Kimi K2.5
Discover Moonshot AI's Kimi K2.5, a 1T-parameter open-source agentic model featuring native multimodal capabilities, a 262K context window, and SOTA reasoning.
moonshotKimiJanuary 27, 2026About Kimi K2.5
Learn about Kimi K2.5's capabilities, features, and how it can help you achieve better results.
Kimi K2.5 is an open-source multimodal model from Moonshot AI. It uses a 1 trillion parameter Mixture-of-Experts architecture where 32 billion parameters are active per token. The system unifies text, image, and video processing through a single reasoning framework rather than using separate external encoders for each modality. This architecture allows the model to handle 256,000 tokens of context while maintaining high retrieval accuracy and logical consistency across very long sequences.
The model is distinguished by its Agent Swarm capability. This feature allows the system to coordinate up to 100 parallel sub-agents to execute complex research or engineering tasks simultaneously. By integrating a 400M parameter MoonViT-3D encoder, K2.5 can analyze several hours of video content with temporal precision. It is specifically designed for autonomous execution, outperforming many proprietary models on agentic benchmarks like SWE-Bench and BrowseComp.
Kimi K2.5 provides a dedicated Thinking mode for tasks requiring deep logic. When enabled, the model generates an internal chain of reasoning to self-correct and verify steps before producing a final answer. This makes it highly effective for competition-level mathematics and large-scale software development. Its token economics are optimized for enterprise deployment, offering frontier-level intelligence at a fraction of the cost of competing closed-source systems.
Use Cases
Discover the different ways you can use Kimi K2.5 to achieve great results.
Autonomous Software Engineering
Solving complex GitHub issues and building multi-file project architectures using SWE-Bench optimized logic.
Visual Web Development
Creating functional frontend code and UI designs directly from screen recordings of existing website interactions.
Multi-Threaded Research
Using Agent Swarm to crawl and synthesize information from over 100 sources in a single parallel workflow.
Long Video Analysis
Extracting specific events and temporal data from hours of security or lecture footage without frame extraction tools.
Mathematical Proof Generation
Applying the deep thinking mode to solve olympiad-level math problems with a 96 percent accuracy rate.
Enterprise Document Automation
Generating multi-page PDF reports and complex financial spreadsheets from unstructured business data sources.
Strengths
Limitations
API Quick Start
fireworks/kimi-k2p5
import OpenAI from 'openai';
const client = new OpenAI({ apiKey: process.env.KIMI_API_KEY, baseURL: 'https://api.moonshot.cn/v1' });
async function main() {
const res = await client.chat.completions.create({
model: 'kimi-k2.5',
messages: [
{ role: 'system', content: 'You are Kimi, a reasoning agent.' },
{ role: 'user', content: 'Design a parallel research plan for quantum computing trends.' }
],
extra_body: { thinking: { type: 'enabled' } }
});
console.log(res.choices[0].message.content);
}
main();Install the SDK and start making API calls in minutes.
Community Feedback
See what the community thinks about Kimi K2.5
“Kimi K2.5 costs almost 10 percent of what Opus costs at a similar performance level.”
“People forget Nvidia lost 600 billion dollars when a Chinese lab open sourced something major. Kimi is doing that again with frontier intelligence.”
“The Attention Residuals concept in K2.5 is the first architectural change in years that actually fixes the LLM forgetting problem.”
“Workers AI runs big models now. Kimi K2.5 first. It is one of the best open source models out there, very good for coding too.”
“Kimi K2.5 is a different beast. It is a smart incredible RP model, but it can get neurotic if you do not use community presets.”
“I replaced my GPT 4 workflow with Kimi K2.5 because the thinking mode is more transparent and the context window handles my whole repo.”
Related Videos
Watch tutorials, reviews, and discussions about Kimi K2.5
“Kimmy K2.5 beating GPT 5.2 with high thinking, absolutely destroying the other Frontier models.”
“It is the strongest open source coding model to date with 76.8 on SWE verified.”
“Agent swarm is a shift from single agent to multi agent executing parallel workflows across up to 1500 coordinated steps.”
“The context window is massive at 256k tokens which is plenty for most projects.”
“Moonshot is really pushing the boundaries of what open weights can do in early 2026.”
“It really nailed the whole Apple design aesthetic and produced a nice looking website with animations just from a video.”
“The Swarm feature looks very cool and it is definitely fun to use as it assigns ID badges to each sub agent.”
“K2.5 is much cheaper at 60 cents per million input tokens and 3 dollars per million output tokens.”
“The native video processing means you don't have to use expensive external tools to process frames.”
“This model is a game changer for developers who need autonomous agents on a budget.”
“Moonshot achieved this by giving each sub agent rewards at separate critical step stages to prevent serial collapse.”
“The model learns to choose parallelism only when it shortens this critical path, which is very clever innovation.”
“Kimi K2.5 is just around the edge of being able to run this on consumer hardware using GGUF.”
“The thinking mode is incredibly robust for solving complex logical errors in Python.”
“Seeing a 1 trillion parameter model released like this is huge for the open source community.”
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Pro Tips
Expert tips to help you get the most out of Kimi K2.5 and achieve better results.
Enable Thinking Mode
Pass the thinking parameter in your API request to reach maximum accuracy for math and coding tasks.
Trigger Agent Swarm
Instruct the model to deploy a swarm for research tasks to force parallel orchestration across sub-agents.
Optimize Temperature
Use a temperature of 1.0 for thinking mode to permit diverse reasoning but lower it to 0.6 for standard chat.
Joint Vision Prompts
Upload error screenshots alongside code snippets to leverage the model's unified text-vision training.
Context Caching
Utilize context caching for repeated long documents to reduce input costs by up to 90 percent.
Testimonials
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Frequently Asked Questions
Find answers to common questions about Kimi K2.5