Kimi K2.5: 100 AI Agents Working Together, Complete Guide [2026]

Kimi K2.5 is a native multimodal AI model. Unlike older models that only learned from text and had vision bolted on later, K2.5 was trained on text, images, and video together from the start. It learned from roughly 15 trillion pieces of mixed content.

The model has 1 trillion parameters total, but only 32 billion are active for any single query. It uses a Mixture-of-Experts architecture with 384 specialist networks. Each prompt gets routed to the 8 most relevant experts plus one shared expert. So you get the intelligence of a massive model without needing to run all of it at once.

It also comes with a 256,000 token context window, enough to process an entire codebase or a 200-page document in one go.

Most AI models work like a single employee. You give them a task and they do it step by step. Even autonomous agents like Manus AI work sequentially. Agent Swarm works differently. You give K2.5 a complex task, and it breaks it into subtasks, spins up specialized agents for each one, and runs them in parallel.

Here is a real example. Say you ask K2.5 to research 10 competitor products, summarize each one, and put together a comparison report. A normal AI would research them one at a time. Agent Swarm creates 10 research helpers that all work at the same time, then a final helper that combines everything into your report.

The numbers:

• Up to 100 sub-agents per prompt
• Up to 1,500 coordinated tool calls per task
• 4.5x faster than single-agent execution on complex tasks
• 80% reduction in end-to-end runtime

The agents are not pre-built. K2.5 creates them dynamically based on what your task needs. It might create an "AI Researcher" for one subtask and a "Code Writer" for another. An internal orchestrator coordinates the swarm.

Because K2.5 learned from images and text together, it can interpret what it sees in a picture. If you build websites and apps, here is what that gets you:

• Screenshot to code: Show it a picture of a website design and it writes the code to build it, including animations and effects
• Video to automation: Record yourself doing something on screen and K2.5 can write the code to automate that process
• Visual bug fixing: Send it a screenshot of something that looks broken and it can figure out what went wrong
• Reading text from images: It scores 92.3% on text extraction tests, the best of any model. GPT-5.2 scores 80.7% on the same test

K2.5 goes beyond image captioning. Give it a design mockup and it produces an interactive, phone-friendly website layout.

Where K2.5 is the best:

• Reasoning with tools: K2.5 scores 50.2% on the HLE test vs 45.5% for GPT-5.2. When K2.5 gets access to tools like web search and code running, its score jumps by 20 points. GPT-5.2 only gains 11 points with the same tools
• Vision tasks: It wins 8 out of 16 major image understanding tests, and leads on reading text from images (92.3%)
• Web browsing tasks: Scores 74.9 on BrowseComp (K2 was already strong here)

Where K2.5 falls behind:

• Writing and fixing code: Claude Opus 4.5 leads at 80.9% on SWE-Bench (a test where AI fixes real bugs in real projects). GPT-5.2 scores 80.0%. K2.5 is solid at 76.8% but not the best here
• Command line tasks: Claude leads at 59.3% on Terminal-Bench

For a typical task (generating about 5,000 words of output), K2.5 costs roughly $0.014 per request compared to $0.019 for GPT-5.2. That is about 27% in savings.

Moonshot says K2.5 is 5.1x cheaper than GPT-5.2 for coding tasks and 10.1x cheaper for reasoning tasks. K2.5 sometimes needs more back and forth to get the same quality result, but the price per request is so much lower that it still ends up cheaper overall.

Since the model is completely free and open source, you can also download it and run it on your own computers. The download is about 595GB though, and you need expensive hardware to run it, so this only makes sense for bigger teams. Other Chinese AI companies are also undercutting on price; see our DeepSeek V4 vs Qwen3 Max comparison.

K2.5 is available in four modes on kimi.com:

• K2.5 Instant: Fast answers for simple questions. No deep thinking. Best for quick lookups and casual use
• K2.5 Thinking: Works through problems step by step before answering. Use this for math, logic, and analysis tasks
• K2.5 Agent: A single AI helper with access to tools like web browsing, code running, and file handling. Good for research and coding tasks
• K2.5 Agent Swarm (Beta): The full multi-helper system. Up to 100 helpers working in parallel. Best for complex, multi-step projects

# Quick start with the Moonshot API
pip install openai  # Compatible with OpenAI SDK

from openai import OpenAI

client = OpenAI(
  api_key="your-moonshot-api-key",
  base_url="https://api.moonshot.ai/v1"
)

response = client.chat.completions.create(
  model="kimi-k2.5",
  messages=[{"role": "user", "content": "Your prompt here"}]
)

The biggest jump is in vision (K2 could not see, K2.5 can) and agentic capability (single agent to 100-agent swarm). Office productivity also improved by 59.3% on document-heavy tasks. Coding went from 71.3% to 76.8% on SWE-Bench, a smaller gain.

Use K2.5 if you need:

• Research or analysis tasks with many steps that benefit from running at the same time
• Turning designs, screenshots, or videos into working code
• Processing lots of documents (reports, financial models, academic papers)
• A cheaper alternative to GPT-5.2 for tasks that use lots of tools
• An open-source model you can self-host and fine-tune

Stick with Claude or GPT-5.2 if you need:

• The best possible code writing and bug fixing (Claude Code leads on the SWE-Bench test)
• Command line and terminal automation (Claude leads on Terminal-Bench)
• The most reliable answers for simple, one-shot text questions

We also compared the top options in our best AI automation tools for 2026.

The scores are close to GPT-5.2 and the pricing undercuts it by a wide margin. But the reason to pay attention to Kimi K2.5 is Agent Swarm.

Most AI models are getting incrementally better at the same thing: answering one question at a time. K2.5 is trying something different. It splits your problem across dozens of specialized agents that all work in parallel. It is messy (still in beta), but other labs are already experimenting with multi-agent setups too.

Whether you use K2.5 through the API, Kimi Code, or just try Agent Swarm on kimi.com, it is worth seeing what 100 agents working on your problem at once actually feels like. And if you are still on K2, read our full Kimi K2 Thinking breakdown to see what K2 looked like three months ago.

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