Speed vs. Depth: The Tortoise and the Hare in AI Generation

In the age of instant gratification, we want our AI to be instant. We've grown accustomed to Groq's LPU inference speeds where text flies onto the screen faster than we can read it. So, when users first try Kimi k2 Thinking, the reaction is often: "Why is it so slow?"

Kimi k2 is not a speed demon. In fact, compared to the lightning-fast responses of GPT-4o-mini or Haiku, Kimi feels like it's wading through molasses. But this slowness is not a bug; it's a feature. It is the tangible cost of Depth.

In this article, we'll analyze the "Speed vs. Depth" trade-off, look at the token generation stats, and explain why waiting 30 seconds for an answer might actually save you 30 minutes of work.

The Token Economics of "Thinking"

To understand the speed issue, we have to look at what's happening under the hood.

When you ask a standard LLM a question, it immediately starts generating the answer.

• Standard Model: Input -> [Answer Token 1] [Answer Token 2]...

When you ask Kimi k2 Thinking a question, it starts a hidden process.

• Kimi k2: Input -> [Thought Token 1] [Thought Token 2] ... [Thought Token 500] -> [Answer Token 1]...

You, the user, only see the answer tokens (usually). But the GPU is crunching through hundreds, sometimes thousands, of thought tokens before it even clears its throat to speak.

The Benchmarks

User reports and benchmarks paint a clear picture:

• Claude 3.5 Sonnet: ~90-100 tokens/second.
• Kimi k2 Thinking: ~30-40 tokens/second (effective speed).

That's a 3x difference. In a world of real-time applications, that lag is noticeable. It makes Kimi feel "heavy."

The "Slow Thinking" Advantage

Why would anyone tolerate this? Because fast answers are often wrong answers.

Daniel Kahneman's book Thinking, Fast and Slow describes two systems of human thought. System 1 is fast and instinctive (and prone to bias). System 2 is slow and logical. Most LLMs are pure System 1. They predict the next word based on vibes. Kimi k2 attempts to simulate System 2.

Scenario: The Tricky SQL Query

User: "Write a SQL query to find the top 3 users who spent the most money in the last month, but exclude users who refunded more than 50% of their orders."

Fast Model (System 1): Immediately spits out a query. It looks correct. You run it. It fails because it didn't account for the refunds table join properly or messed up the date window logic. You spend 15 minutes debugging it.

Kimi k2 (System 2): Pauses.

• Thinking: "I need to join users, orders, and refunds. I need to calculate total spend and total refund per user. Then I need to filter by the ratio. I need to be careful about division by zero if total spend is 0..."
• Output: Produces a correct query with COALESCE and proper HAVING clauses.

The generation took 20 seconds longer. But the code worked the first time. The Time-to-Correct-Code was actually lower, even if the Time-to-First-Token was higher.

When Speed Matters (And When It Doesn't)

Understanding this trade-off helps you decide when to use Kimi.

Don't Use Kimi k2 For:

• Chatbots: Users hate latency in conversation.
• Autocomplete: Code completion needs to be <50ms.
• Simple Facts: "What is the capital of France?" doesn't require deep thought.

Do Use Kimi k2 For:

• Complex Refactoring: "Rewrite this class to be thread-safe."
• Data Analysis: "Look at this CSV and find the anomaly."
• Creative Writing: "Write a mystery novel plot with no plot holes."

The User Experience Challenge

The challenge for Moonshot AI (and developers using Kimi) is managing user expectations. If the screen is blank for 10 seconds, the user thinks the app crashed.

UI Solutions:

1. Show the Thoughts: Some interfaces stream the "thinking" tokens in a collapsed gray box. Seeing the AI "work" makes the wait tolerable and even fascinating. It builds trust.
2. Progress Indicators: Instead of a spinning loader, show steps: "Analyzing Request... Checking Constraints... Drafting Code..."

Conclusion

In a tech culture obsessed with speed, Kimi k2 asks us to slow down. It reminds us that intelligence takes time. As hardware improves (H200s, Blackwell), this gap will close. But for now, Kimi k2 is the "Slow Food" movement of AI: it takes longer to prepare, but the meal is much more satisfying.

Next time you find yourself tapping your foot waiting for Kimi to reply, remember: it's not lagging. It's thinking. And that makes all the difference.