Accelerated cloud API reasoner fournissant élevé-vitesse chaîne de pensée deductions à abordable commercial tarification.
Technical Specifications
- Provider
- Moonshot AI
- Category
- reasoning
- Release Date
- 2025-11
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- API Pay-as-you-go
- 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-turbo
- Supported SDK & API Protocol
- Moonshot AI REST API (OpenAI-Compatible)
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.6/1M in) across full 262K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.06/1M tokens
- 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
- API Prompt Engineering & RAG Distillation
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed Moonshot AI Cloud API
- System Card & Safety Framework
- Moonshot AI Production Safety & Alignment Verified
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Kimi K2 Thinking Turbo Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~179 requests/day equals $20/month.
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 Turbo:
Benchmark Scores & 6-Axis Capability Radar
- GPQA Diamond
- 68.5%
- MMLU-Pro
- 81.5%
- HumanEval
- 92%
- AIME
- 81%
- Chatbot Arena Elo
- 1365pts
Strengths
- Significantly plus rapide temps-à-premier-token que auto-hébergé K2 pensée setups
- coût-effective raisonnement API tarification à $0.60 par millions entrée tokens
- Dependable sorties structurées pour multi-agent outil boucles
Limitations & Trade-offs
- propriétaire hosted API rather que à poids ouverts
- pensée trace cannot be fully disabled sur simple prompts
Editorial Assessment
un rapide, abordable hosted raisonnement endpoint qui balances pensée profondeur avec vitesse.
Best for: production raisonnement pipelines, automatisé code reviews, et faible-latence recherche bots
Compare Kimi K2 Thinking Turbo
Frequently Asked Questions about Kimi K2 Thinking Turbo
How much does the Kimi K2 Thinking Turbo API cost per 1M tokens?+
Kimi K2 Thinking Turbo by Moonshot AI is priced at $0.6 per 1M input tokens and $2.4 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.06 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Kimi K2 Thinking Turbo 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 Turbo's context window?+
Kimi K2 Thinking Turbo 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 16,384 tokens.
Can I run Kimi K2 Thinking Turbo locally on my own GPU (Open Weights)?+
No, Kimi K2 Thinking Turbo is a proprietary closed-weights model hosted by Moonshot AI. It cannot be downloaded for local GPU execution and must be accessed via Moonshot AI's official API or cloud platform.
Does Moonshot AI train on data sent to the Kimi K2 Thinking Turbo API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (Moonshot AI Terms). Full commercial ownership of generated outputs. Supported deployment channels include: Moonshot AI Cloud API & Enterprise Endpoints.
How does Kimi K2 Thinking Turbo perform on coding and reasoning benchmarks?+
Kimi K2 Thinking Turbo achieves a composite Intelligence Index of 84/100, scoring N/A% on SWE-bench Verified, 68.5% on GPQA Diamond, 81.5% on MMLU-Pro, and 1365 pts on Chatbot Arena Elo.
What is Kimi K2 Thinking Turbo best used for?+
Kimi K2 Thinking Turbo is best suited for: production raisonnement pipelines, automatisé code reviews, et faible-latence recherche bots. Assessment: un rapide, abordable hosted raisonnement endpoint qui balances pensée profondeur avec vitesse.
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
Kimi K2
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Kimi K2 Thinking Turbo:
L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.
Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).
Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.
Un modèle d'IA entraîné par apprentissage par renforcement pour allouer du temps de calcul à la réflexion logique avant d'émettre une solution, idéal pour les problèmes scientifiques et le code complexe.
Une méthode de raisonnement pas-à-pas où le modèle décompose le problème en étapes logiques intermédiaires avant de conclure, augmentant drastiquement la précision en mathématiques et programmation.
La capacité d'un LLM à déterminer quand une action externe est requise et à générer des arguments structurés pour invoquer des API, calculatrices ou bases de données avant de poursuivre.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.