especializado deliberate modelo de razonamiento trained con reinforcement learning para slow investigación, multi-hour plan formation, y matemáticas proofs.
Technical Specifications
- Provider
- Moonshot AI
- Category
- reasoning
- Release Date
- 2025-11
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- kimi-k2-thinking
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 620 GB VRAM (4-bit Quantized) · 2150 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Kimi K2 Thinking Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Kimi K2 Thinking:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 50%
- GPQA Diamond
- 70%
- MMLU-Pro
- 82.5%
- HumanEval
- 93%
- AIME
- 82.5%
- Chatbot Arena Elo
- 1375pts
Strengths
- Exhaustive paso un paso matemático razonamiento y verificación bucles
- Spacious 262K ventana de contexto accommodating denso científico whitepapers
- de pesos abiertos lanzamiento con unrestricted académico y comercial utility
Limitations & Trade-offs
- extendido razonamiento chains increase response latencia noticeably
- Overly verbose en simple conversacional requests
Editorial Assessment
un extraordinary abierto deliberate reasoner diseñado para complejo problem-solving.
Best for: formal matemático proofs, algorithmic arquitectura, y comprehensive científico investigación
Compare Kimi K2 Thinking
Frequently Asked Questions about Kimi K2 Thinking
How much does the Kimi K2 Thinking API cost per 1M tokens?+
Kimi K2 Thinking by Moonshot AI is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the Kimi K2 Thinking pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Kimi K2 Thinking's context window?+
Kimi K2 Thinking features a 262K tokens context window, which equals approximately 196,608 English words or ~524 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 32,000 tokens.
Can I run Kimi K2 Thinking locally on my own GPU (Open Weights)?+
Yes, Kimi K2 Thinking (1T total (32B active MoE)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Moonshot AI train on data sent to the Kimi K2 Thinking API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Kimi K2 Thinking perform on coding and reasoning benchmarks?+
Kimi K2 Thinking achieves a composite Intelligence Index of 85/100, scoring 50% on SWE-bench Verified, 70% on GPQA Diamond, 82.5% on MMLU-Pro, and 1375 pts on Chatbot Arena Elo.
What is Kimi K2 Thinking best used for?+
Kimi K2 Thinking is best suited for: formal matemático proofs, algorithmic arquitectura, y comprehensive científico investigación. Assessment: un extraordinary abierto deliberate reasoner diseñado para complejo problem-solving.
Moonshot AI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Moonshot AI's flagship reasoning tier:
Kimi K3
Kimi K2.7 Code
Kimi K2.6
Kimi K2.5
Kimi K2 Thinking Turbo
Kimi K2
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Kimi K2 Thinking:
La unidad básica de texto que procesa un modelo de lenguaje, que normalmente equivale a 3 o 4 caracteres o aproximadamente 0,75 palabras. Por ejemplo, una frase típica se divide en varios tokens.
Un conjunto estandarizado de pruebas para evaluar y comparar objetivamente las capacidades de distintos modelos de IA en tareas como programación (SWE-bench) o matemáticas avanzadas (AIME).
La cantidad máxima de tokens que un modelo puede considerar a la vez, incluyendo tanto la entrada que lee como la salida que genera. Una ventana de 1.000.000 de tokens puede procesar unas 750.000 palabras en una sola petición.
Un modelo de IA entrenado para invertir cómputo adicional en pensar y validar hipótesis lógicas antes de responder, diseñado para problemas complejos de ingeniería y matemáticas.
Una técnica de razonamiento paso a paso donde el modelo genera pensamientos intermedios antes de formular la respuesta final, mejorando radicalmente la resolución de problemas matemáticos y lógicos.
La capacidad de un modelo de detectar la necesidad de una función externa y emitir argumentos JSON estructurados para ejecutar API, búsquedas web o consultas a bases de datos.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.