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AI Leaderboard

作者 路多辛 · GitHub ↗ · v1.20.1 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install ai-leaderboard
功能描述
Comprehensive AI leaderboard for LLM models and AI applications. Query model rankings, model IDs, and pricing from OpenRouter, Artificial Analysis, and Pinch...
使用说明 (SKILL.md)

AI Rankings Leaderboard Skill

Description

A comprehensive skill for querying AI model and application rankings from multiple authoritative sources. Get the latest insights on LLM performance, popularity, pricing, and value metrics.

Data Sources

Source URL Focus
Artificial Analysis https://artificialanalysis.ai/ Intelligence Index, Speed, Price benchmarks
LLM Leaderboard https://artificialanalysis.ai/leaderboards/models Model comparison (100+ models)
LLM API Providers https://artificialanalysis.ai/leaderboards/providers API Provider comparison (500+ endpoints)
Image & Video Leaderboards https://artificialanalysis.ai/ (Image & Video section) Image/Video model ELO rankings
OpenRouter Rankings https://openrouter.ai/rankings Model usage & popularity
OpenRouter Apps https://openrouter.ai/apps AI applications ranking
OpenRouter Models https://openrouter.ai/models All available models with pricing
OpenRouter Free Models https://openrouter.ai/models?q=free Free models only
Pinchbench https://pinchbench.com/ Model benchmark (Success Rate, Speed, Cost, Value)

Features

1. Artificial Analysis LLM Leaderboard

Intelligence Index (智力指数)

  • Artificial Analysis Intelligence Index v4.0: Comprehensive model intelligence score
  • 10 evaluation dimensions: Multiple independent assessment criteria
  • Frontier Models: Top intelligence models (Gemini 3.1 Pro, GPT-5.4, Claude Opus 4.6, etc.)
  • Reasoning Models: Identifies models with reasoning capabilities

Artificial Analysis Coding Index (编程能力指数)

Artificial Analysis Agentic Index (智能体能力指数)

Performance Metrics

Metric Description
Intelligence Index Overall model intelligence score (higher is better)
Speed Output tokens per second (tokens/s)
Blended Price Combined USD per million tokens (3:1 input/output ratio)
Input Price Price per million input tokens (USD)
Output Price Price per million output tokens (USD)
Latency (TTFT) Time to First Token in seconds
Context Window Maximum context length supported

Model Comparison Table Columns

Column Description
Features Model features (reasoning badge, etc.)
Model Model name with logo
Context Window Max context length
Creator Provider/Company
Intelligence Index AI intelligence score
Blended USD/1M Tokens Combined input/output price
Median Tokens/s Median output speed
Latency First Chunk (s) Time to first token
Further Analysis Link to detailed analysis

Filters Available

Filter Options
Frontier Models On/Off
Open Weights On/Off (开源权重模型)
Size Class Small, Medium, Large, etc.
Reasoning On/Off (推理模型筛选)
Model Status Current, Preview, Discontinued

2. Artificial Analysis LLM API Providers Leaderboard

Comparison of 500+ AI Model Endpoints

Column Description
API Provider Provider name (Cerebras, Groq, Fireworks, etc.)
Model Model name
Context Window Max context length
License Model license
Intelligence Index Model intelligence score
Blended USD/1M Tokens Combined price
Median Tokens/s Output speed
Median First Chunk (s) Latency (TTFT)
Total Response (s) End-to-end response time
Reasoning Time (s) Reasoning model computation time
End-to-End Response Time Full request-response cycle

Key Providers

  • Cerebras
  • Eigen AI
  • Fireworks
  • SambaNova
  • Together.ai
  • Hyperbolic
  • Nebius Fast
  • Google Vertex
  • Groq
  • Azure OpenAI
  • AWS Bedrock
  • OpenAI Direct
  • Anthropic Direct
  • And 10+ more...

3. Artificial Analysis Image & Video Leaderboards

Text-to-Image Leaderboard

  • ELO scores from blind preference votes
  • 95% confidence intervals displayed
  • Top models: GPT Image 1.5, Imagen 4 Ultra, Gemini Image models, etc.

Video Leaderboards

Category Description
Text to Video (with Audio) Text generates video with sound
Text to Video (without Audio) Text generates silent video
Image to Video (with Audio) Image + text generates video with sound
Image to Video (without Audio) Image + text generates silent video
Image Editing Edit existing images with AI

Evaluation Method

  • ELO scoring system (blind preference voting)
  • 95% confidence intervals
  • Real user preference data

4. OpenRouter Model Rankings

  • LLM Leaderboard: Overall model usage rankings
  • Market Share: Market share by model provider
  • Categories: Rankings by use case
  • Languages: Natural language support rankings
  • Programming: Programming language support
  • Context Length: Long context handling
  • Tool Calls: Tool calling capabilities
  • Images: Image processing volume

5. OpenRouter App Rankings

  • Most Popular: Top apps by token usage
  • Trending: Fastest growing apps this week
  • Categories: Coding Agents, Productivity, Creative, Entertainment

6. OpenRouter Model Catalog

  • All Models: Complete list of available models on OpenRouter
  • Free Models: Models with $0 pricing (free to use)
  • Model ID: The exact model parameter to use when calling OpenRouter API
  • Pricing Info: Input/output token pricing

7. Pinchbench Benchmarks

  • Success Rate: Task completion success percentage
  • Speed: Response time performance
  • Cost: Cost per run analysis
  • Value: Price-performance ratio

Trigger Keywords

General AI Rankings

  • "AI rankings" / "AI 排行榜"
  • "LLM leaderboard" / "LLM 排行"
  • "model comparison" / "模型对比"
  • "best AI models" / "最好的 AI 模型"
  • "AI apps ranking" / "AI 应用排行"
  • "model benchmark" / "模型评测"

Artificial Analysis Specific

  • "Artificial Analysis" / "artificialanalysis"
  • "AI intelligence index" / "AI 智力指数"
  • "intelligence index" / "智力指数"
  • "模型速度排行" / "speed ranking"
  • "模型价格对比" / "price comparison"
  • "fastest models" / "最快模型"
  • "cheapest models" / "最便宜模型"
  • "tokens per second" / "t/s" / "tokens/s"
  • "latency" / "TTFT" / "首 token 延迟"
  • "Artificial Analysis Intelligence Index"
  • "AAII" / "AA Intelligence"
  • "API providers" / "API 提供商"
  • "LLM providers" / "LLM 提供商"
  • "Cerebras" / "Groq" / "Fireworks"
  • "open weights" / "开源权重"
  • "reasoning models" / "推理模型"
  • "elo score" / "ELO 评分"
  • "image arena" / "图生图"
  • "text to image" / "文生图"
  • "text to video" / "文生视频"
  • "image to video" / "图生视频"

OpenRouter Specific

  • "free models" / "免费模型" / "free AI models"
  • "OpenRouter models" / "OpenRouter 免费模型"
  • "OpenRouter rankings" / "OpenRouter 排行"
  • "Pinchbench"
  • "OpenRouter model ID" / "OpenRouter 模型 ID"
  • "查找 OpenRouter" / "OpenRouter 上的模型"
  • "model ID for [模型名]" / "[模型名] model ID"
  • "OpenRouter 上 [模型名]" / "OpenRouter [模型名] 模型"
  • "OpenRouter model parameter"
  • "调用量排行" / "使用量排行" / "top models" / "top 模型"
  • "OpenRouter 调用量" / "OpenRouter 使用量"

Runtime Tools

This skill requires:

  • execute_command: Execute shell commands and scripts
  • use_skill: Load browser-automation skill for JavaScript-rendered pages
  • web_fetch: Fallback for simple HTTP requests

Installation

Required CLI Dependency: agent-browser

The agent-browser CLI must be installed before using this skill. Install via:

npm install -g agent-browser
# or
npx agent-browser --version

This skill calls agent-browser via subprocess with hardcoded argument arrays (no shell injection risk).

Note on browser eval: The agent-browser eval command executes document.body.innerText or similar DOM queries on the remote page to extract rendered content. This is standard web scraping behavior for JavaScript-rendered pages and is limited to reading page content only.

Browser Automation Support

For JavaScript-rendered pages (OpenRouter Rankings, Artificial Analysis), this skill uses browser automation:

  1. Load browser-automation skill first:

    use_skill("browser-automation")
    
  2. Navigate to rankings page:

    agent-browser open "https://artificialanalysis.ai/leaderboards/models"
    agent-browser wait --load networkidle
    agent-browser eval "document.body.innerText"
    
  3. Key pages requiring browser:

    • https://artificialanalysis.ai/leaderboards/models - LLM comparison (100+ models)
    • https://artificialanalysis.ai/leaderboards/providers - API providers (500+ endpoints)
    • https://artificialanalysis.ai/ - Image & Video leaderboards
    • https://openrouter.ai/rankings - Model usage rankings (JS rendered)
    • https://openrouter.ai/apps - App rankings (JS rendered)

Artificial Analysis Page Structure

LLM Leaderboard Page (/leaderboards/models):

LLM Leaderboard - Comparison of over 100 AI models
├── HIGHLIGHTS section
│   ├── Intelligence: Gemini 3.1 Pro Preview, GPT-5.4 (xhigh)
│   ├── Speed: Mercury 2 (943 t/s), NVIDIA Nemotron 3 Super (462 t/s)
│   └── Price: Gemma 3n E4B (cheapest)
├── Filters:
│   ├── Frontier Models | Open Weights | Size Class | Reasoning | Model Status
├── Comparison table columns:
│   ├── Features | Model | Context Window | Creator
│   ├── Intelligence Index | Blended USD/1M | Median Tokens/s | Latency
│   └── Further Analysis
└── Key definitions (expandable)
    ├── Context window
    ├── Output Speed (tokens/s)
    ├── Latency (Time to First Token)
    ├── Price (3:1 blended)
    ├── Output Price
    └── Input Price

LLM API Providers Page (/leaderboards/providers):

LLM API Providers Leaderboard - 500+ endpoints
├── Filters (same as LLM Leaderboard)
├── Comparison table columns:
│   ├── API Provider | Model | Context Window | License
│   ├── Intelligence Index | Blended USD/1M | Median Tokens/s
│   ├── Median First Chunk (s) | Total Response (s) | Reasoning Time (s)
│   └── Further Analysis
└── 24+ Providers: Cerebras, Groq, Fireworks, SambaNova, etc.

Image & Video Leaderboards (on homepage):

Image & Video Leaderboards
├── Tabs:
│   ├── Text to Image (ELO scores, 95% CI)
│   ├── Image Editing
│   ├── Text to Video (with Audio)
│   ├── Text to Video (without Audio)
│   ├── Image to Video (with Audio)
│   └── Image to Video (without Audio)
└── Top models with ELO rankings

OpenRouter Page Structure (Reminder)

OpenRouter Rankings Page (/rankings):

https://openrouter.ai/rankings
├── Top Models (chart header)
├── LLM Leaderboard ← THIS is the usage ranking (parse this!)
│   ├── 1. MiniMax M2.5 (1.75T tokens)
│   ├── 2. Step 3.5 Flash (1.34T tokens)
│   └── [Show more] button
├── Market Share (different metric - don't mix!)
└── ...

Usage Examples

Query Artificial Analysis Intelligence Index

User: "What are the top models on Artificial Analysis Intelligence Index?"
-> Fetches Artificial Analysis LLM Leaderboard and displays top models by intelligence

Query Model Speed Rankings

User: "Which AI models are the fastest in terms of output speed?"
-> Fetches Artificial Analysis data and lists models by tokens/second

Query API Providers

User: "Compare LLM API providers like Cerebras and Groq"
-> Fetches Artificial Analysis Providers Leaderboard and compares speed/price

Query Image/Video Models

User: "What are the best text-to-image models?"
-> Fetches Artificial Analysis Image Arena leaderboard with ELO scores

Query Model Rankings (OpenRouter)

User: "What are the top 10 AI models right now?"
-> Fetches OpenRouter rankings and displays top models with usage stats

Query Free Models

User: "What free models are available on OpenRouter?"
-> Fetches https://openrouter.ai/models?q=free and lists all free models with their model IDs

Get Model ID for API Calls

User: "What's the model ID for GPT-4o on OpenRouter?"
-> Fetches https://openrouter.ai/models and returns the exact model parameter to use

Compare Model Performance

User: "Compare GPT-4 and Claude on Pinchbench"
-> Fetches Pinchbench data and compares success rate, speed, cost

Output Format

Artificial Analysis Intelligence Index

==================================================
    Artificial Analysis Intelligence Index
==================================================

Top 10 Models by Intelligence:

| Rank | Model | Intelligence | Speed (t/s) | Price ($/M) |
|------|-------|--------------|-------------|-------------|
| 1 | Gemini 3.1 Pro Preview | 57 | ~50 | $1.25 |
| 2 | GPT-5.4 (xhigh) | 57 | ~60 | $15.00 |
| 3 | Claude Opus 4.6 (max) | 53 | ~80 | $18.00 |
| 4 | Claude Sonnet 4.6 (max) | 52 | ~85 | $4.50 |
| 5 | GLM-5 | 50 | ~45 | $0.50 |
...

Fastest Models: Mercury 2 (943 t/s), NVIDIA Nemotron 3 Super (462 t/s)
Best Price: Gemma 3n E4B, Granite 4.0 H Small

Data Source: Artificial Analysis (artificialanalysis.ai)
==================================================

API Providers Comparison

==================================================
    LLM API Providers Leaderboard
==================================================

| Provider | Model | Speed (t/s) | Price ($/M) | Latency (s) |
|----------|-------|-------------|-------------|-------------|
| Cerebras | Llama 3.1 70B | 2143 | $0.12 | 0.08 |
| Groq | Llama 3.1 70B | 943 | $0.59 | 0.15 |
| Fireworks | Llama 3.1 70B | 562 | $0.90 | 0.22 |
...

Data Source: Artificial Analysis Providers
==================================================

Image Arena (ELO Rankings)

==================================================
    Text-to-Image Leaderboard (ELO)
==================================================

| Rank | Model | ELO Score | 95% CI |
|------|-------|-----------|--------|
| 1 | GPT Image 1.5 (high) | 1342 | ±12 |
| 2 | Imagen 4 Ultra | 1289 | ±15 |
| 3 | Gemini 3.1 Flash Image | 1245 | ±18 |
...

Data Source: Artificial Analysis Image Arena
==================================================

OpenRouter Model Rankings

==================================================
    AI Model Rankings (OpenRouter)
==================================================

Top 10 Models by Usage:

| Rank | Model | Provider | Tokens | Growth |
|------|-------|----------|--------|--------|
| 1 | MiniMax M2.5 | minimax | 1.75T | +15% |
| 2 | Step 3.5 Flash | step | 1.34T | +22% |
...

Data Source: OpenRouter (Weekly Rankings)
==================================================

Free Models List

==================================================
    Free Models on OpenRouter
==================================================

| Model Name | Model ID (for API) | Context |
|------------|-------------------|---------|
| GPT-4o Mini | openai/gpt-4o-mini | 128K |
| Llama 3.3 70B | meta-llama/llama-3.3-70b-instruct | 128K |
| DeepSeek V3 | deepseek/deepseek-chat | 64K |
...

💡 Usage: Set model parameter to the Model ID value
   Example: model="openai/gpt-4o-mini"

Data Source: OpenRouter Models
==================================================

Execution Instructions

Method 1: Browser Automation for Rankings (Recommended)

Artificial Analysis and OpenRouter rankings pages require JavaScript rendering:

# Step 1: Load browser-automation skill (REQUIRED)
use_skill("browser-automation")

# Step 2: Navigate to Artificial Analysis LLM Leaderboard
agent-browser open "https://artificialanalysis.ai/leaderboards/models"
agent-browser wait --load networkidle

# Step 3: Wait for content to load, then extract
agent-browser wait 3000
agent-browser eval "document.body.innerText"

# Step 4: Close browser when done
agent-browser close

Method 2: Python Script for OpenRouter Model Catalog

Use the query_leaderboard.py script to fetch model data via OpenRouter API (no JavaScript needed):

# List free models
python3 "${SKILL_DIR}/query_leaderboard.py --free"

# Search models by name
python3 "${SKILL_DIR}/query_leaderboard.py -s glm"
python3 "${SKILL_DIR}/query_leaderboard.py -s gpt"

# Get specific model info
python3 "${SKILL_DIR}/query_leaderboard.py --id openai/gpt-4o"

# List all models with limit
python3 "${SKILL_DIR}/query_leaderboard.py --all --limit 50"

Method 3: Web Fetch (Fallback)

When browser/Python is not available, use web_fetch:

  1. For Artificial Analysis: Fetch https://artificialanalysis.ai/leaderboards/models
  2. For OpenRouter model catalog: Use OpenRouter API https://openrouter.ai/api/v1/models
  3. For benchmarks: Fetch https://pinchbench.com/

Note: Rankings pages require JavaScript rendering - use browser automation (Method 1).

Notes

  • Data is updated regularly (Artificial Analysis, OpenRouter weekly, Pinchbench near real-time)
  • Artificial Analysis Intelligence Index is based on 10 independent evaluations
  • ELO scores are from blind preference voting with 95% confidence intervals
  • Pinchbench disclaimer: "For entertainment purposes only, should not be relied upon for critical decisions"
  • Rankings reflect actual usage data from millions of users
  • Free models have $0.00 pricing on OpenRouter
  • Model ID format: Use the exact string (e.g., openai/gpt-4o-mini) as the model parameter in API calls

Artificial Analysis API Patterns

Based on observed page structure, Artificial Analysis provides:

Example model providers API:

/models/gpt-oss-120b/providers
/models/gemini-3-1-pro-preview/providers
/models/claude-opus-4-6-adaptive/providers

OpenRouter API Usage

When calling OpenRouter API (for chat completions), use the Model ID. Note: This skill's scripts (fetch_rankings.py, query_leaderboard.py) only read public leaderboard data and do NOT require API authentication.

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",  # \x3C- Model ID from this skill
    "messages": [{"role": "user", "content": "Hello"}]
  }'
安全使用建议
Before installing: 1) Confirm you have or are willing to install the agent-browser CLI (SKILL.md and fetch_rankings.py depend on it) — the registry metadata currently omits this requirement. 2) Review the bundled Python files locally (fetch_rankings.py and query_leaderboard.py) for completeness and any hidden network endpoints; the provided fetch_rankings.py snippet appears truncated/incomplete in places which may cause runtime errors. 3) Run the scripts in a controlled environment (sandbox or with network monitoring) the first time to verify they only contact the listed public sites. 4) Do not provide any API keys unless you audit where they will be used; the skill does not require secrets but shows an example curl that references $OPENROUTER_API_KEY. 5) If you expect the skill to work out-of-the-box, ask the publisher to update the registry metadata to include agent-browser as a required binary and to confirm the code is complete and maintained.
功能分析
Type: OpenClaw Skill Name: ai-leaderboard Version: 1.20.1 The AI Rankings Leaderboard skill is a legitimate tool designed to fetch and display LLM performance data from authoritative sources like Artificial Analysis and OpenRouter. The included Python scripts (fetch_rankings.py and query_leaderboard.py) use safe subprocess handling (shell=False) and standard web requests to retrieve public data. No evidence of data exfiltration, malicious prompt injection, or unauthorized execution was found; the browser automation logic is strictly limited to scraping rendered page content for the stated purpose.
能力评估
Purpose & Capability
The name/description and included scripts clearly aim to scrape/query public leaderboard sites (Artificial Analysis, OpenRouter, Pinchbench). That purpose justifies network access and browser automation. However, SKILL.md declares a cli_dependency on agent-browser while the registry metadata lists no required binaries; this mismatch is unexpected and should be reconciled.
Instruction Scope
Runtime instructions and the Python scripts limit actions to visiting public leaderboard pages and calling public OpenRouter APIs. They do not request local credentials or read unrelated system files in the provided content. The fetch_rankings script relies on page DOM evaluation and snapshots (document.body.innerText) which can expose whatever text appears on those pages, but that is consistent with the stated goal. One concern: the SKILL.md and code mention "run after loading browser-automation skill" (a cross-skill dependency) which is not otherwise documented in metadata.
Install Mechanism
There is no install spec (instruction-only), so nothing is fetched or run at install time. The code files are bundled with the skill; runtime will call local 'agent-browser' if present. Absence of an install step reduces supply-chain risk, but also means the skill silently depends on an external CLI being present.
Credentials
The skill declares no required environment variables or credentials. The query_leaderboard script includes a documentation example showing how to call OpenRouter with an API key, but the code itself fetches only public endpoints and does not require secrets. No unrelated credentials or config paths are requested.
Persistence & Privilege
The skill does not request always:true and does not claim to modify other skills or system settings. It appears to be regular, opt-in functionality with normal autonomous invocation allowed (the platform default).
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install ai-leaderboard
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-leaderboard 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.20.1
- Added agent-browser as a CLI dependency in SKILL.md for improved capabilities. - Updated version to 1.20.1 in metadata. - No changes to features, data sources, or usage; documentation now specifies the required dependency.
v1.20.0
No file changes detected in this version. - No new features, bug fixes, or updates were introduced. - The skill version remains at 1.9.0 in the documentation, with no modifications to functionality or content.
v1.9.0
No file changes detected for version 1.9.0. - No updates or changes were introduced in this version.
v1.0.0
**Major update: Adds Artificial Analysis as a primary data source and expands ranking features.** - Integrated Artificial Analysis leaderboards for Intelligence Index, Coding Index, and Agentic Index. - Added detailed support for model speed, pricing, intelligence scores, and provider comparison from Artificial Analysis. - Expanded trigger keywords to cover Artificial Analysis metrics and Chinese phrases. - Updated data sources to include image & video model leaderboards and 500+ API provider benchmarks. - Enhanced feature descriptions for LLM, image, and video model evaluation. - Incremented version to 1.9.0.
v1.7.0
Version 1.7.0 - Improved handling and documentation of the "Show more" button for full leaderboard extraction on OpenRouter rankings, including robust JavaScript browser automation instructions. - Clarified and emphasized correct parsing of the "LLM Leaderboard" section to avoid data contamination from other ranking sections. - Updated SKILL.md usage instructions and parsing caveats for greater accuracy and developer clarity.
v1.6.0
**Changelog for ai-leaderboard v1.6.0** - Added instructions for accurately parsing only the "LLM Leaderboard" section on the OpenRouter rankings page to avoid mixing in unrelated data. - Provided explicit guidance on handling multiple sections in the page text and stopping parsing at the next section header (e.g., "Market Share"). - Clarified parsing patterns for model ranking (rank, model name, provider, tokens, growth). - Included warnings about common mistakes, such as mixing in market share or category data with leaderboard data. - No changes to APIs, data sources, or general functionality. Documentation and parsing accuracy improved.
v1.5.0
**AI Rankings Leaderboard v1.5.0 Changelog** - Added browser automation support for JavaScript-rendered pages (e.g., OpenRouter Rankings, App rankings). - Documented precise method for expanding model/app leaderboards via JavaScript click, including a robust "Show more" button script. - Updated runtime tools: now requires `use_skill` (browser-automation), `execute_command`, and `web_fetch` (as fallback). - Enhanced documentation with step-by-step browser automation guidance for fetching complete leaderboard data. - Added new field `display_name` for improved multilingual display. - Added new backend script: `fetch_rankings.py`.
v1.3.1
Version 1.3.1 - Expanded trigger keywords to improve detection, including additional Chinese phrases ("调用量排行", "使用量排行", "OpenRouter 调用量", "OpenRouter 使用量") and English variants ("top models", "top 模型", "OpenRouter 排行"). - No changes to functionality, features, or output—documentation update only.
v1.3.0
Version 1.3.0 – Adds Python script support for leaderboard queries - Introduced the query_leaderboard.py script for direct API access to OpenRouter model data (listing, searching, and filtering). - Updated SKILL.md with new Python-based instructions and clarified recommended/alternate data retrieval methods. - Improved guidance for fetching model catalog and free model lists using the Python script or API. - Web-fetch fallback now recommended only when script/API unavailable. - No changes to trigger words, output formats, or core features.
v1.2.0
AI Rankings Leaderboard 1.2.0 - Expanded multilingual trigger keyword support (Chinese-language triggers now included) - Refined and clarified feature documentation, usage examples, and output formats - Improved OpenRouter and Pinchbench data coverage details for model/app rankings and benchmarks - Highlighted importance and examples of Model ID parameter for API integration - Enhanced guidance for fetching, formatting, and interpreting ranking data from all sources
元数据
Slug ai-leaderboard
版本 1.20.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 10
常见问题

AI Leaderboard 是什么?

Comprehensive AI leaderboard for LLM models and AI applications. Query model rankings, model IDs, and pricing from OpenRouter, Artificial Analysis, and Pinch... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 333 次。

如何安装 AI Leaderboard?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install ai-leaderboard」即可一键安装,无需额外配置。

AI Leaderboard 是免费的吗?

是的,AI Leaderboard 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

AI Leaderboard 支持哪些平台?

AI Leaderboard 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 AI Leaderboard?

由 路多辛(@luduoxin)开发并维护,当前版本 v1.20.1。

💬 留言讨论