---
title: "月之暗面发布 Kimi K3：2.8 万亿参数开源模型，性能超越多数竞品"
source: "Simon Willison 博客（Atom）"
source_tier: "t2"
url: "https://simonwillison.net/2026/Jul/16/kimi-k3/#atom-everything"
permalink: "https://feed.astralor.com/items/itm_98a9920bcc16cf1dadf9844d"
published: "2026-07-16T20:19:30.000Z"
score: 76
channel: "ai"
tags: ["Kimi-K3","大语言模型","开源模型","月之暗面","模型定价"]
---

# 月之暗面发布 Kimi K3：2.8 万亿参数开源模型，性能超越多数竞品

## AI 点评

> Kimi K3 的发布标志着中国 AI 实验室在超大参数开源模型上的突破，2.8T 参数和承诺的开源时间表令人期待。对于开发者来说，定价是选择的关键因素——虽然 $3/$15 比前代贵不少，但相比 Opus 4.8 仍然便宜一半。不过，实际测试显示其推理 token 消耗巨大，长期使用成本可能超预期。值得关注的是它在前端代码领域的表现超越 Fable 5，这可能暗示其在代码生成方面的独特优势。

## 速览

月之暗面（Moonshot AI）于 2026 年 7 月 16 日发布 Kimi K3，宣称拥有 2.8 万亿参数，是目前最大的开源模型。模型在多项基准测试中超越 Claude Opus 4.8 和 GPT-5.5，仅次于 Claude Fable 5 和 GPT-5.6 Sol。定价为每百万输入 token 3 美元、输出 token 15 美元，高于前代 K2.6，但仍低于 Opus 4.8。作者 Simon Willison 通过自创的“鹈鹕骑自行车”测试发现，K3 推理开销巨大，单次生成成本 25 美分，但视觉能力出色。该模型计划于 2026 年 7 月 27 日开源权重。

## 关键点

- 参数规模 2.8T，是目前最大的开源模型，计划于 7 月 27 日开源
- 自报基准超越 Claude Opus 4.8 和 GPT-5.5，仅次于顶级模型
- 定价显著高于前代，成本约 $3/$15 每百万 token，但仍比 Opus 4.8 便宜一半
- 实际测试显示推理 token 消耗巨大（13k+），单次鹈鹕 SVG 生成成本 25 美分

## 全文

Chinese AI lab Moonshot AI announced Kimi K3 this morning, describing it as their "most capable model to date, with 2.8 trillion parameters". It's currently available via their website and API, but an open weight release is promised "by July 27, 2026". Moonshot are calling this the first "open 3T-class model" (I guess they're rounding 2.8 trillion up to 3 trillion), taking the crown from DeepSeek's 1.6T v4 Pro. Their self-reported benchmarks have K3 mostly beating Claude Opus 4.8 max and GPT-5.5 high, while losing out to Claude Fable 5 and GPT-5.6 Sol. A few highlights from the Artificial Analysis report on the model: "On our private long-horizon knowledge work evaluation, Kimi K3 reaches an overall Elo of 1547, +732 points from Kimi K2.6 and behind only Claude Fable 5." "Cost per task ($0.94) is similar to GPT-5.6 Sol ($1.04), ~1/2 the price of Opus 4.8 ($1.80) and higher than open weights peers" "Kimi K3’s token usage on the Artificial Analysis Intelligence Index decreased significantly, using 21% fewer output tokens than K2.6." The model is also now the leading model on Arena.ai's Frontend Code arena, surpassing even Claude Fable 5. The new model is notable for the pricing: $3/million input tokens and $15/million output tokens, putting it at the same level as Anthropic's Claude Sonnet series and making it the most expensive model released by a Chinese AI lab to date. This is a significant increase on their earlier models such as Kimi K2.6 at $0.95/$4. 2.8 trillion parameters is also more than twice the size of that 1T model. But how does it pelican? I used OpenRouter (to avoid signing up for a Moonshot API key) with the llm-openrouter plugin to generate an SVG of a pelican riding a bicycle: llm -m openrouter/moonshotai/kimi-k3 'Generate an SVG of a pelican riding a bicycle' Here's the transcript. It looks like this: That pelican took 95 input tokens and 16,658 output tokens (13,241 were reasoning tokens), for a total cost of 25 cents! Since K3 accepts image input I ran it against that rendered SVG above (with my alt text prompt) and got back (for 0.6 cents): Cartoon illustration of a white pelican wearing a red scarf, riding a red bicycle along a gray road with white dashed lines; the pelican has a large orange beak and webbed orange feet pedaling, with white motion lines behind it; the background shows a light blue sky with white clouds, a yellow sun, two small black birds in flight, and green grass with tiny white flowers in the foreground What can we learn from the pelican? My Generate an SVG of a pelican riding a bicycle test is 21 months old now. It was never a particularly great benchmark. It started out as a joke on how absurdly difficult it is to compare these models, but then for the first year it turned out to have a surprising correlation to how good the models actually were. That connection has been mostly severed now. The GPT-5.6 and Claude Fable 5 pelicans are outclassed by GLM-5.2, and much as I love GLM I don't think that's a Fable-class model. (I'm still not convinced that labs are training for the benchmark - if they were, I'd expect much better results. There's a chance that Gemini has optimized for any combination of an animal on a vehicle though!) The biggest limitation of the pelican is that it doesn't touch at all on the thing that matters most for today's model: agentic tool calling and the ability to operate tools reliably as conversations grow in length. So don't go using pelicans to compare models! All of that said, I still get a decent amount of value out of running the benchmark myself. Firstly, it's a forcing function for actually trying the model. If I show you a pelican, that means I've managed to run a prompt through it. If the model has an official API I'll use that, if it's open weight (and small enough to fit a 128GB M5 MacBook Pro) I'll try running it on my own machine, usually via llama.cpp or LM Studio or Ollama. I'll frequently use OpenRouter since that usually provides a proxy to an official API without me needing a new API key. Most of my pelicans are generated using my LLM CLI tool, which helps encourage me to ensure the latest models are supported by that (via one of its plugins). More importantly though, even the act of a single prompt to "Generate an SVG of a pelican riding a bicycle" can reveal interesting model characteristics. Consider the result for Kimi K3 today. Running those simple prompts helped emphasize several points about the model. It only has one reasoning effort right now, "max" - and it shows. The model consumed 13,241 reasoning tokens to output 3,417 tokens of response. This is expensive - the pelican cost 25 cents! How does the prompt "Generate an SVG of a pelican riding a bicycle" add up to 95 input tokens? OpenAI's tokenizer counts 10, Anthropic's counts 10 for Opus 4.6, 30 for Opus 4.7 and 25 for Sonnet 5/Fable 5. Prompting "hi" to Kimi K3 counted 86 tokens, suggesting there may be an 85 token hidden system prompt. It refused to leak it though. Vision works well: the alt text it generated is very good. K3 currently only has one thinking effort level, but I've been deriving quite a bit of value recently from running the same pelican prompt through different effort levels to get a quick idea for what impact those have. Here's my matrix for the GPT-5.6 model family, for example. Really though the main things I gain from the pelican test are: It's a "hello world" exercise for prompting a model A rough cost and reasoning estimate for a simple task Confirmation that the model can output valid SVG and has a basic idea of geometry and spatial awareness. This is a much bigger deal for the smaller models that run on my laptop. It's still interesting to compare pelicans between releases in the same model family. K3's pelican is a notable improvement from Kimi 2.5. It's something I can share that demonstrates I've tried it. Plus a comment with a pelican in it is kind of a tradition on Hacker News at this point, any time I'm late I get comments asking where it is! Tags: ai, generative-ai, llms, llm-pricing, pelican-riding-a-bicycle, llm-release, ai-in-china, artificial-analysis, moonshot, kimi

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