xAI Grok Models Guide Skill

xAI Grok Models Guide

Complete guide to selecting the right Grok model for your use case, with pricing and capability comparisons.

Model Quick Reference

| Model | Best For | Input $/1M | Output $/1M | Context | |-------|----------|------------|-------------|---------| | grok-4-1-fast | Tool calling, agents | $0.20 | $0.50 | 2M | | grok-4 | Complex reasoning | $3.00 | $15.00 | 256K | | grok-3-fast | General tasks | $0.20 | $0.50 | 131K | | grok-3-mini | Lightweight tasks | $0.30 | $0.50 | 131K | | grok-2-vision | Image analysis | $2.00 | $10.00 | 32K |

Model Selection Decision Tree

What's your primary need?
│
├─► Tool calling / Agent workflows
│   └─► grok-4-1-fast ($0.20/$0.50)
│
├─► Complex reasoning / Analysis
│   └─► grok-4 ($3.00/$15.00)
│
├─► General chat / Simple tasks
│   └─► grok-3-fast ($0.20/$0.50)
│
├─► High volume / Cost sensitive
│   └─► grok-3-mini ($0.30/$0.50)
│
└─► Image/Vision tasks
    └─► grok-2-vision ($2.00/$10.00)

Detailed Model Profiles

grok-4-1-fast (Recommended for Most Uses)

Best for: Tool calling, agentic workflows, real-time search

# Best choice for X search and sentiment analysis
response = client.chat.completions.create(
    model="grok-4-1-fast",
    messages=[{"role": "user", "content": "Search X for AAPL sentiment"}]
)

Features:

2 million token context window
Optimized for tool calling
Fast response times
Best price/performance ratio

Variants:

grok-4-1-fast-reasoning - Maximum intelligence
grok-4-1-fast-non-reasoning - Instant responses

grok-4

Best for: Deep analysis, complex reasoning, research

# Use for complex multi-step analysis
response = client.chat.completions.create(
    model="grok-4",
    messages=[{"role": "user", "content": "Analyze market trends..."}]
)

Features:

Highest reasoning capability
Best for complex tasks
256K context window

grok-3-fast

Best for: General purpose, balanced performance

# Good default choice for most tasks
response = client.chat.completions.create(
    model="grok-3-fast",
    messages=[{"role": "user", "content": "Summarize this..."}]
)

Features:

Fast responses
131K context
Good balance of speed/quality

grok-3-mini

Best for: High-volume, cost-sensitive applications

# Use for bulk processing
response = client.chat.completions.create(
    model="grok-3-mini",
    messages=[{"role": "user", "content": "Classify: ..."}]
)

Features:

Lowest latency
Most cost-effective
Good for simple tasks

grok-2-vision

Best for: Image analysis, charts, screenshots

import base64

# Encode image
with open("chart.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="grok-2-vision",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Analyze this chart"},
            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_data}"}}
        ]
    }]
)

Cost Optimization Strategies

1. Use the Right Model

# For filtering/classification - use mini
filter_response = client.chat.completions.create(
    model="grok-3-mini",
    messages=[{"role": "user", "content": f"Is this relevant? {text}"}]
)

# For analysis - use fast
if is_relevant:
    analysis = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{"role": "user", "content": f"Analyze: {text}"}]
    )

2. Leverage Caching

Cached input tokens are 75% cheaper:

Regular: $0.20/1M
Cached: $0.05/1M

3. Batch Similar Requests

# Instead of 10 separate calls, batch them
texts = ["text1", "text2", "text3"]
batch_prompt = "Analyze these texts:\n" + "\n".join(texts)

response = client.chat.completions.create(
    model="grok-3-fast",
    messages=[{"role": "user", "content": batch_prompt}]
)

Tool Calling Costs

| Tool | Cost per 1,000 calls | |------|---------------------| | X Search | $5.00 | | Web Search | $5.00 | | Code Execution | $5.00 | | Document Search | $2.50 |

Context Window Comparison

| Model | Context | Pages of Text | Hours of Audio | |-------|---------|---------------|----------------| | grok-4-1-fast | 2M | ~6,000 | ~50 | | grok-4 | 256K | ~800 | ~6 | | grok-3-fast | 131K | ~400 | ~3 | | grok-2-vision | 32K | ~100 | ~1 |

Model Capabilities Matrix

| Capability | 4.1 Fast | 4 | 3 Fast | 3 Mini | 2 Vision | |------------|----------|---|--------|--------|----------| | Tool Calling | ⭐⭐⭐ | ⭐⭐ | ⭐ | ⭐ | ❌ | | Reasoning | ⭐⭐ | ⭐⭐⭐ | ⭐⭐ | ⭐ | ⭐⭐ | | Speed | ⭐⭐⭐ | ⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | | Cost | ⭐⭐⭐ | ⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | | Vision | ❌ | ❌ | ❌ | ❌ | ⭐⭐⭐ | | X Search | ⭐⭐⭐ | ⭐⭐ | ⭐⭐ | ⭐ | ❌ |

Recommended Configurations

Financial Sentiment Pipeline

MODELS = {
    "filter": "grok-3-mini",      # Fast filtering
    "analyze": "grok-4-1-fast",   # Tool calling + analysis
    "deep": "grok-4"              # Complex reasoning (rare)
}

High-Volume Processing

MODELS = {
    "bulk": "grok-3-mini",
    "quality_check": "grok-3-fast"
}

Research & Analysis

MODELS = {
    "search": "grok-4-1-fast",
    "analyze": "grok-4",
    "summarize": "grok-3-fast"
}

API Usage Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("XAI_API_KEY"),
    base_url="https://api.x.ai/v1"
)

# List available models
models = client.models.list()
for model in models.data:
    print(f"{model.id}")

# Use specific model
response = client.chat.completions.create(
    model="grok-4-1-fast",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=100
)

Related Skills

xai-auth - Authentication setup
xai-agent-tools - Tool calling
xai-sentiment - Sentiment analysis

Agent Skills: xAI Grok Models Guide

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