Kimi K2: Moonshot AI's Open-Source Trillion-Parameter Beast Redefining Agentic AI

In July 2025, Beijing-based Moonshot AI released Kimi K2, a trillion-parameter open-source model that quickly drew attention across the AI research and developer community.

Six months later, with K2 Thinking and K2.5 now available, the model family has reshaped expectations around what open-source systems can handle, particularly in long-horizon reasoning and agent-driven workflows.

Let’sbreaks down what K2 actually delivers, how to use it, and where it genuinely excels versus where the hype outruns reality.

What Is Kimi K2?

Kimi K2 is a Mixture-of-Experts (MoE) large language model built for agentic tasks. It has 1 trillion parameters, with 32 billion parameters activated per token. Each token is routed through a subset of specialized “expert” networks, reducing compute while maintaining capacity. The model was trained on approximately 15.5 trillion tokens and supports a context length of 128k tokens.

K2 has 61 layers, 64 attention heads, and 384 experts (8 selected per token), with a vocabulary of 160,000 tokens. The MuonClip optimizer stabilizes training by adjusting attention logits. Moonshot AI designed K2 for autonomous problem-solving: it can plan multi-step workflows, call external tools, evaluate results, and adjust actions without requiring new prompts at each step.

From Kimi Chatbot to K2 Series Evolution

Moonshot AI's journey started in October 2023 when they launched the original Kimi chatbot. At that time, it was notable for supporting 128,000 tokens of context, a first for publicly available models. The company, founded by Tsinghua University graduate Yang Zhilin, quickly attracted backing from Alibaba and built a user base that briefly ranked third among Chinese AI chatbots.

The K2 series represents a significant pivot toward open-source. In July 2025, Moonshot released Kimi K2 under a Modified MIT License, making it freely available for commercial and research use. November 2025 brought K2 Thinking, a reasoning variant trained to interleave chain-of-thought with tool calls. 

Model	Release	Architecture	Parameters	Context
Kimi (Original)	Oct 2023	Transformer	Undisclosed	~128k tokens
Kimi K1.5	Jan 2025	Undisclosed (RLHF)	Undisclosed	Undisclosed
Kimi K2 Base	July 2025	Sparse MoE	1T (32B active)	128k tokens
Kimi Linear	Oct 2025	MoE	48B (3B active)	Undisclosed
Kimi K2 Thinking	Nov 2025	MoE	~1T (32B active)	256k tokens
Kimi K2.5	Jan 2026	MoE	1.04T (32B active)	256k tokens

Most recently, January 2026 saw the release of K2.5, which adds native multimodal capabilities and introduces an agent swarm paradigm.

Kimi K2.5: Multimodal Extension and Multi-Agent Support

Kimi K2.5 builds on K2 with additional pretraining on roughly 15 trillion mixed text and visual tokens, making it natively multimodal. It can reason over images and video alongside text, which enables workflows such as generating and debugging front-end code from visual inputs, or iteratively inspecting images, writing code, executing it, and adjusting outputs based on results.

The model also supports parallel agent execution. For complex tasks, K2.5 can spawn multiple sub-agents internally and coordinate their work without predefined roles or external orchestration. Moonshot AI reports that this reduces end-to-end execution time compared with sequential single-agent workflows. Agent Swarm remains in beta.

K2.5 supports practical workflows including coding, document generation, spreadsheets, PDFs, and slide decks, and integrates with Kimi Code, which runs in the terminal or connects to IDEs such as VS Code, Cursor, and Zed.

Feature	Notes
Multimodal reasoning	Text + image + video
Coding	Front-end generation and visual debugging
Agent Swarm	Up to 100 sub-agents, beta mode
Productivity	Documents, spreadsheets, slides
Access	Kimi.com, Kimi App, API, Kimi Code (Instant, Thinking, Agent, Agent Swarm)

Architecture: 1T Total / 32B Active MoE Parameters

K2's architecture follows MoE principles similar to DeepSeek-V3, but with some distinct choices. The model contains 384 specialized expert networks organized across 61 layers (1 dense layer plus 60 MoE layers). 

For any given token, the routing mechanism activates only 8 experts plus 1 shared expert, keeping active parameters at 32 billion.

Key technical specifications:

Component	Specification
Total Parameters	1 trillion
Active Parameters	32 billion
Experts	384 total, 8+1 selected per token
Context Window	128K (K2), 256K (K2 Thinking/K2.5)
Training Data	15.5 trillion tokens
Precision	FP8 (K2), INT4 native (K2 Thinking/K2.5)

One genuine innovation is MuonClip, Moonshot's custom optimizer. Training trillion-parameter models typically involves loss spikes and instabilities that require manual intervention. MuonClip allowed K2's pre-training to complete on 15.5 trillion tokens with zero loss spikes, a significant engineering achievement.

Key Features and Capabilities

K2 differentiates itself through three main strengths: agentic intelligence, long-context processing, and competitive benchmark performance on reasoning and coding tasks.

Agentic Intelligence & Tool Calling (200–300 Steps Autonomous)

The headline feature is K2's ability to execute extended autonomous workflows. K2 Thinking can handle 200 to 300 sequential tool calls without human intervention. This means the model can: take on a research task, search the web, analyze results, run code, interpret output, recognize when it needs more information, search again, and continue this loop for hundreds of steps while maintaining coherent goal pursuit.

Moonshot achieved this through a large-scale agentic data synthesis pipeline. They simulated over 20,000 tool-use scenarios across hundreds of domains, including real APIs, shell commands, databases, and synthetic tools. The model then underwent joint reinforcement learning, improving through interactions with both real and simulated environments.

This "interleaved thinking" works as follows: K2 Thinking generates reasoning tokens, decides to call a tool, receives results, reasons about those results, decides on the next action, and continues. This differs from models that separate planning from execution. K2 treats thinking and acting as a continuous loop.

Long Context (256K Tokens) & Multimodal Support (K2.5)

K2's context window expanded from 128K tokens in the base model to 256K tokens in K2 Thinking and K2.5. For reference, 256K tokens can hold roughly 190,000 words, enough to process entire codebases, lengthy legal documents, or multi-document research collections in a single session.

K2.5 adds native multimodal capabilities through continued pre-training on approximately 15 trillion mixed visual and text tokens. Unlike models that bolt vision onto an existing text model, K2.5 processes images and videos as first-class inputs. This enables use cases like generating code from UI mockups, debugging based on screenshots, and reconstructing websites from video demonstrations.

Advanced Reasoning, Coding & Math Benchmarks

Benchmark results show K2’s performance across reasoning, coding, and mathematics tasks:

Category	Benchmark	Metric	Kimi K2	GPT-4.1	Claude
Math & STEM	MATH-500	Accuracy	97.4%	92.4%	94.4%
Math & STEM	AIME 2024	Avg@64	69.6	46.5	48.2
Coding	SWE-bench Verified	Single Attempt	65.8%	54.6%	~72.7%
Coding	LiveCodeBench v6	Pass@1	53.7%	44.7%	47.4%
Tool Use	Tau2 Telecom	Avg@4	65.8	38.6	45.2
General	MMLU	EM	89.5%	90.4%	92.9%

K2 is designed for multi-step reasoning and agentic tasks. It can break down problems, execute sequential workflows, and interact with external tools such as APIs, databases, or shells.

Post-training included simulations of thousands of tool-use tasks across multiple domains.

The model is open source under a Modified MIT License. It is available on Hugging Face and supports inference engines including vLLM, SGLang, and TensorRT-LLM. API access is provided via Moonshot AI’s platform and Anthropic-compatible endpoints.

Model	Input Cost per 1M tokens	Output Cost per 1M tokens
Kimi K2	$0.15	$2.50
GPT-4.1	$2.00	$8.00
Claude Opus 4	$15.00	$75.00
Claude Sonnet 4	$3.00	$15.00
Gemini 2.5 Pro	$2.50	$15.00
DeepSeek-V3	$0.27	$1.10

OK Computer Agent Mode & Agent Swarm (K2.5)

Moonshot's consumer products leverage K2's agentic capabilities through OK Computer, an agent mode in the Kimi app for complex task automation.

K2.5 introduces the Agent Swarm paradigm. Instead of a single agent grinding through tasks sequentially, K2.5 can spawn and coordinate up to 100 sub-agents executing parallel workflows across up to 1,500 tool calls. The main agent decomposes problems into parallelizable subtasks assigned to dynamically instantiated specialist agents.

According to Moonshot's benchmarks, Agent Swarm reduces end-to-end runtime by up to 80% compared to single-agent execution, with wall-clock improvements reaching 4.5x through parallelization.

How to Access and Use Kimi K2

K2's open-source nature means multiple access paths, from zero-configuration web interfaces to full local deployments.

Free Methods: Web UI, API, Hugging Face

The fastest way to try K2 is through Kimi.com or the Kimi mobile app. The free tier provides unlimited basic conversations, though advanced features like Deep Research have usage limits.

For programmatic access, Moonshot's platform offers an API compatible with both OpenAI and Anthropic SDKs. You can sign up at platform.moonshot.ai, recharge a minimum of $1, and start making calls. The API supports function calling, JSON mode, and streaming out of the box.

Model weights are available on Hugging Face under repositories like moonshotai/Kimi-K2-Instruct, allowing complete inspection and fine-tuning for those who need it.

Running Locally: Ollama, vLLM, SGLang

Running a trillion-parameter model locally requires serious hardware, but quantized versions make it more accessible. Unsloth provides GGUF quantizations, with the 2-bit dynamic version (UD-Q2_K_XL) balancing size and accuracy for consumer hardware.

For production deployments, Moonshot recommends vLLM or SGLang. Hardware requirements vary by quantization. Full FP8 weights need approximately 1TB of storage across multiple cards. INT4 quantizations (like K2 Thinking native) reduce this to around 594GB. Enthusiasts have run quantized versions on single RTX 3090s, achieving approximately 7 tokens per second generation.

Pricing & Inference (Moonshot API)

Moonshot AI charges for model usage based on the number of tokens processed. A token generally corresponds to 3-4 English characters, though longer words may count as multiple tokens. Both input and output tokens contribute to usage billing. File uploads and document extraction are temporarily free and do not incur charges.

Kimi K2.5 (Multimodal)

K2.5 handles both text and visual inputs and supports long-context reasoning, agentic workflows, and multiple operational modes. The model also provides features like ToolCalls, partial execution, and internet search. Pricing varies depending on whether the input comes from cached context or not:

Model	Tokens	Input Price (Cache Hit)	Input Price (Cache Miss)	Output Price	Max Context
Kimi K2.5	1M	$0.10	$0.60	$3.00	262,144

Kimi K2 (Text-Focused)

K2 remains focused on coding, reasoning, and agentic workflows with 1 trillion parameters and 32 billion active per token. Several variants are available to balance speed, reasoning depth, and context length:

Model	Tokens	Input Price (Cache Hit)	Input Price (Cache Miss)	Output Price	Max Context
K2-0905-preview	1M	$0.15	$0.60	$2.50	262,144
K2-0711-preview	1M	$0.15	$0.60	$2.50	131,072
K2-turbo-preview	1M	$0.15	$1.15	$8.00	262,144
K2-thinking	1M	$0.15	$0.60	$2.50	262,144
K2-thinking-turbo	1M	$0.15	$1.15	$8.00	262,144

Variants differ in focus: standard previews balance reasoning and coding; turbo models prioritize higher throughput; thinking models specialize in multi-step reasoning and agent workflows.

Moonshot-v1 Series

Moonshot also offers the v1 generation models, which cover general text generation and optional vision capabilities. Prices scale with context length:

Model	Tokens	Input Price	Output Price	Max Context
v1-8k	1M	$0.20	$2.00	8,192
v1-32k	1M	$1.00	$3.00	32,768
v1-128k	1M	$2.00	$5.00	131,072
v1-8k-vision	1M	$0.20	$2.00	8,192
v1-32k-vision	1M	$1.00	$3.00	32,768
v1-128k-vision	1M	$2.00	$5.00	131,072

Token usage is measured per 1 million tokens. Cached tokens are billed at the input price for cache hits.

This setup allows developers to choose models depending on whether they prioritize multimodal reasoning, high-speed outputs, or long-context processing, while keeping costs predictable.

Performance Benchmarks & Comparisons

K2's strongest showing is in agentic benchmarks, tasks requiring planning, tool use, and multi-step execution. On BrowseComp, which tests autonomous web research, K2 Thinking scored 60.2% versus GPT-5's 54.9%. On Humanity's Last Exam (with tools), K2 Thinking achieved 44.9% compared to GPT-5's 41.7%.

For research workflows, data gathering, and tasks requiring iterative tool use, K2 represents a genuinely competitive open-source option. Nathan Lambert from Interconnects.ai noted that K2 Thinking represents "the closest open models have been to the closed frontier of performance ever."

Vs. DeepSeek, GPT-4, Claude

K2’s relative performance differs depending on the task:

Where K2 leads:

• Competitive programming (83.1% on LiveCodeBench v6)

• Agentic search and browsing tasks

• Long-horizon multi-step workflows

• Cost efficiency (significant margin)

Where K2 trails:

• Repository-level bug fixing (GPT-5 and Claude maintain small leads on SWE-Bench Verified)

• Multimodal tasks (Claude Opus 4.5 leads on MMMU at 84.2%)

• Terminal-based coding tasks (mid-pack performance)

DeepSeek remains slightly stronger on pure mathematical competitions and algorithmic puzzles. Claude and GPT-5 maintain edges in complex repository-scale engineering. But the gap has narrowed substantially, and K2's pricing advantage makes it viable for many production use cases where absolute peak performance isn't required.

Community Feedback on X: Highlights and Observations

Kimi K2 drew strong interest for its benchmark performance and agentic capabilities. The K2.5 release generated positive reaction among AI/dev users  -  lots of surprise at the open-source SOTA benchmarks, Agent Swarm hype, strong coding/vision capabilities, and free/local access - mixed with some conspiracy-flavored skepticism about how China achieved it so quickly. The official @Kimi_Moonshot announcement post itself got massive engagement. 

Agent Swarm

A prominent feature of K2.5 is the Agent Swarm, which can coordinate up to 100 sub-agents in parallel and perform up to 1,500 tool calls. This approach enables the model to handle complex, multi-step tasks more efficiently than a single-agent setup. 

Users have highlighted its effectiveness in research, coding, and automated self-review workflows, noting how parallelism reduces runtime and manages complex dependencies.

Practical Productivity and Multimodal Use

K2.5 has also been applied in practical productivity scenarios, showing versatility across coding and multimodal tasks:

• Generating static sites or structured web content from prompts, with attention to style and layout.

• Translating visual inputs, sketches, or 3D designs into functional HTML/CSS/JS outputs.

• Performing multi-step agentic workflows involving Markdown, LaTeX, and tool coordination for documentation or reporting.

Criticisms and Limitations

Some users report content censorship, experience friction when integrating external tools, and observe occasional overhype compared to everyday interactions.

• Content restrictions: The model can refuse certain prompts outside coding tasks, which may limit flexibility for creative or exploratory work.

• Integration challenges: Non-native environments can introduce friction, particularly when connecting to third-party APIs or handling specific file formats.

  
  

• Conversation naturalness: Casual interactions may feel less fluid than structured tasks, and context handling can occasionally be inconsistent.

The Future of Agentic AI with Kimi K2

Kimi K2 demonstrates how agentic AI can handle complex reasoning and tool integration, while K2.5 expands on this with multimodal inputs and coordinated Agent Swarms for faster, parallel task execution.

Open-Source Momentum & Community Impact

The Modified MIT License allows commercial use with one significant caveat: attribution requirements kick in for products exceeding 100 million monthly users or $20 million in monthly revenue. For the vast majority of developers and businesses, this effectively means unrestricted use.

Community activity around K2 continues growing. Hugging Face downloads, while lower than DeepSeek V3, have been increasing. Fine-tuned variants targeting specific domains are appearing, and integration support in major frameworks continues expanding.

Potential for Visual & Swarm Agents

K2.5’s multimodal features and Agent Swarm approach show where Moonshot is heading. By combining vision understanding with coordinated multi-agent execution, it can handle more complex workflows. 

Early examples include generating frontend interfaces from video references, managing office tasks through conversation, and assisting with visual debugging.

Your Next Move

Kimi K2 offers a trillion-parameter open-source option for agentic tasks, handling multi-step coding, extended reasoning, and long-context workflows. You should note that content filtering can block some prompts, and integrating external tools outside its native setup may need extra attention. 

For cases where autonomous tool use or cost efficiency matters, it’s a solid choice. Proprietary models still have edges in large-scale reliability and seamless multimodal integration, but K2 shows that open-source can now handle serious workloads.

You can also connect with us to explore Kimi K2 integrations, experiment with agentic workflows, and get guidance on using open-source AI for coding, research, and automation tasks.

Frequently Asked Questions

What makes Kimi K2 different from other open-source AI models?

Kimi K2 uses a Mixture-of-Experts (MoE) architecture with a trillion parameters, activating only 32 billion per token. It’s designed for agentic tasks, enabling multi-step reasoning, autonomous tool use, and long-context workflows, which most other open models don’t prioritize.

How do I run Kimi K2.5 locally / offline on my hardware?

Yes, it's open-weights on Hugging Face (moonshotai/Kimi-K2.5). Use Unsloth's guide for quantized versions (unsloth.ai/docs/models/kimi-k2.5) - needs high RAM/VRAM (often 200+ GB total for full speed). Ollama supports pulls (e.g., ollama run kimi-k2.5), MLX/Exo works great on Macs (24+ tok/sec on dual M3 Ultra). Native INT4 quantization helps efficiency, but it's heavy - start with quantized 4-bit for testing.

How can I use Kimi K2.5 for coding (VS Code, Kimi Code, etc.)?

Easiest: Install "Kimi Code" VS Code extension → sign in with Moonshot API key (free trial week on lower tiers at platform.moonshot.ai). Works via OpenAI-compatible API. Also integrates with Kilo Code (free tier, #1 on OpenRouter for some), Claude Code / OpenCode (add endpoint manually). Users report strong agentic coding, fewer loops than Opus 4.5, excellent "Code with Taste" for aesthetic UIs.

How does Kimi K2.5 compare to Claude Opus 4.5, GPT-5, etc.?

Often ~90% of Opus 4.5 / GPT-5.2 level in real coding/agent tasks; beats them on some open benchmarks (SWE-bench Verified 76.8%, MMMU Pro 78.5%, HLE 50.2%). Wins big on vision/multimodal (VideoMMMU, OCRBench) and Agent Swarm speed. Cheaper/faster (API ~1/5 Claude pricing), open-source edge. Some call it "benchmaxed" or note gaps in casual chat/naturalness.

What about censorship or content filtering in Kimi K2.5?

Yes, noticeable (common in Chinese open-weights) - more conservative on sensitive/non-coding prompts, refuses or deflects sometimes. Less issue for dev, coding, agentic, or technical tasks. Users test boundaries or use creative prompting to bypass in edge cases.

How do I access/try Agent Swarm, and is it good?

Live on kimi.com (chat/agent mode); full Swarm beta for higher-tier subscribers (up to 100 parallel sub-agents, 1,500 tool calls, ~4.5× faster on complex/multi-step tasks). Self-orchestrates agents dynamically - great for research, multi-company analysis, self-fixing code, visual workflows. Many call it a game-changer vs single-agent models; pair with Kimi Code for best dev use.