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Kimi K2 Thinking
- Instruction Following
Kimi K2 Thinking is MoonshotAI’s most advanced open-source reasoning model, designed as a long-horizon “thinking agent” that interleaves step-by-step reasoning with tool use. It is notable for its trillion-parameter Mixture-of-Experts architecture, strong benchmark performance, and ability to maintain coherent behavior across hundreds of tool calls within a 256k-token context window.
About the model
What is Kimi K2 Thinking?
Kimi K2 Thinking is a large-scale open-source Mixture-of-Experts language model from MoonshotAI optimized for deep, tool-using reasoning. It is mainly used for complex agentic research workflows, long-horizon coding and debugging, and advanced mathematical or scientific problem-solving that require many sequential reasoning steps. It also supports applications like autonomous writing and analysis, web browsing with information synthesis, and multi-step tool orchestration for production agents. It belongs to MoonshotAI’s Kimi K2 family of models, extending the original Kimi K2 series toward more powerful open reasoning and agent capabilities.
Model capabilities
5 Core Capabilities
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Advanced Reasoning
Performs multi-step logical reasoning on complex, expert-level problems, leveraging extended thinking tokens and tool use for accurate conclusions.
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Agentic Tool Use
Acts as a thinking agent, autonomously planning and executing long tool-call sequences to solve intricate tasks without human intervention.
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Coding Assistance
Handles software engineering tasks, including code comprehension, generation, and debugging, using agentic workflows and reasoning-driven improvements.
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Knowledge-Rich Writing
Generates detailed, coherent written content across domains, combining strong knowledge retrieval with stepwise reasoning for high-quality outputs.
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Long-Context Handling
Processes very long inputs with a large context window, maintaining coherence and leveraging prior details for better task performance.
Use cases
6 Most Valuable Use Cases
- Autonomous Research Workflows
- Complex Code Generation
- Mathematical Problem Solving
- Tool-Orchestrated Automation
- Long-Context Document Analysis
- Agentic Reasoning Benchmarks
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Kimi K2–class reasoning models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 220ms | 120 tps | 99.99% | $0.25 | $0.75 | 256K |
| MoonshotAI | CN / Global | ~320ms | ~70 tps | ~99.9% | ~$0.40 | ~$1.20 | ~200K |
| OpenAI (o3-mini) | Global | ~350ms | ~80 tps | 99.9% | ~$1.10 | ~$4.40 | 200K |
| Anthropic (Claude 3.7 Sonnet Thinking-equivalent) | US / EU | ~380ms | ~60 tps | 99.9% | ~$1.20 | ~$4.80 | 200K |
| Google Cloud (Gemini 2.0 Pro Thinking-equivalent) | Global | ~340ms | ~75 tps | 99.9% | ~$0.90 | ~$3.60 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Kimi K2 Thinking | GPT-4.1 | Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~900ms | ~700ms | ~800ms |
| Context Window | 200K | 128K | 200K |
| Input Price ($/1M) | $2.00 | $5.00 | $3.00 |
| Output Price ($/1M) | $6.00 | $15.00 | $15.00 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 40 tps | 60 tps | 50 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (30 days)
- 7.8B
- Completion tokens generated (30 days)
- 9.6M
- API requests served (30 days)
- 99.8%
- Avg uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Intelligently route each request across models and providers based on latency, cost, or quality. One integration that always picks the best path for you.
Smart multi-model routing -
Cost-Aware Orchestration
Define budget and quality targets, then let LLM.API choose the optimal models. Automatically downgrade, upgrade, or mix providers to keep spend under control.
Optimize every token -
Automatic Fallbacks
Configure policy-based failover across regions and providers. When a model errors or times out, LLM.API seamlessly retries on backups without changing your code.
Resilience by default -
Deep Observability
Centralize logs, traces, metrics, and cost for every provider in one place. Quickly debug prompts, spot regressions, and understand real-world model performance.
See every request -
Task-Level Abstractions
Describe tasks—chat, scoring, extraction—once and let LLM.API match them to the right models and prompts. Ship features faster with consistent, reusable interfaces.
From models to tasks -
High-Throughput Batching
Send thousands of requests in a single batch with built-in rate control and retries. Maximize throughput while staying within provider limits and budgets.
Scale without throttling
Decision guide
When to Use — When NOT to Use
Use it if...
- You need strong Chinese-language reasoning and analysis for complex, technical or academic tasks.
- You need an LLM optimized for multi-step thinking rather than lightweight chat or tooling.
- Your use case involves exploratory research, brainstorming, and structured problem decomposition in Chinese.
- Your use case involves long-form analytical writing, reports, or explanations in Chinese contexts.
- You need a model from a China-based provider for data residency or localization.
- Your use case involves comparing or ensemble-running multiple Chinese LLMs for robustness.
Avoid if...
- You need an English-first model with state-of-the-art performance across many global benchmarks.
- Your workload requires tight integration with US-centric ecosystems, tooling, and compliance workflows.
- You need guaranteed low latency and highly optimized inference infrastructure outside mainland China.
- Your workload requires fully transparent, English-language documentation, benchmarks, and operational playbooks.
- You need mature, widely adopted SDKs, plugins, and community support across many languages.
- Your workload requires fine-tuning or custom training pipelines not exposed by MoonshotAI.
FAQ
Frequently Asked Questions
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What is Kimi K2 Thinking?
Kimi K2 Thinking is a MoonshotAI large language model focused on complex reasoning and problem-solving, exposed via the unified LLM.API gateway.
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What is Kimi K2 Thinking best suited for?
Kimi K2 Thinking is best for multi-step reasoning, code understanding, data analysis, and agent-style tool workflows where correctness matters more than raw speed.
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What is the context window of Kimi K2 Thinking?
Kimi K2 Thinking supports a large context window suitable for long documents and multi-step conversations; check LLM.API model docs for the exact current limit.
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How fast is Kimi K2 Thinking in terms of latency and throughput?
Latency depends on prompt size and load, but Kimi K2 Thinking is optimized for streaming responses with competitive first-token and throughput performance.
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What modalities does Kimi K2 Thinking support?
Kimi K2 Thinking currently supports text input and output via LLM.API; use a separate MoonshotAI or LLM.API vision model for image understanding.
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How is Kimi K2 Thinking priced on LLM.API?
LLM.API charges per input and output token for Kimi K2 Thinking; see the LLM.API pricing page for the latest exact rates.
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How do I call Kimi K2 Thinking through LLM.API?
Set the model parameter to the Kimi K2 Thinking identifier in LLM.API’s /chat or /completions endpoint and authenticate with your LLM.API key.
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How does Kimi K2 Thinking compare to similar reasoning-focused models?
Kimi K2 Thinking emphasizes careful reasoning and tool-use over raw speed, often outperforming generic chat models on complex multi-step logic problems.
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Does Kimi K2 Thinking support function calling or tools via LLM.API?
Yes, you can define tools/functions in your LLM.API request and let Kimi K2 Thinking decide when and how to call them.
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What are the main limitations of Kimi K2 Thinking?
Kimi K2 Thinking can hallucinate, lacks real-time knowledge, may be slower on large prompts, and should not be used as a sole source for critical decisions.