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Kimi K3's 'always-on' reasoning drives unexpected costs

By PulseAugur Editorial · [1 sources] ·

A developer using the Kimi K3 model discovered that its "always-on" reasoning capability, while powerful for tasks like deep code review, leads to significantly higher costs than anticipated. A single request from one user consumed over 243,000 tokens, with a substantial portion attributed to the model's internal thinking process. Additionally, a bug in the developer's implementation caused token counts to be double-counted, inflating usage by up to 3x. The developer advises using Kimi K3 for specialized, long-context tasks and opting for more cost-effective models like DeepSeek Chat for general purposes. AI

IMPACT Highlights the significant cost implications of advanced reasoning models and the need for strategic task routing.

RANK_REASON Developer's analysis of a specific model's cost and usage patterns.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Kimi K3's 'always-on' reasoning drives unexpected costs

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0 / 100
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Tool
Developer's analysis of a specific model's cost and usage patterns.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, infra
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High
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Story freshness
27 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Daniel Dong ·

    Kimi K3 is 10x more expensive than I thought. And it's not the pricing's fault.

    <p>Kimi K3 is 10x more expensive than I thought. And it's not the pricing's fault.</p> <p>I added Kimi K3 to my API gateway two weeks ago. 1M context, always-on<br /> reasoning. The flagship. I watched the usage numbers climb and thought<br /> "great, people love it."</p> <p>Then…

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