Data over Vibes: Choosing the Right AI Tool

Most software decisions get made on vibes. Feature lists, pricing pages, hype. But when tools affect your workflow, usage and spend, you need to ask: does this fit how I actually work?

That question came up for me when my ChatGPT Plus subscription was set to expire. I was considering T3.chat instead. At $8/month, it’s a killer deal. Clean UX, great model lineup, privacy-first posture. But one thing gave me pause:

1,500 messages per month.

It sounded like enough… but was it? Rather than guess, I ran the numbers.

Export Your Data, Don’t Trust Your Gut

ChatGPT lets you export your full usage history as a conversations.json file. I wrote a small Python script to:

• Parse all messages between 2025-03-20 and 2025-04-17
• Count how many were authored
• Compute a daily average
• Forecast usage through 2025-04-26 with a 20% buffer

Here’s what came back:

--- FORECASTED USAGE (2025-03-20 → 2025-04-26) ---
Total actual messages: 1632
Active days so far: 29
Avg messages/day: 56.28
Avg/day (+20% buffer): 67.53
Remaining days to forecast: 9
Projected total usage by 2025-04-26: 2239 messages

What the Data Said

If I switched to T3.chat today, I’d exceed the message cap by ~49%.

That doesn’t mean T3.chat is bad. It’s excellent for users with leaner, more intentional workflows. But for me, it would force a change:

• Fewer iterative prompts
• More scoped, complete instructions
• Offload auxiliary tasks to GitHub Copilot or other tools

The real takeaway? This wasn’t a pricing question. It was a workflow question.

How the Script Works (and Why It’s Simple)

You don’t need a data pipeline to get useful insights. Here’s a simplified breakdown of how the Python script works:

1. Load your conversations.json export:

with open("conversations.json", "r") as f:
    data = json.load(f)

2. Loop through the data and count user-authored messages:

for conv in data:
    for msg in conv.get("mapping", {}).values():
        if msg.get("message", {}).get("author", {}).get("role") == "user":
            ts = msg["message"].get("create_time")
            if ts and start_date <= datetime.fromtimestamp(ts) <= today:
                dt = datetime.fromtimestamp(ts)
                daily_counts[dt.strftime("%Y-%m-%d")] += 1

3. Calculate stats and forecast:

avg_per_day = total_msgs / active_days
avg_per_day_buffered = avg_per_day * 1.2
projected_msgs = total_msgs + (avg_per_day_buffered * remaining_days)

Done. Fast, accurate and actionable.

Why This Matters

Software trade-offs aren’t theoretical. They’re real, lived constraints. And if you want to stay efficient, whether you’re a developer, writer, or analyst—your tool choices need to match your patterns.

Good decisions come from data, not assumptions.

This one Python script saved me from adopting a tool that didn’t align with how I work today. Maybe I’ll evolve toward it later. But now, I know exactly what that change would cost.

TL;DR

If you’re evaluating alternatives to ChatGPT:

• Export your data
• Analyze your real usage
• Let that shape your choice

No guesswork. No regret. Just data-backed clarity.

2025-07-21 Update:

Usage patterns evolve. What you see above was a snapshot. My habits have shifted dramatically since then. After Gemini 2.5 Pro consistently exceeded and ChatGPT low-balled my expectations, I switched to T3. Three months in, I’ve never hit the 1,500 message cap (as Theo’s repeated praises on Twitter). The closest I’ve come is 1,200 messages, leaving a comfortable 300-message buffer. T3.chat fits my workflow perfectly now. So while the data-driven approach still stands, my choice has evolved with my needs.