OpenAI Codex CLI: The Complete GTM Team Guide [2026]

OpenAI released GPT-5.3-Codex on February 5, 2026—their most capable agentic coding model yet. The Codex CLI puts this power at your fingertips, letting you automate GTM tasks directly from your terminal.

This guide covers everything GTM teams need to know: installation, essential commands, and real workflows for sales automation, content generation, and pipeline management.

What's New in GPT-5.3 Codex

Before diving into the CLI, here's why GPT-5.3 matters:

Feature	GPT-5.2 Codex	GPT-5.3 Codex
Speed	Baseline	25% faster
Mid-turn steering	Limited	Full support
Multi-file context	50K tokens	100K tokens
Tool reliability	89%	96%
Reasoning depth	Good	Significantly improved

The killer feature? Mid-turn steering—you can direct Codex while it's working, correcting course without starting over.

Installing the Codex CLI

Get started in 60 seconds:

# Install globally via npm
npm install -g @openai/codex

# Verify installation
codex --version
# codex 1.4.0 (gpt-5.3-codex)

# Authenticate
codex auth login
# Opens browser for OpenAI authentication

First Run

Test your installation:

codex "explain what you can do for a sales team"

You should see Codex describe its capabilities for sales automation, data analysis, and content generation.

Essential Codex Commands for GTM

Basic Syntax

codex "<natural language task>"

Codex interprets your request and executes the appropriate actions. You can also:

# Run in a specific directory
codex --cwd /path/to/project "<task>"

# Include files for context
codex --include "*.csv" "<task>"

# Set output format
codex --output json "<task>"

# Enable mid-turn steering
codex --interactive "<task>"

The --interactive Flag (Mid-Turn Steering)

This is GPT-5.3's superpower. Instead of waiting for Codex to finish, you can course-correct in real-time:

codex --interactive "analyze our pipeline and suggest improvements"

While Codex works, you can type commands like:

• focus on deals stuck longer than 30 days
• ignore deals under $10K
• also check for missing next steps

Codex adjusts its analysis mid-stream without starting over.

GTM Workflows with Codex CLI

1. Pipeline Analysis

Analyze your CRM data directly:

# Export pipeline from your CRM first (or connect via API)
codex --include "pipeline.csv" "
Analyze this sales pipeline and identify:
1. Deals that have been stuck in the same stage for >30 days
2. Deals with no recent activity
3. Deals missing next steps
4. Predicted close rates by stage

Output as a prioritized action list for the sales manager.
"

Sample output:

## Pipeline Health Report

### 🚨 Stuck Deals (30+ days same stage)
1. Acme Corp - Proposal stage for 47 days - $85K
   → Action: Escalate to VP Sales, consider discount strategy
2. TechStart Inc - Demo stage for 38 days - $32K
   → Action: Re-engage champion, check for competing eval

### ⚠️ Missing Next Steps (23 deals)
- Priority 1: Deals >$50K with no activity last 14 days
- Recommend: Mandatory next-step field in CRM

### 📊 Stage Conversion Rates
- Lead → Discovery: 42% (healthy)
- Discovery → Demo: 68% (above benchmark)
- Demo → Proposal: 31% (⚠️ below benchmark 45%)
- Proposal → Closed Won: 28% (needs attention)

2. Lead Research at Scale

Research a list of leads:

codex --include "leads.csv" --output json "
For each company in this list:
1. Find their LinkedIn company page
2. Get employee count and recent funding
3. Identify likely decision makers in Sales/Marketing
4. Note any recent news or hiring signals

Output as enriched JSON with research_notes for each lead.
"

3. Email Sequence Generation

Generate personalized email sequences:

codex "
Create a 5-email sequence for SDR outreach to VP of Sales at mid-market SaaS companies.

Context: We're MarketBetter, an AI-powered SDR platform.
Pain point: SDR productivity and lead prioritization
Differentiator: We tell SDRs WHO to contact AND WHAT to do

Requirements:
- Email 1: Cold intro, <100 words
- Email 2: Value-add (share relevant content)
- Email 3: Social proof (customer results)
- Email 4: Direct ask for meeting
- Email 5: Breakup email

Include subject lines and personalization tokens.
"

4. Competitor Analysis

Research competitors systematically:

codex --interactive "
Research these competitors and create a comparison matrix:
- Warmly
- 6sense
- Apollo
- ZoomInfo

For each, find:
1. Pricing (actual prices, not just 'contact us')
2. Key features
3. G2 rating and top complaints
4. Recent product updates
5. Where MarketBetter wins

I'll guide you as you research.
"

With --interactive, you can steer:

• "Dig deeper on Warmly's pricing—check G2 reviews for price mentions"
• "Skip ZoomInfo, we have that already"
• "Focus more on their visitor identification capabilities"

5. Meeting Prep

Prepare for sales calls:

codex "
I have a demo call with Sarah Chen, VP Sales at DataFlow Inc.

Research:
1. Sarah's LinkedIn background
2. DataFlow Inc recent news/funding
3. Their current tech stack (from job postings)
4. Common challenges for companies their size
5. 3 personalized talking points

Also draft 3 discovery questions specific to their situation.
"

6. Content Generation

Generate blog post outlines:

codex "
Create an outline for a blog post: 'Why Intent Data Fails Without Action'

Target keyword: intent data for sales
Word count: 2000 words
Audience: VP Sales, SDR Managers

Include:
- Compelling intro hook
- 5-7 main sections with subheadings
- Data points to research
- CTA to MarketBetter demo

Make it contrarian—most content says intent data is magic. 
We say it's useless without the action layer.
"

7. CRM Data Cleanup

Fix messy CRM data:

codex --include "contacts.csv" "
Clean this contact list:
1. Standardize company names (remove Inc., LLC variants)
2. Fix obvious email typos (@gmial.com, etc.)
3. Parse full names into first/last
4. Flag likely duplicates
5. Validate phone number formats

Output as cleaned CSV with a 'changes_made' column.
"

Advanced Codex Patterns

Chaining Commands

Build complex workflows:

# Research → Enrich → Generate sequence
codex "research DataFlow Inc" > /tmp/research.txt && \
codex --include /tmp/research.txt "generate 3 personalized email openers based on this research"

Template-Based Generation

Create reusable prompt templates:

# Save as ~/.codex/templates/competitor-analysis.txt
cat << 'EOF' > ~/.codex/templates/competitor-analysis.txt
Research {{COMPETITOR}} and provide:
1. Company overview (employees, funding, HQ)
2. Product positioning
3. Pricing structure
4. Key differentiators
5. Customer complaints (from G2/Capterra)
6. How we beat them

Format as markdown with clear sections.
EOF

# Use the template
codex --template competitor-analysis COMPETITOR=Warmly

Integration with Other Tools

Combine Codex with your existing stack:

# Pull from HubSpot, analyze with Codex, push back
hubspot contacts list --limit 100 --format csv > leads.csv
codex --include leads.csv "score these leads 1-100 based on fit for AI SDR tools"
# Parse output and update HubSpot via API

Codex CLI vs Claude Code vs ChatGPT

When to use each:

Task	Best Tool	Why
Multi-file code changes	Codex CLI	Purpose-built for code
Long document analysis	Claude Code	200K context window
Quick questions	ChatGPT	Fastest for simple tasks
Pipeline data analysis	Codex CLI	Structured output
Email writing	Claude Code	Better nuance
Competitor research	Either	Both strong
Meeting prep	Codex (interactive)	Mid-turn steering

The real answer? Use them together. Codex for structured tasks and code, Claude for nuanced writing and analysis.

Cost Considerations

Codex CLI usage is charged per token:

Usage	Approximate Cost
Simple task (<1K tokens)	~$0.02
Pipeline analysis (5K tokens)	~$0.10
Research task (10K tokens)	~$0.20
Large batch (50K tokens)	~$1.00

For most GTM teams, budget $50-100/month for heavy CLI usage. Compare that to the hours saved.

Common Gotchas

1. Rate Limits

Free tier: 20 requests/minute. Paid: 100 requests/minute.

For batch processing:

# Add delays between requests
for company in $(cat companies.txt); do
  codex "research $company"
  sleep 3
done

2. Context Limits

Even with 100K tokens, large files need chunking:

# Process large CSV in chunks
split -l 100 huge_leads.csv chunk_
for chunk in chunk_*; do
  codex --include $chunk "process this batch"
done

3. Output Consistency

For structured output, be explicit:

# Bad: "analyze this data"
# Good:
codex "analyze this data and return JSON with fields: 
{insights: string[], recommendations: string[], priority: high|medium|low}"

The MarketBetter Integration

MarketBetter uses the same AI models under the hood—but packages them into a complete GTM platform:

• Daily Playbook — Codex-style analysis of your entire pipeline, delivered as actionable tasks
• AI Chatbot — GPT-5.3 powers real-time lead qualification
• Smart Dialer — AI-prioritized call lists based on intent signals
• Email Automation — Personalized sequences generated at scale

The difference? MarketBetter eliminates the prompting and integration work. Your SDRs get AI-powered insights without touching a command line.

See how MarketBetter turns AI into pipeline →

Quick Reference Card

# Installation
npm install -g @openai/codex
codex auth login

# Basic usage
codex "<task>"

# With file context
codex --include "data.csv" "<task>"

# Interactive mode (mid-turn steering)
codex --interactive "<task>"

# JSON output
codex --output json "<task>"

# Specific directory
codex --cwd /path/to/project "<task>"

# Help
codex --help

Getting Started Checklist

• Install Codex CLI (npm install -g @openai/codex)
• Authenticate (codex auth login)
• Test basic command
• Try --interactive mode
• Export CRM data for analysis
• Create first prompt template
• Set up shell alias for common tasks
• Budget token usage

The Codex CLI puts GPT-5.3's power directly in your terminal. For GTM teams, that means faster research, smarter analysis, and more personalized outreach—all from the command line.

---

Want more AI automation for GTM? Check out Codex vs Claude Code for sales automation and building AI agents with OpenClaw.