Incremental Context Testing for LLMs: A Simple Script for Stress Testing Limits

After benchmarking the R1 1776 model and seeing how post training influenced its performance (full post here), I realized another gap.

Models that can technically handle a huge context window often degrade long before you hit their max token limit.

Plenty of benchmarks test for raw throughput or model quality at a static context size. But what about the slow, creeping degradation as you gradually increase context? Models rarely fall off a cliff. They fade.

I wanted a lightweight way to test that incrementally.

Introducing the Context Stretching Script

To solve this, I wrote a simple script that:

• Sends increasingly large prompts to a model
• Measures response time and content quality
• Graphs the slowdown as tokens are added

Rather than asking the model random trivia, it uses a repeated token (default: hello) to simulate larger and larger contexts in a controlled way. Think of it as slowly tightening a vise to find the pressure point.

Here's the flow:

1. Generate a base prompt with a repeated token up to an initial size
2. Send it to the model and measure response time
3. Incrementally increase the prompt length
4. Repeat
5. Plot the latency growth (or failure points)

It is simple, fast to run, and immediately reveals if a model's claimed context window matches real-world behavior.

Why This Matters

Post-training, quantization, or fine-tuning can subtly erode a model’s ability to handle long contexts. The model might "accept" a long prompt without crashing, but you see things like:

• Slower generation
• Memory spikes
• Garbled outputs
• Early cutoffs

This script gives you a cheap and dirty way to catch it without needing a massive evaluation pipeline. If you care about real-world deployment (and not just leaderboard scores), this kind of stress testing is essential.

It is simple, easy to run, and immediately reveals if a model's claimed context window matches real world behavior.

Why This Matters

Post training, quantization, or fine tuning can subtly erode a model’s ability to handle long contexts. The model might "accept" a long prompt without crashing, but you see things like:

• Slower generation
• Memory spikes
• Garbled outputs
• Early cutoffs

This script gives you a cheap and dirty way to catch it without needing a massive evaluation pipeline. If you care about real world deployment and not just leaderboard scores, this kind of stress testing is essential.

Today more models are advertising 128k plus context windows. Very few can use them well.

Help Improve It

This script is very much a work in progress.
If you have ideas on better ways to measure degradation, catch failure points earlier, or simulate more realistic long prompts, I would love to hear them.

Feel free to leave a comment or suggestion. I want to keep improving this and eventually fold it into a larger open source benchmarking suite.