moonshot

Kimi K3

Kimi K3 is Moonshot AI's 2.8T MoE model with a 1M token context window, native multimodal vision, and frontier-tier coding performance for complex agents.

Open WeightsMultimodalCodingMoEReasoning
moonshot logomoonshotKimiJuly 16, 2026
Context
1.0Mtokens
Max Output
16Ktokens
Input Price
$3.00/ 1M
Output Price
$15.00/ 1M
Modality:TextImageVideo
Capabilities:VisionToolsStreamingReasoning
Benchmarks
GPQA
93.5%
GPQA: Graduate-Level Science Q&A. A rigorous benchmark with 448 multiple-choice questions in biology, physics, and chemistry created by domain experts. PhD experts only achieve 65-74% accuracy, while non-experts score just 34% even with unlimited web access (hence 'Google-proof'). Kimi K3 scored 93.5% on this benchmark.
HLE
44.3%
HLE: High-Level Expertise Reasoning. Tests a model's ability to demonstrate expert-level reasoning across specialized domains. Evaluates deep understanding of complex topics that require professional-level knowledge. Kimi K3 scored 44.3% on this benchmark.
MMLU
89.2%
MMLU: Massive Multitask Language Understanding. A comprehensive benchmark with 16,000 multiple-choice questions across 57 academic subjects including math, philosophy, law, and medicine. Tests broad knowledge and reasoning capabilities. Kimi K3 scored 89.2% on this benchmark.
MMLU Pro
78.5%
MMLU Pro: MMLU Professional Edition. An enhanced version of MMLU with 12,032 questions using a harder 10-option multiple choice format. Covers Math, Physics, Chemistry, Law, Engineering, Economics, Health, Psychology, Business, Biology, Philosophy, and Computer Science. Kimi K3 scored 78.5% on this benchmark.
SimpleQA
48%
SimpleQA: Factual Accuracy Benchmark. Tests a model's ability to provide accurate, factual responses to straightforward questions. Measures reliability and reduces hallucinations in knowledge retrieval tasks. Kimi K3 scored 48% on this benchmark.
IFEval
89.1%
IFEval: Instruction Following Evaluation. Measures how well a model follows specific instructions and constraints. Tests the ability to adhere to formatting rules, length limits, and other explicit requirements. Kimi K3 scored 89.1% on this benchmark.
AIME 2025
88%
AIME 2025: American Invitational Math Exam. Competition-level mathematics problems from the prestigious AIME exam designed for talented high school students. Tests advanced mathematical problem-solving requiring abstract reasoning, not just pattern matching. Kimi K3 scored 88% on this benchmark.
MATH
81.2%
MATH: Mathematical Problem Solving. A comprehensive math benchmark testing problem-solving across algebra, geometry, calculus, and other mathematical domains. Requires multi-step reasoning and formal mathematical knowledge. Kimi K3 scored 81.2% on this benchmark.
GSM8k
96.8%
GSM8k: Grade School Math 8K. 8,500 grade school-level math word problems requiring multi-step reasoning. Tests basic arithmetic and logical thinking through real-world scenarios like shopping or time calculations. Kimi K3 scored 96.8% on this benchmark.
MGSM
94.2%
MGSM: Multilingual Grade School Math. The GSM8k benchmark translated into 10 languages including Spanish, French, German, Russian, Chinese, and Japanese. Tests mathematical reasoning across different languages. Kimi K3 scored 94.2% on this benchmark.
MathVista
70.2%
MathVista: Mathematical Visual Reasoning. Tests the ability to solve math problems that involve visual elements like charts, graphs, geometry diagrams, and scientific figures. Combines visual understanding with mathematical reasoning. Kimi K3 scored 70.2% on this benchmark.
SWE-Bench
49%
SWE-Bench: Software Engineering Benchmark. AI models attempt to resolve real GitHub issues in open-source Python projects with human verification. Tests practical software engineering skills on production codebases. Top models went from 4.4% in 2023 to over 70% in 2024. Kimi K3 scored 49% on this benchmark.
HumanEval
92.5%
HumanEval: Python Programming Problems. 164 hand-written programming problems where models must generate correct Python function implementations. Each solution is verified against unit tests. Top models now achieve 90%+ accuracy. Kimi K3 scored 92.5% on this benchmark.
LiveCodeBench
68%
LiveCodeBench: Live Coding Benchmark. Tests coding abilities on continuously updated, real-world programming challenges. Unlike static benchmarks, uses fresh problems to prevent data contamination and measure true coding skills. Kimi K3 scored 68% on this benchmark.
MMMU
72%
MMMU: Multimodal Understanding. Massive Multi-discipline Multimodal Understanding benchmark testing vision-language models on college-level problems across 30 subjects requiring both image understanding and expert knowledge. Kimi K3 scored 72% on this benchmark.
MMMU Pro
59.5%
MMMU Pro: MMMU Professional Edition. Enhanced version of MMMU with more challenging questions and stricter evaluation. Tests advanced multimodal reasoning at professional and expert levels. Kimi K3 scored 59.5% on this benchmark.
ChartQA
88.4%
ChartQA: Chart Question Answering. Tests the ability to understand and reason about information presented in charts and graphs. Requires extracting data, comparing values, and performing calculations from visual data representations. Kimi K3 scored 88.4% on this benchmark.
DocVQA
93.6%
DocVQA: Document Visual Q&A. Document Visual Question Answering benchmark testing the ability to extract and reason about information from document images including forms, reports, and scanned text. Kimi K3 scored 93.6% on this benchmark.
Terminal-Bench
88.3%
Terminal-Bench: Terminal/CLI Tasks. Tests the ability to perform command-line operations, write shell scripts, and navigate terminal environments. Measures practical system administration and development workflow skills. Kimi K3 scored 88.3% on this benchmark.
ARC-AGI
8%
ARC-AGI: Abstraction & Reasoning. Abstraction and Reasoning Corpus for AGI - tests fluid intelligence through novel pattern recognition puzzles. Each task requires discovering the underlying rule from examples, measuring general reasoning ability rather than memorization. Kimi K3 scored 8% on this benchmark.

About Kimi K3

Learn about Kimi K3's capabilities, features, and how it can help you achieve better results.

Architecture and Scale

Kimi K3 is a third-generation flagship model from Moonshot AI, utilizing a massive 2.8 trillion parameter Mixture-of-Experts (MoE) architecture. The model activates 16 out of 896 experts for each token, allowing for high computational efficiency without sacrificing the intelligence depth associated with extreme parameter counts. It introduces Kimi Delta Attention and attention residuals to maintain high factual accuracy and stability across its full 1,048,576-token context window.

Multimodal Capabilities

The model is natively multimodal, capable of processing text, images, and video frames within a unified framework. This integration enables sophisticated agentic workflows, such as visual auditing of generated code or analyzing long technical videos for information retrieval. Kimi K3 is specifically designed for long-horizon tasks, including autonomous research and complex software engineering, where visual feedback loops are essential.

Performance Positioning

Kimi K3 competes directly with closed-source models like Claude 3.5 Opus and GPT-5 tier systems. It holds high rankings on the LMArena Frontend Code Board, demonstrating proficiency in web development and 3D procedural generation. By combining open weights with frontier-level reasoning, the model provides a platform for developers to build specialized autonomous systems that require deep technical understanding and large-scale data processing.

Kimi K3

Use Cases

Discover the different ways you can use Kimi K3 to achieve great results.

Long-Horizon Agentic Coding

Executing autonomous multi-hour coding sessions that involve thousands of tool calls and self-correction based on terminal output.

3D Procedural Game Development

Generating functional browser-based 3D environments using 3JS and WebGPU from high-level natural language descriptions.

Autonomous Chip Design

Utilizing open-source EDA tools to design, optimize, and verify custom ASIC chips in 24-48 hour autonomous runs.

Scientific Literature Synthesis

Reviewing massive repositories of PhD-level scientific papers to implement complex astrophysical or chemical calculations.

Interactive Data Visualizations

Processing large historical datasets to build drill-down research dashboards and interactive web-based reports.

Multi-Agent Orchestration

Coordinating complex networks of heterogeneous agents to manage large-scale parallel search and execution tasks.

Strengths

Limitations

Frontier Coding Performance: Ranked #1 on the LMArena Frontend Code Board, outperforming industry giants in creative coding and UI development.
Output Throughput Latency: Generates 17-20 tokens per second, which is significantly slower than smaller specialized models.
Massive Context Stability: Maintains 90.4% accuracy across its full 1M-token window, utilizing Delta Attention to prevent information loss.
Hardware Inference Ceiling: Running the model locally requires compute clusters with 1.5TB+ of VRAM, making it inaccessible for consumer hardware.
Native Multimodal Agency: Processes video and images within a unified framework, allowing the model to self-correct tasks via visual feedback.
Reasoning Token Overhead: The extensive thinking phase can result in high time-to-first-token measurements of up to 8 seconds.
Extreme Parameter Scale: At 2.8 trillion parameters, it is the largest open-weights model available, offering deep specialized technical knowledge.
Factual Accuracy Gaps: A SimpleQA score of 48% indicates the model can struggle with high-precision factual verification despite strong reasoning.

API Quick Start

moonshotai/kimi-k3

View Documentation
moonshot SDK
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.MOONSHOT_API_KEY,
  baseURL: 'https://api.moonshot.cn/v1',
});

async function main() {
  const completion = await client.chat.completions.create({
    model: 'kimi-k3',
    messages: [{ role: 'user', content: 'Design a procedural 3D forest in 3JS.' }],
    stream: true,
  });

  for await (const chunk of completion) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }
}

main();

Install the SDK and start making API calls in minutes.

Community Feedback

See what the community thinks about Kimi K3

Kimi K3 just three-shotted a complex game for me using 600k tokens. The era of Chinese labs being far behind is officially over.
AICodeKing
youtube
The long-horizon capabilities are insane. It worked through multiple errors in a finetuning task without asking me for a single hint.
DevGuru_88
reddit
It's basically the 'Linux moment' for reasoning models. We're seeing frontier logic available with open weights.
SiliconValleyWatcher
hackernews
K3 makes the Opus tier feel obsolete for coding. It's on par with the absolute top tier but at a fraction of the cost per task.
TechReporter_X
twitter
The model was available on Kimi Code at rollout. Two variants: K3 Max for chat and K3 Swarm Max for parallel processing.
ivanfioravanti
twitter
I'm pretty sure I'm hitting the limits of what 1.5TB if memory can do for a 2.5T parameter 1M context model.
mweinbach
twitter

Related Videos

Watch tutorials, reviews, and discussions about Kimi K3

This is a model that is called Kimiko 3, 3 trillion parameters, and a model that is on par with Opus.

Kimiko 3 essentially just has better information routing, so it can get more intelligence out of the same amount of compute.

The benchmarks suggest it's beating Fable 5 in coding benchmarks.

I'm interested to see how it handles the full 1 million context window in the real world.

The fact that this is open weights is a huge development for the ecosystem.

The elevator logic is correct. People wait properly and get picked up one by one... K3 is right up there with the top models.

The moment you give it a real, messy, long-horizon task, K3 pulls ahead of both Opus and GPT-5.6.

It three-shotted a complex game for me using 600k tokens.

Its native vision allows it to fix visual bugs in real-time.

The pricing is actually very competitive compared to other frontier models.

Kimi K3 didn't just catch up with Opus and GPT-5.5, it caught up with Fable and GPT-5.6.

There is a huge step up when we go from GLM 5.2 to Kimi K3 in terms of UI asset generation.

The 2.8 trillion parameters explain why it feels so much smarter at logic.

It's able to process video frames for visual auditing of code.

Moonshot AI is definitely leading the open-weights movement right now.

More than just prompts

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Pro Tips

Expert tips to help you get the most out of Kimi K3 and achieve better results.

Leverage Kimi CLI

K3 is highly optimized for the Kimi CLI harness; using this interface provides the best performance for long-horizon agentic tasks.

Use Visual Feedback Loops

Provide the model with screenshots of its generated UI; its native vision allows it to identify and fix visual bugs in real-time.

Optimize for Caching

Take advantage of prompt caching to reduce input costs by up to 90% for repeated context blocks in large documents.

Set Thinking Effort

Adjust the reasoning parameter to 'max effort' for mathematical proofs or chip design to fully utilize the MoE routing logic.

Testimonials

What Our Users Say

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Frequently Asked Questions

Find answers to common questions about Kimi K3