What's the real-world max token count before quality degrades? My tests.

Hey everyone! I've been putting Kimi through its paces lately, specifically trying to find that sweet spot for long-form content and analysis. We all see the big, impressive token context window advertised, but I wanted to see where the practical, quality limit really sits before outputs start getting vague, repetitive, or lose the plot.

I ran a series of tests feeding it progressively longer documents—sales playbooks, competitive intelligence dumps, lengthy email threads—and then asked for specific summaries, gap analyses, and action item extraction. Here's what I found in my workflow:

* **Solid Performance Zone (Up to ~120K tokens):** This is the sweet spot. Kimi handles complex instructions, maintains strong coherence, and references details from deep in the text accurately. Perfect for consolidating a big competitive research doc or a detailed project brief.
* **Noticeable Degradation Starts (~150K+ tokens):** Beyond this point, I started seeing more "template-like" phrasing in summaries and some minor hallucination of specifics from the middle sections. It felt like it was prioritizing the beginning and end of the context.
* **Practical Max for Quality Output (~180K tokens):** Around here, the quality drop was clear. Responses became more generic, missed nuanced instructions buried in my prompt, and occasionally conflated information. For mission-critical work, I wouldn't push past this point.

My takeaway? For serious revenue ops or sales intelligence work—like parsing a massive CRM data export alongside new market research—chunking your input to stay under 150K tokens yields the best, most reliable results. It's still a huge amount of context, but knowing this limit helps plan workflows.

Has anyone else done similar stress tests? I'm curious if your findings match up, especially when working with different types of documents.

— Dan