# Kimi K3.1 Pro's faster mode uses about twice the quota **Summary:** Kimi K3.1 Pro scores 55 on Artificial Analysis, while Kimi Code defaults to max effort and Highspeed uses about twice the quota. I'd compare effort settings and time to acceptable results before spending more on speed. - Canonical: https://markhuang.ai/news/kimi-k3-1-pro-highspeed-quota - Language: en - Author: [Mark Huang](https://markhuang.ai/about) - Published: 2026-10-23 - Section: News - Tags: Kimi K3.1 Pro, Moonshot AI, Reasoning Effort, AI Coding, Model Benchmarks, Kimi Code - Source: [Artificial Analysis](https://artificialanalysis.ai/models/kimi-k3-1-pro) - License: https://creativecommons.org/licenses/by-nc/4.0/ --- ![A glowing compute core beside an unmarked effort dial, an accelerator lever, and a reservoir of translucent tokens](https://cdn.markhuang.ai/news/kimi-k3-1-pro-highspeed-quota/hero.webp) *A conceptual view of the choice: reasoning effort and faster output both belong in the spending decision.* [Artificial Analysis's Kimi K3.1 Pro evaluation](https://artificialanalysis.ai/models/kimi-k3-1-pro) gives the model an Intelligence Index score of 55 and a weighted cost of $2.11 per Index task. That is enough to put it on my shortlist. Before buying more speed, though, I would check a setting in Kimi Code's documentation. [Kimi Code's model guide](https://www.kimi.com/code/docs/en/kimi-code/models.html) says K3.1 Pro defaults to maximum reasoning effort. It also offers Highspeed, which Moonshot says has identical model capabilities and approximately 2 to 3 times the output speed, at about twice the quota usage. My first move would be to compare effort settings before spending more quota to accelerate the output. For a developer choosing a coding subscription or an API model, that distinction changes the experiment. I want to know how much reasoning the task needs and how long an acceptable result takes. A faster stream alone would not settle the purchase for me. ## What the 150 million tokens tell me Artificial Analysis reports 150 million output tokens across the Intelligence Index evaluation, alongside prices of $2 per million input tokens and $10 per million output tokens. That total covers the evaluation suite. It gives me a reason to inspect usage, but I still need measurements from representative tasks to estimate a monthly budget. The $2.11 figure helps compare models if I keep its scope in mind. Artificial Analysis calculates cost per task using each evaluation's token costs and task count, then weights those results by the evaluation's contribution to the Index. Its [methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking) combines 10 evaluations and explicitly acknowledges that the aggregate may not apply directly to every use case. A score of 55 is also an Index score, not a 55% success rate on my work. I raised the token-budget concern in my [earlier Kimi K3 analysis](https://markhuang.ai/news/kimi-k3-130-million-token-catch). For K3.1 Pro, I want to check whether the effort controls can reduce spending while preserving the result I need. The listing does not answer that question for my workload. ## Max is a configuration choice Kimi Code documents low, high, and max reasoning settings for K3.1 Pro. Thinking cannot be disabled; requests that disable it are rejected. I can compare the effort levels while keeping thinking enabled. My comparison would give each setting the same representative tasks and judge the deliverables against the same requirements. A small code change needs a clear acceptance condition, such as passing the relevant checks without introducing unrelated edits. For a research task, I would check the cited evidence and the answer's factual accuracy. I have not run that comparison here, so I cannot claim that low or high preserves max's quality. The strongest objection to reducing effort is that extra reasoning may prevent a repair that costs more than the tokens saved. I agree. I would keep max when it produces enough additional accepted work to justify its usage. But I want evidence for keeping that setting. There is a practical catch in the same guide: changing either the model or thinking effort invalidates the context cache. Kimi recommends starting a new session when changing them. That calls for fresh sessions under consistent conditions. Switching settings repeatedly inside one conversation would make it harder to tell why usage changed. ## When Highspeed would earn the extra quota Artificial Analysis reports 73.5 output tokens per second for the regular Pro model. Its definition measures generation after output begins, so that number does not describe the time from sending a request to receiving finished work. Nor does the regular Pro listing establish Highspeed's performance. Kimi Code's documentation draws the boundary plainly: Highspeed accelerates model output, while tool calls and script execution remain unaffected. If generating output dominates a task, faster generation could be worth the extra quota. If a task spends much of its time running tools, I would expect a smaller overall improvement. I would check the time saved across the whole job before accepting the extra quota usage. I would also keep the billing units separate. Artificial Analysis's dollar prices describe API tokens. The roughly doubled usage in the Kimi Code guide describes Coding Plan quota. Those figures do not establish a Highspeed API price or tell me how many tasks a subscription will cover. ## The disagreement worth testing The early [Hacker News discussion](https://news.ycombinator.com/item?id=50212065) contains a useful disagreement. Some commenters describe older Kimi models as prone to overthinking and complain about quota. Others say K3 produces useful work or needs fewer corrections. These are anecdotes about earlier models, not controlled evidence of K3.1 Pro's quality. They do explain why I would measure human correction time alongside token usage. [Moonshot's release post](https://www.kimi.ai/blog/kimi-k3-1-pro) says Pro and Highspeed are available through its products and API, with full model weights promised by November 4, 2026. I can evaluate the hosted service now; an evaluation of the promised downloadable release has to wait for the files. For hosted coding work, my starting point would be regular Pro, comparing effort levels before trying Highspeed on tasks where model output accounts for much of the delay. The extra quota would be worth spending if it brought acceptable work back sooner by enough to matter. Until that comparison, I would leave Highspeed off by default.