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

References

• xAI Models
• Pricing
• Grok 4.1 Fast Announcement