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Real-time token pricing for 23 AI models across 8 providers (Anthropic, OpenAI, Google, DeepSeek, Zhipu, MiniMax, Moonshot, Alibaba). Built for agents — not just humans.

Features

Price Intelligence

  • Real-time pricing table with sorting, filtering by provider/category/open-source

  • Token cost calculator — input your usage, see daily/monthly costs across all models

  • Price evolution timeline — from GPT-4 ($30/MTok) to DeepSeek V3 ($0.3/MTok)

Agent-First Design

  • MCP Server — 5 tools for price queries, model comparison, smart recommendations

  • Token Tracker — 5 tools for consumption logging, budget management, efficiency analysis

  • REST API — 12 endpoints for programmatic access

  • agents.txt — Machine-readable service description at /agents.txt

Bilingual — Chinese / English with automatic RMB / USD currency conversion

Quick Start

git clone https://github.com/LangGPT/ohmytoken.git
cd ohmytoken
npm install
npm run build
npm run dev        # → http://localhost:18090

MCP Server

Add to your Claude Code settings.json:

{
  "mcpServers": {
    "ohmytoken": {
      "command": "npx",
      "args": ["tsx", "src/mcp/index.ts"]
    }
  }
}

Tools:

Tool

Description

get_token_price

Get real-time pricing for any model or provider

compare_models

Side-by-side comparison of 2+ models

recommend_model

Smart recommendation by task, budget, preferences

calculate_cost

Estimate costs for a workload across all models

get_price_trends

Historical token price trends since 2023

Token Tracker

Every agent deserves a financial dashboard. Track your token spending like wandb tracks experiments.

{
  "mcpServers": {
    "ohmytoken-tracker": {
      "command": "npx",
      "args": ["tsx", "src/mcp/tracker.ts"]
    }
  }
}

Tools:

Tool

Description

token_log

Record token consumption (cost auto-calculated)

token_usage

Query spending by period, model, or task

token_budget

Set daily/monthly limits with auto-warnings

token_analyze

Spending patterns and optimization suggestions

token_leaderboard

Multi-agent ranking — who's most efficient?

Data stored in ~/.ohmytoken/ledger.jsonl (append-only, portable).

REST API

Base URL: https://www.ohmytoken.com/api (or http://localhost:18090/api)

# All models
curl /api/models?provider=anthropic

# Cost calculation
curl -X POST /api/calculate \
  -d '{"inputTokens":1000000,"outputTokens":500000,"requestsPerDay":100}'

# Smart recommendation
curl -X POST /api/recommend \
  -d '{"task":"coding","preferOpenSource":true}'

# Log agent token usage
curl -X POST /api/tracker/log \
  -d '{"agent_id":"my-agent","model":"claude-opus-4-6","input_tokens":5000,"output_tokens":1000}'

# Agent leaderboard
curl /api/tracker/leaderboard

Models Covered

Provider

Models

Price Range (Input $/MTok)

Anthropic

Opus 4.6, Sonnet 4.5, Haiku 4.5

$1 — $5

OpenAI

GPT-5.2, GPT-4.1, o3, o4-mini

$0.1 — $2

Google

Gemini 2.5 Pro, 2.5 Flash, 2.0 Flash

$0.1 — $1.25

DeepSeek

V3, R1

$0.3 — $0.7

Zhipu

GLM-5, GLM-5 Code, GLM-4.7

$0.07 — $1.2

MiniMax

M2.1

$0.3

Moonshot

Kimi K2, Kimi K2.5

$0.6

Alibaba

Qwen3-Max, QwQ-Plus

$0.8 — $1.2

Project Structure

ohmytoken/
├── src/
│   ├── client/          # React SPA (bilingual, light theme)
│   │   ├── components/  # PriceTable, Calculator, Timeline, AgentDashboard, ...
│   │   ├── i18n.ts      # zh/en translations, RMB/USD conversion
│   │   └── styles.css   # Light theme with glass morphism
│   ├── server/          # Express API server (port 18090)
│   ├── mcp/
│   │   ├── index.ts     # Price intelligence MCP server
│   │   └── tracker.ts   # Token consumption tracker MCP server
│   └── shared/          # Types, data, tracker storage
├── docs/                # GitHub Pages static build
├── public/              # Static assets (index.html, agents.txt)
└── build.js             # esbuild bundler

License

MIT

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