Last updated Mar 31, 2026.

Kimi K2.5 Explained: Architecture, Benchmarks & API on Qubrid AI

7 minutes read

Shubham Tribedi

Kimi K2.5 Explained: Architecture, Benchmarks & API on Qubrid AI

Built with a massive Mixture-of-Experts (MoE) architecture, Kimi K2.5 combines enormous model capacity with practical efficiency. While it excels in reasoning and coding, it is especially powerful as a vision-language model, designed to understand and reason over images, videos, and text together.

For developers, the best part is simple and you don’t need specialized hardware. Through Qubrid AI, you can instantly experiment with Kimi K2.5 using a web playground or integrate it into applications via API.

In this guide, we’ll explore what Kimi K2.5 is, how its architecture works, its multimodal capabilities, and how you can start using it on Qubrid AI.

What is Kimi K2.5?

Kimi K2.5 is a Mixture-of-Experts large language model designed to handle advanced reasoning tasks, software engineering workflows, and multimodal inputs.

Unlike traditional dense models where every parameter is activated during inference, MoE models activate only a subset of parameters for each token. This allows the model to scale to extremely large sizes without proportional increases in compute cost.

Key Specifications

Feature	Specification
Total Parameters	1 Trillion
Active Parameters	~32 Billion per token
Architecture	Mixture-of-Experts
Experts	384
Experts Active per Token	8
Context Window	256K tokens
Focus Areas	Coding, reasoning, agents, multimodal

Because only a small portion of the model is active for each token, Kimi K2.5 delivers the capacity of a trillion-parameter system while maintaining the efficiency of a much smaller model.

👉 You can try Kimi K2.5 model on Qubrid AI here: https://platform.qubrid.com/model/kimi-k2.5

How the Mixture-of-Experts Architecture Works

To understand why Kimi K2.5 is efficient, it's useful to understand the concept behind Mixture-of-Experts (MoE) models. Instead of using one giant neural network, MoE architectures split the network into multiple specialized components called experts.

Simplified Flow

Input Token
     │
Gating Network
     │
Select Top Experts
     │
Process Through Experts
     │
Combine Outputs
     │
Final Prediction

The gating network determines which experts should process each token. In the case of Kimi K2.5, only 8 experts out of 384 are activated per token.

This design offers several advantages:

Compute efficiency: Only a fraction of parameters are used during inference.
Scalability: New experts can be added to increase model capacity without drastically increasing cost.
Expert specialization: Different experts can become highly optimized for specific tasks such as coding, reasoning, or language understanding and for visual reasoning.

This architecture is what makes extremely large models like Kimi K2.5 practical to deploy.

Benchmark Performance

Kimi K2.5 performs strongly across benchmarks that measure coding ability, reasoning skills, and multimodal understanding.

Check out Kimi's blog for more information: https://www.kimi.com/blog/kimi-k2-5

Coding and Software Engineering

Benchmark	Score	What It Measures
SWE-bench Verified	76.8%	Fixing real GitHub issues
LiveCodeBench	85.0%	Competitive programming tasks

SWE-bench is particularly valuable because it evaluates how well models solve real software engineering problems, including debugging and modifying existing repositories.

Reasoning and Problem Solving

Benchmark	Score
Humanity’s Last Exam	50.2%
BrowseComp	74.9%
MATH-500	96.2%

The 96.2% score on MATH-500 demonstrates strong mathematical reasoning ability and logical problem solving.

Multimodal Understanding

Kimi K2.5 is also trained with multimodal data, enabling it to process images and video along with text.

Benchmark	Score
MMMU Pro	78.5%
VideoMMMU	86.6%
LongVideoBench	79.8%

These benchmarks show that the model can analyze visual information while combining it with textual reasoning.

Built for Agent Workflows

One of the most interesting aspects of Kimi K2.5 is its focus on agent-based workflows. Moonshot AI introduced a training method called Parallel Agent Reinforcement Learning (PARL). This approach trains the model to coordinate multiple agents working on different tasks simultaneously.

What This Enables

Parallel agents: Up to 100 agents can work on different subtasks at once.
Large-scale tool usage: The system can perform thousands of tool calls within a single session.
Improved speed: Parallel execution allows workflows to run significantly faster than sequential agents.

This capability makes Kimi K2.5 well suited for a variety of practical applications, including autonomous coding assistants that help generate and debug code, AI research agents that gather and analyze information, workflow automation systems that coordinate tasks across tools, and pipelines that require multi-step reasoning to solve complex problems.

Long Context Capabilities

Another standout feature of Kimi K2.5 is its 256K token context window. This allows the model to process extremely large inputs, such as entire code repositories, long research papers, full conversation histories, and even lengthy video transcripts.

For developers building applications like code review systems or enterprise assistants, long context can significantly improve accuracy and understanding.

Getting Started with Kimi K2.5 on Qubrid AI

Running trillion-parameter models locally typically requires specialized GPU infrastructure. Qubrid AI simplifies this by providing access to large models through a managed platform. Developers can experiment with Kimi K2.5 instantly without worrying about hardware setup.

Step 1: Create a Qubrid AI Account

Start by signing up on the Qubrid AI platform. Start with a \(5 top-up and get \)1 worth of tokens free to explore the platform and run real workloads.

Step 2: Use the Playground

The Qubrid Playground allows you to interact with models directly in your browser. You have the ability to test prompts, modify parameters such as temperature and token limits, and explore various models.
Simply select moonshotai/Kimi-K2.5 from the model list and start testing prompts.
This is a vision model on our platform. Upload an image and run the prompt like: "Extract insights from the above image"

Step 3: Integrate the API

Once you're ready to build applications, you can integrate Kimi K2.5 (Vision) using Qubrid’s OpenAI-compatible API.

Python Example

from openai import OpenAI

client = OpenAI(
    base_url="https://platform.qubrid.com/v1",
    api_key="YOUR_QUBRID_API_KEY",
)

response_stream = client.chat.completions.create(
    model="moonshotai/Kimi-K2.5",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What is in this image? Describe the main elements."
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
                    }
                }
            ]
        }
    ],
    temperature=0.7,   # more stable for vision tasks
    max_tokens=1024,   # 16k is overkill unless needed
    stream=True
)

for chunk in response_stream:
    if chunk.choices:
        delta = chunk.choices[0].delta
        if hasattr(delta, "content") and delta.content:
            print(delta.content, end="", flush=True)

print("\n")

Because the API follows a familiar structure, developers can integrate it quickly into existing applications.

Practical Use Cases

Kimi K2.5 can power a wide range of AI applications.

AI Coding Assistants: Tools that generate code, debug issues, and suggest improvements for existing repositories.
Vision-Centric Applications: From extracting insights in documents and analyzing UI/UX to enabling visual quality checks and interpreting charts or diagrams, Kimi K2.5 turns visual data into actionable understanding.
Autonomous Developer Agents: AI agents that can plan tasks, modify codebases, run tests, and iterate on solutions.
Enterprise Knowledge Assistants: Systems that analyze internal documents, architecture diagrams, and technical knowledge bases.
Multimodal Applications: Applications that combine text, images, and video analysis in a single workflow.

Why Developers Use Qubrid AI

Qubrid AI provides a practical way for developers to experiment with large models without infrastructure complexity.

Key advantages include:

No GPU setup required: Developers can run large models without managing hardware.
Fast inference infrastructure: The platform runs on high-performance GPUs for low latency.
Unified API: Multiple models can be accessed using the same API pattern.
Playground to production workflow: Developers can test prompts in the playground and deploy the same configuration via API.

👉 You can explore all models here: https://platform.qubrid.com/models

Final Thoughts

Kimi K2.5 represents a new generation of large language models built specifically for developer workflows and agent-based systems.

Its Mixture-of-Experts architecture enables trillion-parameter scale while maintaining efficient inference. Combined with strong benchmark performance in coding, reasoning, and multimodal tasks, it is a powerful model for building advanced AI applications.

For developers who want to experiment with the model without dealing with infrastructure challenges, Qubrid AI provides one of the easiest ways to get started.