAPI document · agent-readyAnalyticsBusiness IntelligenceapiKey (optional)10 EndpointsREST

For Agents

Look up current LLM token prices, price history, benchmark scores, the LLM Price Index, and ranked models for use cases such as coding or long-context with Model Price Watch. Free tier needs no key; an optional X-API-Key raises the daily limit.

Use for: Find the cheapest model for coding tasks, Get current per-token prices for every tracked LLM, List all price changes recorded this month, Retrieve the latest LLM Price Index value with its citation

Not supported: Does not run model inference, route requests to providers, or track your own token spend. Use for reading published LLM price, benchmark, and price-index data only.

The Model Price Watch API serves read-only JSON feeds of LLM token prices, with every price traced to the provider's official pricing page and re-verified on a schedule. It returns every tracked model with its current prices in USD per 1,000,000 tokens, the list of providers, published benchmark scores keyed by model id, recorded price changes over time, and a dated changelog of model and price updates. It also publishes the LLM Price Index, a single citeable figure for the cost of frontier inference, broken out by capability tier, and ranked model lists for use cases such as coding, long-context, reasoning, and embedding.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: C-Signals: 4

97%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

10%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: B-Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

50%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: D+Signals: 4

46%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

52%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: C-Signals: 1

50%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: A-Signals: 1

70%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Model Price Watch API to your agent

Capabilities

Start building with Model Price Watch API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Model Price Watch API

Jentic AI Readiness Score

Model Price Watch API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness