ComparEdge LLM Cost

Name: ComparEdge LLM Cost
Author: comparedge

by comparedgeversion 1.0.0i★ –iRuns on your computer

Registry nameio.github.imkemit-ops/comparedge-llm-cost

Install

Overview

Description from the maker (original English)

Token cost math for LLM API calls: verified per-1M-token rates for 69 models, 17 providers.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi0 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 0 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "comparedge-llm-cost": {
      "command": "npx",
      "args": [
        "-y",
        "@comparedge/llm-cost-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add comparedge-llm-cost -- npx -y @comparedge/llm-cost-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "comparedge-llm-cost": {
      "command": "npx",
      "args": [
        "-y",
        "@comparedge/llm-cost-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "comparedge-llm-cost": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@comparedge/llm-cost-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

! Caution
Maker matches the repo
The publisher (imkemit-ops) is not the repo owner (comparedge); someone else may have listed it
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7277 servers in this category
Published to the registry more recently than 21% of the other 7277 servers in this category
Basic checks equal to or better than 30% of the other 7277 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is ComparEdge LLM Cost safe?

It passed 0 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Someone asks what the AI feature will cost at scale, and the honest answer around most teams is a shrug. Rates moved twice since anyone last checked, and the model itself will happily quote prices from its training data. This MCP server keeps current per-million-token rates for 69 models where your assistant can reach them, and does the arithmetic itself.

Watch it work

$ You: price Claude Opus 4.8 on a 25k-token prompt with a 1k answer, run 5,000 times

llm-cost › estimate_cost

Cost estimate: Claude Opus 4.8 (Anthropic) Rates: input $5/1M, output $25/1M, cached input $0.5/1M

Per call (25,000 in + 1,000 out tokens): input: $0.1250 output: $0.0250 per call total: $0.1500

Across 5,000 calls: $750.00

Ways to pay less for the same 5,000 calls: with cached input: $187.50 via batch API: $375.00

Nothing here is rounded or guessed. The rate is verified, date-stamped, and the server multiplied.

Open a second session: same call across four models, ranked

$ You: compare that call on Opus 4.8, Sonnet 5, GPT-5.6 Terra and Gemini 3.1 Pro

llm-cost › compare_models_cost

25,000 in + 1,000 out, cheapest first:

Claude Sonnet 5 $0.0600 /call $300.00 /5k
Gemini 3.1 Pro $0.0620 /call $310.00 /5k
GPT-5.6 Terra $0.0775 /call $387.50 /5k
Claude Opus 4.8 $0.1500 /call $750.00 /5k

Ranking uses each model's live feed entry, not remembered prices.

The gap it closes

An assistant on its own	An assistant with llm-cost
quotes output rates from training data, often a generation stale	reads the current rate, dated
flattens the estimate to "a few cents"	`$0.1500` per call, `$750.00` across 5,000
never mentions batch or caching discounts	`$375.00` batch, `$187.50` cached, only where the model really offers them
confidently wrong, no way to tell	every figure traces to a feed entry with a verification date

The same numbers answer in a browser through the LLM calculator, and the LLM category hub ranks every tracked model by rating and price.

Where the numbers travel

flowchart LR

V["provider pricing pages 17 providers"] --> CE["verification date-stamped checks"] CE --> F["model prices feed 69 models, USD per 1M tokens"] F -->|"6h cache, serve stale on failure"| MCP["llm-cost server local arithmetic"] MCP --> A["your agent"]

The server never calls a provider API. It reads one public feed, llms-model-prices.json, and computes locally. Nothing to rate-limit, no key to leak, and a network hiccup serves the last good copy instead of an error. How each price gets checked is written up in the methodology, and the catalog behind it ships as an open dataset under CC BY 4.0.

The six tools

Four do the math. Two help you find the exact model id the math wants.

Tool	Answers	Params
`estimate_cost`	one call, or N identical calls, in dollars	`model`, `input_tokens`, `output_tokens`, `calls?`
`compare_models_cost`	the same call priced across 2 to 6 models	`models[]`, `input_tokens`, `output_tokens`
`monthly_budget`	daily, monthly, yearly spend for a workload	`model`, `daily_calls`, `avg_input_tokens`, `avg_output_tokens`
`cheapest_models`	lowest-cost models, optional context floor	`min_context?`, `limit?`
`list_models`	every model with rates, context and tier	`provider?`
`list_providers`	providers with model counts and cheapest pick	none

Token rules of thumb, for when nobody knows the counts

A page of English prose is roughly 500 tokens; one token is about four characters.
Model references are forgiving: claude-opus-4-8, Opus 4.8 and anthropic/opus resolve to the same model. When the resolver is unsure, it returns candidates instead of guessing.
cheapest_models ranks by a blended rate weighting input to output 3 to 1, because real workloads read far more than they write. Confirm the winner with estimate_cost on your actual split.

Prompts

Prompt	Args	Runs
`estimate_my_workflow`	`workflow`, `model?`	token estimates plus per-run and monthly cost for a described workflow
`pick_cheapest_model`	`task`	cheapest model that still meets the requirement, top candidates priced
`forecast_ai_budget`	`model`, `usage`	monthly and yearly bill projected from expected volume

Wire it up

"mcpServers": { "llm-cost": { "command": "npx", "args": ["-y", "@comparedge/llm-cost-mcp@latest"] } } }

Claude Desktop keeps this file at ~/Library/Application Support/Claude/claude_desktop_config.json. Cursor: Settings, then MCP. VS Code with Copilot reads .vscode/mcp.json. Restart the client; six tools appear. No API key, no account. Per-client walkthroughs live in the setup guide.

Family

Built by ComparEdge, where software prices are checked against vendor pages before anyone quotes them. Two siblings share the data: the full catalog server and a price-change watcher, both on ComparEdge MCP.

MIT licensed. JSON-RPC 2.0 over stdio, standard Model Context Protocol.

ComparEdge LLM Cost

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Watch it work

The gap it closes

Where the numbers travel

The six tools

Prompts

Wire it up

Family