Kimi-K2-Thinking on Novita AI: Open-Source Reasoning Model That Outperforms GPT-5

Kimi-K2-Thinking, Moonshot AI’s groundbreaking open-source reasoning model, is now available on Novita AI. This state-of-the-art “thinking agent” combines deep multi-step reasoning with extensive tool orchestration, executing 200–300 sequential tool calls without human intervention. With 1 trillion total parameters, 32 billion activated parameters, and a 256,000-token context window, K2-Thinking sets new standards in agentic intelligence while remaining fully accessible as an open-weight model.

Current pricing for Kimi-K2-Thinking on Novita AI: $0.60 / M input tokens, $2.50 / M output tokens

What is Kimi-K2-Thinking?

Kimi-K2-Thinking is Moonshot AI’s most advanced open-source reasoning model, built as a “thinking agent” that reasons step-by-step while dynamically invoking tools. Unlike traditional reflex-grade models, K2-Thinking employs extended chain-of-thought reasoning across hundreds of steps, making it ideal for complex problem-solving that requires sustained focus and tool orchestration.

Deep Thinking & Tool Orchestration

K2-Thinking is end-to-end trained to interleave chain-of-thought reasoning with function calls, enabling autonomous research, coding, and writing workflows that last hundreds of steps without drift. The model can execute 200–300 sequential tool calls in a single session while maintaining coherent reasoning across the entire process.

Native INT4 Quantization

Quantization-Aware Training (QAT) is employed in the post-training stage to achieve lossless 2x speed-up in low-latency mode. This native INT4 quantization allows K2-Thinking to support efficient inference with roughly double the generation speed while achieving state-of-the-art performance.

Extended Context Window

K2-Thinking supports a 256,000-token context window, enabling it to process lengthy documents, maintain context across extended conversations, and handle complex multi-turn reasoning tasks that require substantial context retention.

Technical Architecture and Specifications

Kimi-K2-Thinking represents cutting-edge engineering in mixture-of-experts architecture, optimized specifically for reasoning tasks:

Specification	Value
Architecture	Mixture-of-Experts (MoE)
Total Parameters	1 Trillion
Activated Parameters	32 Billion
Context Length	256,000 tokens
Number of Layers	61 (including 1 dense layer)
Attention Mechanism	MLA (Multi-Head Latent Attention)
Number of Experts	384
Selected Experts per Token	8
Vocabulary Size	160,000
Activation Function	SwiGLU
Quantization	Native INT4 with QAT
Recommended Temperature	1.0

This sophisticated architecture enables efficient processing while maintaining the full power of the trillion-parameter model through intelligent expert selection and native quantization support.

Benchmark Performance: Leading Open-Source Model

Kimi-K2-Thinking demonstrates exceptional performance across reasoning, agentic, and coding benchmarks, often surpassing proprietary models like GPT-5 and Claude Sonnet 4.5:

Reasoning Tasks

Benchmark	Setting	K2 Thinking	GPT-5	Claude Sonnet 4.5 (Thinking)	K2 0905	DeepSeek-V3.2	Grok-4
HLE (Text-only)	no tools	23.9	26.3	19.8*	7.9	19.8	25.4
	w/ tools	44.9	41.7*	32.0*	21.7	20.3*	41.0
	heavy	51.0	42.0	–	–	–	50.7
AIME25	no tools	94.5	94.6	87.0	51.0	89.3	91.7
	w/ python	99.1	99.6	100.0	75.2	58.1*	98.8
	heavy	100.0	100.0	–	–	–	100.0
HMMT25	no tools	89.4	93.3	74.6*	38.8	83.6	90.0
	w/ python	95.1	96.7	88.8*	70.4	49.5*	93.9
	heavy	97.5	100.0	–	–	–	96.7
IMO-AnswerBench	no tools	78.6	76.0*	65.9*	45.8	76.0*	73.1
GPQA	no tools	84.5	85.7	83.4	74.2	79.9	87.5

General Tasks

Benchmark	Setting	K2 Thinking	GPT-5	Claude Sonnet 4.5 (Thinking)	K2 0905	DeepSeek-V3.2
MMLU-Pro	no tools	84.6	87.1	87.5	81.9	85.0
MMLU-Redux	no tools	94.4	95.3	95.6	92.7	93.7
Longform Writing	no tools	73.8	71.4	79.8	62.8	72.5
HealthBench	no tools	58.0	67.2	44.2	43.8	46.9

Agentic Search Tasks

Benchmark	Setting	K2 Thinking	GPT-5	Claude Sonnet 4.5 (Thinking)	K2 0905	DeepSeek-V3.2
BrowseComp	w/ tools	60.2	54.9	24.1	7.4	40.1
BrowseComp-ZH	w/ tools	62.3	63.0*	42.4*	22.2	47.9
Seal-0	w/ tools	56.3	51.4*	53.4*	25.2	38.5*
FinSearchComp-T3	w/ tools	47.4	48.5*	44.0*	10.4	27.0*
Frames	w/ tools	87.0	86.0*	85.0*	58.1	80.2*

Coding Tasks

Benchmark	Setting	K2 Thinking	GPT-5	Claude Sonnet 4.5 (Thinking)	K2 0905	DeepSeek-V3.2
SWE-bench Verified	w/ tools	71.3	74.9	77.2	69.2	67.8
SWE-bench Multilingual	w/ tools	61.1	55.3*	68.0	55.9	57.9
Multi-SWE-bench	w/ tools	41.9	39.3*	44.3	33.5	30.6
SciCode	no tools	44.8	42.9	44.7	30.7	37.7
LiveCodeBenchV6	no tools	83.1	87.0*	64.0*	56.1*	74.1
OJ-Bench (cpp)	no tools	48.7	56.2*	30.4*	25.5*	38.2*
Terminal-Bench	w/ simulated tools (JSON)	47.1	43.8	51.0	44.5	37.7

Note: Bold denotes best performance in each category. Asterisks (*) indicate scores directly from model tech reports or blogs. K2-Thinking demonstrates leading performance across reasoning, agentic search, and coding tasks, establishing itself as the top open-source reasoning model.

Key Features and Capabilities

Autonomous Multi-Step Reasoning

K2-Thinking excels at complex tasks requiring sustained reasoning across hundreds of steps. The model can autonomously plan, execute, verify, and adapt its approach while maintaining task coherence throughout the entire process.

Extensive Tool Orchestration

The model can execute 200–300 sequential tool calls in a single session, enabling it to:

• Search and retrieve information from multiple sources
• Execute code and verify results
• Navigate web browsers for research tasks
• Access databases and APIs
• Coordinate multiple tools for complex workflows

Separate Reasoning Stream

K2-Thinking exposes its internal reasoning process through a separate reasoning_content field in the API response, allowing developers to understand and inspect how the model arrives at its conclusions. This transparency is valuable for debugging, validation, and understanding model behavior.

Production-Ready Optimization

With native INT4 quantization achieved through Quantization-Aware Training, K2-Thinking delivers:

• 2x generation speed improvement
• Reduced GPU memory requirements
• Maintained accuracy with lossless quantization
• Cost-effective inference at scale

Open-Weight Accessibility

Released under a modified MIT license, K2-Thinking is fully open-weight and accessible for research, development, and commercial applications. The model can be downloaded, fine-tuned, and deployed locally or via API.

How to Access Kimi-K2-Thinking on Novita AI

Getting started with Kimi-K2-Thinking is fast, simple, and affordable on Novita AI.

Use the Playground (No Coding Required)

• Instant Access: Sign up, and start experimenting with Kimi-K2-Thinking and other top models in seconds.
• Interactive UI: Experience the model’s deep reasoning capabilities through the intuitive interface.
• Reasoning Transparency: View the model’s step-by-step thinking process in real-time.
• Model Comparison: Effortlessly switch between Kimi-K2-Thinking and other top models to find the perfect fit for your needs.

Explore Kimi-K2-Thinking Demo Now

Integrate via API (For Developers)

Seamlessly connect Kimi-K2-Thinking to your applications, workflows, or chatbots with Novita AI’s unified REST API—no need to manage model weights or infrastructure.

Option 1: Direct API Integration (Python Example)

To get started, simply use the code snippet below:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    api_key="<YOUR_API_KEY>",
)

model = "moonshotai/kimi-k2-thinking"
stream = True  # or False
max_tokens = 262144
system_content = "You are Kimi, an AI assistant created by Moonshot AI."
temperature = 1.0
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = {"type": "text"}

chat_completion_res = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": system_content,
        },
        {
            "role": "user",
            "content": "Which one is bigger, 9.11 or 9.9? Think carefully.",
        }
    ],
    stream=stream,
    max_tokens=max_tokens,
    temperature=temperature,
    top_p=top_p,
    presence_penalty=presence_penalty,
    frequency_penalty=frequency_penalty,
    response_format=response_format,
    extra_body={
        "top_k": top_k,
        "repetition_penalty": repetition_penalty,
        "min_p": min_p
    }
)

if stream:
    for chunk in chat_completion_res:
        print(chunk.choices[0].delta.content or "", end="")
else:
    print(chat_completion_res.choices[0].message.content)
    # Access the reasoning process
    print("=====Reasoning Process=====")
    print(chat_completion_res.choices[0].message.reasoning_content)

Key Features:

• Unified endpoint: /v3/openai supports OpenAI’s Chat Completions API format.
• Flexible controls: Adjust temperature, top-p, penalties, and more for tailored results.
• Streaming & batching: Choose your preferred response mode.
• Reasoning access: View the model’s internal thinking via reasoning_content field.

Option 2: Multi-Agent Workflows with OpenAI Agents SDK

Build advanced multimodal agent systems by integrating Novita AI with the OpenAI Agents SDK:

• Plug-and-play: Use Kimi-K2-Thinking in any OpenAI Agents workflow.
• Supports handoffs, routing, and tool use: Design agents that can reason deeply, delegate tasks, or run functions.
• Python integration: Simply point the SDK to Novita’s endpoint (https://api.novita.ai/v3/openai) and use your API key for seamless agent workflows.

Option 3: Connect Kimi-K2-Thinking API on Third-Party Platforms

• Hugging Face: Use Kimi-K2-Thinking in Spaces, pipelines, or with the Transformers library via Novita AI endpoints.
• Agent & Orchestration Frameworks: Easily connect Novita AI with partner platforms like Continue, AnythingLLM, LangChain, Dify, and Langflow through official connectors and step-by-step integration guides.
• OpenAI-Compatible API: Enjoy hassle-free migration and integration with tools such as Cline, Cursor, Trae and Qwen Code, designed for the OpenAI API standard.
• Anthropic-Compatible API: Seamlessly integrate with Claude Code for agentic coding workflows and other Anthropic API-compatible tools.

Use Cases and Applications

Advanced Problem Solving

K2-Thinking excels at PhD-level mathematics, complex reasoning tasks, and multi-disciplinary questions that require deep domain knowledge and sustained analytical thinking across hundreds of reasoning steps.

Autonomous Research Agents

• Information synthesis: Gather, analyze, and synthesize information from multiple sources
• Fact verification: Cross-reference claims across documents and databases
• Literature review: Analyze academic papers and extract key findings
• Competitive intelligence: Research market trends and competitor strategies

Complex Coding Tasks

• System design: Architect complete applications from requirements
• Bug investigation: Debug complex issues through systematic analysis
• Code refactoring: Improve codebases with architectural-level changes
• Frontend development: Create responsive, component-rich web applications

Long-Horizon Workflows

• Document analysis: Process and understand lengthy technical specifications
• Codebase exploration: Navigate and comprehend large software projects
• Multi-step automation: Coordinate complex workflows across multiple tools
• Strategic planning: Develop comprehensive strategies with detailed action plans

Creative and Technical Writing

K2-Thinking delivers enhanced performance in longform writing tasks, producing coherent, well-structured content that maintains consistency across extended outputs.

Conclusion

Kimi-K2-Thinking represents a pivotal moment in open-source AI development, bringing frontier-level reasoning capabilities to the developer community. Its combination of deep multi-step reasoning, extensive tool orchestration, and transparent thinking process makes it an ideal choice for building sophisticated AI agents and applications that require sustained analytical thinking.

With state-of-the-art performance that meets or exceeds proprietary models like GPT-5 and Claude Sonnet 4.5, native INT4 quantization for efficient inference, and a 256,000-token context window, K2-Thinking offers unparalleled value for developers pushing the boundaries of agentic AI.

Try the Kimi-K2-Thinking Demo on Novita AI today and experience the future of open-source reasoning intelligence!

Frequently Asked Questions

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