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ai-models

Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/alinaqi/maggy --skill ai-models
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads ai-models automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

AI Models Reference Skill

Last Updated: December 2025

Philosophy

Use the right model for the job. Bigger isn't always better - match model capabilities to task requirements. Consider cost, latency, and accuracy tradeoffs.

Model Selection Matrix

TaskRecommendedWhy
Complex reasoningClaude Opus 4.5, o3, Gemini 3 ProHighest accuracy
Fast chat/completionClaude Haiku, GPT-4.1 mini, Gemini FlashLow latency, cheap
Code generationClaude Sonnet 4.5, Codestral, GPT-4.1Strong coding
Vision/imagesClaude Sonnet, GPT-4o, Gemini 3 ProMultimodal
Embeddingstext-embedding-3-small, VoyageCost-effective
Voice synthesisEleven Labs v3, OpenAI TTSNatural sounding
Image generationFLUX.2, DALL-E 3, SD 3.5Different styles

Anthropic (Claude)

Documentation

Latest Models (December 2025)

const CLAUDE_MODELS = {
  // Flagship - highest capability
  opus: 'claude-opus-4-5-20251101',

  // Balanced - best for most tasks
  sonnet: 'claude-sonnet-4-5-20250929',

  // Previous generation (still excellent)
  opus4: 'claude-opus-4-6',
  sonnet4: 'claude-sonnet-5',

  // Fast & cheap - high volume tasks
  haiku: 'claude-haiku-4-5-20251001',
} as const;

Usage

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const response = await anthropic.messages.create({
  model: 'claude-sonnet-4-5-20250929',
  max_tokens: 1024,
  messages: [
    { role: 'user', content: 'Hello, Claude!' }
  ],
});

Model Selection

claude-opus-4-5-20251101 (Opus 4.5)
├── Best for: Complex analysis, research, nuanced writing
├── Context: 200K tokens
├── Cost: $5/$25 per 1M tokens (input/output)
└── Use when: Accuracy matters most

claude-sonnet-4-5-20250929 (Sonnet 4.5)
├── Best for: Code, general tasks, balanced performance
├── Context: 200K tokens
├── Cost: $3/$15 per 1M tokens
└── Use when: Default choice for most applications

claude-haiku-4-5-20251001 (Haiku 4.5)
├── Best for: Classification, extraction, high-volume
├── Context: 200K tokens
├── Cost: $1/$5 per 1M tokens
└── Use when: Speed and cost matter most

OpenAI

Documentation

Latest Models (December 2025)

const OPENAI_MODELS = {
  // GPT-5 series (latest)
  gpt5: 'gpt-5.2',
  gpt5Mini: 'gpt-5-mini',

  // GPT-4.1 series (recommended for most)
  gpt41: 'gpt-4.1',
  gpt41Mini: 'gpt-4.1-mini',
  gpt41Nano: 'gpt-4.1-nano',

  // Reasoning models (o-series)
  o3: 'o3',
  o3Pro: 'o3-pro',
  o4Mini: 'o4-mini',

  // Legacy but still useful
  gpt4o: 'gpt-4o',           // Still has audio support
  gpt4oMini: 'gpt-4o-mini',

  // Embeddings
  embeddingSmall: 'text-embedding-3-small',
  embeddingLarge: 'text-embedding-3-large',

  // Image generation
  dalle3: 'dall-e-3',
  gptImage: 'gpt-image-1',

  // Audio
  tts: 'tts-1',
  ttsHd: 'tts-1-hd',
  whisper: 'whisper-1',
} as const;

Usage

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

// Chat completion
const response = await openai.chat.completions.create({
  model: 'gpt-4.1',
  messages: [
    { role: 'user', content: 'Hello!' }
  ],
});

// With vision
const visionResponse = await openai.chat.completions.create({
  model: 'gpt-4.1',
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'What is in this image?' },
        { type: 'image_url', image_url: { url: 'https://...' } },
      ],
    },
  ],
});

// Embeddings
const embedding = await openai.embeddings.create({
  model: 'text-embedding-3-small',
  input: 'Your text here',
});

Model Selection

o3 / o3-pro
├── Best for: Math, coding, complex multi-step reasoning
├── Context: 200K tokens
├── Cost: Premium pricing
└── Use when: Hardest problems, need chain-of-thought

gpt-4.1
├── Best for: General tasks, coding, instruction following
├── Context: 1M tokens (!)
├── Cost: Lower than GPT-4o
└── Use when: Default choice, replaces GPT-4o

gpt-4.1-mini / gpt-4.1-nano
├── Best for: High-volume, cost-sensitive
├── Context: 1M tokens
├── Cost: Very low
└── Use when: Simple tasks at scale

o4-mini
├── Best for: Fast reasoning at low cost
├── Context: 200K tokens
├── Cost: Budget reasoning
└── Use when: Need reasoning but cost-conscious

Google (Gemini)

Documentation

Latest Models (December 2025)

const GEMINI_MODELS = {
  // Gemini 3 (Latest)
  gemini3Pro: 'gemini-3-pro-preview',
  gemini3ProImage: 'gemini-3-pro-image-preview',
  gemini3Flash: 'gemini-3-flash-preview',

  // Gemini 2.5 (Stable)
  gemini25Pro: 'gemini-2.5-pro',
  gemini25Flash: 'gemini-2.5-flash',
  gemini25FlashLite: 'gemini-2.5-flash-lite',

  // Specialized
  gemini25FlashTTS: 'gemini-2.5-flash-preview-tts',
  gemini25FlashAudio: 'gemini-2.5-flash-native-audio-preview-12-2025',

  // Previous generation
  gemini2Flash: 'gemini-2.0-flash',
} as const;

Usage

import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY);
const model = genAI.getGenerativeModel({ model: 'gemini-2.5-flash' });

const result = await model.generateContent('Hello!');
const response = result.response.text();

// With vision
const visionModel = genAI.getGenerativeModel({ model: 'gemini-2.5-pro' });
const imagePart = {
  inlineData: {
    data: base64Image,
    mimeType: 'image/jpeg',
  },
};
const result = await visionModel.generateContent(['Describe this:', imagePart]);

Model Selection

gemini-3-pro-preview
├── Best for: "Best model in the world for multimodal"
├── Context: 2M tokens
├── Cost: Premium
└── Use when: Need absolute best quality

gemini-2.5-pro
├── Best for: State-of-the-art thinking, complex tasks
├── Context: 2M tokens
├── Cost: $1.25/$5 per 1M tokens
└── Use when: Long context, complex reasoning

gemini-2.5-flash
├── Best for: Fast, balanced performance
├── Context: 1M tokens
├── Cost: $0.075/$0.30 per 1M tokens
└── Use when: Speed and cost matter

gemini-2.5-flash-lite
├── Best for: Ultra-fast, lowest cost
├── Context: 1M tokens
├── Cost: $0.04/$0.15 per 1M tokens
└── Use when: High volume, simple tasks

Eleven Labs (Voice)

Documentation

Latest Models (December 2025)

const ELEVENLABS_MODELS = {
  // Latest - highest quality (alpha)
  v3: 'eleven_v3',

  // Production ready
  multilingualV2: 'eleven_multilingual_v2',
  turboV2_5: 'eleven_turbo_v2_5',

  // Ultra-low latency
  flashV2_5: 'eleven_flash_v2_5',
  flashV2: 'eleven_flash_v2', // English only
} as const;

Usage

import { ElevenLabsClient } from 'elevenlabs';

const elevenlabs = new ElevenLabsClient({
  apiKey: process.env.ELEVENLABS_API_KEY,
});

// Text to speech
const audio = await elevenlabs.textToSpeech.convert('voice-id', {
  text: 'Hello, world!',
  model_id: 'eleven_turbo_v2_5',
  voice_settings: {
    stability: 0.5,
    similarity_boost: 0.75,
  },
});

// Stream audio (for real-time)
const audioStream = await elevenlabs.textToSpeech.convertAsStream('voice-id', {
  text: 'Streaming audio...',
  model_id: 'eleven_flash_v2_5',
});

Model Selection

eleven_v3 (Alpha)
├── Best for: Highest quality, emotional range
├── Latency: ~1s+ (not for real-time)
├── Languages: 74
└── Use when: Quality over speed, pre-rendered

eleven_turbo_v2_5
├── Best for: Balanced quality and speed
├── Latency: ~250-300ms
├── Languages: 32
└── Use when: Good quality with reasonable latency

eleven_flash_v2_5
├── Best for: Real-time, conversational AI
├── Latency: <75ms
├── Languages: 32
└── Use when: Live voice agents, chatbots

Replicate

Documentation

Popular Models (December 2025)

const REPLICATE_MODELS = {
  // FLUX.2 (Latest - November 2025)
  flux2Pro: 'black-forest-labs/flux-2-pro',
  flux2Flex: 'black-forest-labs/flux-2-flex',
  flux2Dev: 'black-forest-labs/flux-2-dev',

  // FLUX.1 (Still excellent)
  flux11Pro: 'black-forest-labs/flux-1.1-pro',
  fluxKontext: 'black-forest-labs/flux-kontext', // Image editing
  fluxSchnell: 'black-forest-labs/flux-schnell',

  // Video
  stableVideo4D: 'stability-ai/sv4d-2.0',

  // Audio
  musicgen: 'meta/musicgen',

  // LLMs (if needed outside main providers)
  llama: 'meta/llama-3.2-90b-vision',
} as const;

Usage

import Replicate from 'replicate';

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

// Image generation with FLUX.2
const output = await replicate.run('black-forest-labs/flux-2-pro', {
  input: {
    prompt: 'A serene mountain landscape at sunset',
    aspect_ratio: '16:9',
    output_format: 'webp',
  },
});

// Image editing with Kontext
const edited = await replicate.run('black-forest-labs/flux-kontext', {
  input: {
    image: 'https://...',
    prompt: 'Change the sky to sunset colors',
  },
});

Model Selection

flux-2-pro
├── Best for: Highest quality, up to 4MP
├── Speed: ~6s
├── Cost: $0.015 + per megapixel
└── Use when: Professional quality needed

flux-2-flex
├── Best for: Fine details, typography
├── Speed: ~22s
├── Cost: $0.06 per megapixel
└── Use when: Need precise control

flux-2-dev (Open source)
├── Best for: Fast generation
├── Speed: ~2.5s
├── Cost: $0.012 per megapixel
└── Use when: Speed over quality

flux-kontext
├── Best for: Image editing with text
├── Speed: Variable
├── Cost: Per run
└── Use when: Edit existing images

Stability AI

Documentation

Latest Models (December 2025)

const STABILITY_MODELS = {
  // Image generation
  sd35Large: 'sd3.5-large',
  sd35LargeTurbo: 'sd3.5-large-turbo',
  sd3Medium: 'sd3-medium',

  // Video
  sv4d: 'sv4d-2.0', // Stable Video 4D 2.0

  // Upscaling
  upscale: 'esrgan-v1-x2plus',
} as const;

Usage

const response = await fetch(
  'https://api.stability.ai/v2beta/stable-image/generate/sd3',
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      Authorization: `Bearer ${process.env.STABILITY_API_KEY}`,
    },
    body: JSON.stringify({
      prompt: 'A futuristic city at night',
      output_format: 'webp',
      aspect_ratio: '16:9',
      model: 'sd3.5-large',
    }),
  }
);

Mistral AI

Documentation

Latest Models (December 2025)

const MISTRAL_MODELS = {
  // Flagship
  large: 'mistral-large-latest',  // Points to 2411

  // Medium tier
  medium: 'mistral-medium-2505',  // Medium 3

  // Small/Fast
  small: 'mistral-small-2506',    // Small 3.2

  // Code specialized
  codestral: 'codestral-2508',
  devstral: 'devstral-medium-2507',

  // Reasoning (Magistral)
  magistralMedium: 'magistral-medium-2507',
  magistralSmall: 'magistral-small-2507',

  // Audio
  voxtral: 'voxtral-small-2507',

  // OCR
  ocr: 'mistral-ocr-2505',
} as const;

Usage

import MistralClient from '@mistralai/mistralai';

const client = new MistralClient(process.env.MISTRAL_API_KEY);

const response = await client.chat({
  model: 'mistral-large-latest',
  messages: [{ role: 'user', content: 'Hello!' }],
});

// Code completion with Codestral
const codeResponse = await client.chat({
  model: 'codestral-2508',
  messages: [{ role: 'user', content: 'Write a Python function to...' }],
});

Model Selection

mistral-large-latest (123B params)
├── Best for: Complex reasoning, knowledge tasks
├── Context: 128K tokens
└── Use when: Need high capability

codestral-2508
├── Best for: Code generation, 80+ languages
├── Speed: 2.5x faster than predecessor
└── Use when: Code-focused tasks

magistral-medium-2507
├── Best for: Multi-step reasoning
├── Specialty: Transparent chain-of-thought
└── Use when: Need reasoning traces

Voyage AI (Embeddings)

Documentation

Latest Models (December 2025)

const VOYAGE_MODELS = {
  // General purpose
  large2: 'voyage-large-2',
  large2Instruct: 'voyage-large-2-instruct',

  // Code specialized
  code2: 'voyage-code-2',
  code3: 'voyage-code-3',

  // Multilingual
  multilingual2: 'voyage-multilingual-2',

  // Domain specific
  law2: 'voyage-law-2',
  finance2: 'voyage-finance-2',
} as const;

Usage

const response = await fetch('https://api.voyageai.com/v1/embeddings', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    Authorization: `Bearer ${process.env.VOYAGE_API_KEY}`,
  },
  body: JSON.stringify({
    model: 'voyage-code-3',
    input: ['Your code to embed'],
  }),
});

const { data } = await response.json();
const embedding = data[0].embedding;

Quick Reference

Cost Comparison (per 1M tokens, approx.)

ProviderCheapMidPremium
Anthropic$0.25 (Haiku)$3 (Sonnet 4.5)$5 (Opus 4.5)
OpenAI$0.15 (4.1-nano)$2 (4.1)$15+ (o3)
Google$0.04 (Flash-lite)$0.08 (Flash)$1.25 (Pro)
Mistral$0.25 (Small)$2.70 (Medium)$8 (Large)

Best For Each Task

Reasoning/Analysis    → Claude Opus 4.5, o3, Gemini 3 Pro
Code Generation       → Claude Sonnet 4.5, Codestral 2508, GPT-4.1
Fast Responses        → Claude Haiku, GPT-4.1-mini, Gemini Flash
Long Context          → Gemini 2.5 Pro (2M), GPT-4.1 (1M), Claude (200K)
Vision                → GPT-4.1, Claude Sonnet, Gemini 3 Pro
Embeddings            → Voyage code-3, text-embedding-3-small
Voice Synthesis       → Eleven Labs v3/flash, OpenAI TTS
Image Generation      → FLUX.2 Pro, DALL-E 3, SD 3.5
Video Generation      → Stable Video 4D 2.0, Runway
Image Editing         → FLUX Kontext, gpt-image-1

Environment Variables Template

# .env.example (NEVER commit actual keys)

# LLMs
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
GOOGLE_API_KEY=AI...
MISTRAL_API_KEY=...

# Media
ELEVENLABS_API_KEY=...
REPLICATE_API_TOKEN=r8_...
STABILITY_API_KEY=sk-...

# Embeddings
VOYAGE_API_KEY=pa-...

Model Update Checklist

When models update:
□ Check official changelog/blog
□ Update model ID strings
□ Test with existing prompts
□ Compare output quality
□ Check pricing changes
□ Update context limits if changed

Sources

FAQ

What does the ai-models skill do?

Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate

How do I install ai-models?

Run npx skills add https://github.com/alinaqi/maggy --skill ai-models in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ai-models/ (personal) or .claude/skills/ai-models/ (project).

Which AI agents can use ai-models?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is ai-models safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains ai-models?

It is published by alinaqi in alinaqi/maggy. It has 299 recorded installs.

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