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LLM Prediction Tracker

Track each LLM's prediction accuracy, watch for model drift, and verify how reliable different models really are on forecasting tasks.

Total Predictions
3
Accuracy
50%
Verified
2
Correct ✅
1
Error ❌
1

📊 Model Accuracy Comparison

GPT-4o
0% (0/0)
1 Pending
Claude 3.5
100% (1/1)
0 Pending
Gemini 2.0
0% (0/1)
0 Pending
⏳ PendingGPT-4oConfidence: 75%

Tesla stock will reach $350 in Q1 2026

💡 Based on FSD progress and earnings expectations

Category
Stocks
GoalDate
2026-03-31
Recorded on 2026-01-15
✅ CorrectClaude 3.5Confidence: 68%

The Fed will cut rates by 25 basis points in March 2026

💡 Inflation Data stabilizing

Category
Economy
GoalDate
2026-03-20
Recorded on 2026-02-01
❌ ErrorGemini 2.0Confidence: 55%

Apple will release AR glasses in Spring 2026

💡 Cook hints at delay to Fall 2026

Category
Product
GoalDate
2026-03-15
Recorded on 2026-01-20

🔬 Why Track LLM Predictions?

Large Language Models' prediction capabilities exhibit significant "model drift" — the accuracy of the same model may fluctuate across different time periods.Through long-term tracking, we can:

  • Quantify the reliability of different models on prediction tasks
  • Discover whether models have "overconfidence" issues
  • Track changes in prediction capabilities after model updates
  • Provide data support for decision-making, rather than blindly trusting AI
Source inspiration: community experiments that tracked LLM predictions over time, including public discussions about logging Gemini stock forecasts across multiple weeks.

Why LLM Prediction Tracker Is Worth Using

Track and compare AI model predictions over time. Monitor accuracy, bias, and performance across different LLMs. Free. This page is built for people who want a fast path to a working result, not a vague prompt-and-pray workflow. If you need a more reliable first draft, cleaner output, or a repeatable workflow you can hand to a teammate, LLM Prediction Tracker is designed to shorten that path.

Most visitors use LLM Prediction Tracker because they need something specific done now: a deliverable, a decision, or a workflow checkpoint. The sections below show the fastest way to get value from the tool and the adjacent pages that help you keep going.

How to Use LLM Prediction Tracker

Log predictions from different AI models and track their accuracy.

  1. 1Enter a prediction from any AI model
  2. 2Record the actual outcome when available
  3. 3View accuracy trends over time
  4. 4Compare performance across models

Who Is LLM Prediction Tracker For?

For AI practitioners who want to objectively compare model performance.

AI Researchers

Track model improvements across versions

Product Managers

Justify AI model selection with data

AI Enthusiasts

Compare which LLMs give better answers

What a Good Result Looks Like

A strong outcome from LLM Prediction Tracker is not just “some output.” It should be usable with minimal cleanup, aligned to the task you opened the page for, and specific enough that you can paste it into the next step of your workflow without rewriting everything from scratch.

If the first pass feels too generic, use the use cases, FAQs, and related pages here to tighten the scope. That usually produces better results faster than starting over in a blank chat.

Frequently Asked Questions

Which AI models can I track?▼
Any model: GPT-4, Claude, Gemini, Llama, Mistral, and any open-source models.
How many predictions can I track?▼
Unlimited. All data is stored locally in your browser.
Can I export the data?▼
Yes, export your prediction history and accuracy reports as CSV or JSON.

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