Kimi K2 Thinking Turbo vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Kimi K2 Thinking Turbo by Moonshot AI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Kimi K2 Thinking Turbo
Penseur rapideAccelerated cloud API reasoner fournissant élevé-vitesse chaîne de pensée deductions à abordable commercial tarification.
$0.60 / 1M
$2.40 / 1M
262.1K tokens
N/A
Gemini 4 Argon
Roi multimodal avec sortie 1MGoogle de DeepMind phare de pointe raisonnement moteur intégrant 1-millions-token sortie limits, autonome cybersecurity defense, et en temps réel audio/vision ingestion.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Différences Clés en un Coup d’Œil
- Kimi K2 Thinking Turbo is 3.9x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 262K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 90 tok/s.
| Caractéristique | Kimi K2 Thinking Turbo | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 84 | 97 |
Provider | Moonshot AI | |
Input Price Cost per million input tokens | $0.60 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.40 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 1000K |
Throughput Speed Tokens generated per second | 90 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 81.9% |
GPQA Diamond Graduate-level reasoning | 68.5% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.10 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K2 Thinking Turbo · Strengths & Key Advantages
- 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
Considerations & Trade-offs
- propriétaire hosted API rather que à poids ouverts
- pensée trace cannot be fully disabled sur simple prompts
Gemini 4 Argon · Strengths & Key Advantages
- Groundbreaking 1,000,000 token maximum sortie génération capacité via long Decode Continuation
- 1M-token entrée window avec 99.7% GraphWalks recall (84.2% dans le 256K-1M range)
- natif traitement de complet audio, vidéo streams, et visuel données (91.7% sur LVBench)
- Half le token prix de d'Anthropic Opus 5.5 et #1 sur Chatbot Arena (1525 Elo)
Considerations & Trade-offs
- massif token outputs peut incur élevé absolute coûts if unconstrained (~62K sortie tokens par index tâche)
- $2/$10 tarification est un introductory 50% discount; standard rates revert à $4/$20 once le promotion ends
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Kimi K2 Thinking Turbo | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.18 | $2.45 |
Output Tokens / Task Tokens generated per task | 22K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 10K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Kimi K2 Thinking Turbo | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | kimi-k2-thinking-turbo | models/gemini-4-argon |
| API Protocol | Moonshot AI REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-11 | 2026-09 |
| Knowledge Cutoff | September 2025 | June 2026 |
| Architecture | Hosted Accelerated Reasoning Engine | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.6/1M in) across full 262K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Moonshot AI Production Safety & Alignment Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision, audio, video |
| Output Modalities | text | text |
Kimi K2 Thinking Turbo vs Gemini 4 Argon: Réponses Rapides (FAQ)
Which is more intelligent, Kimi K2 Thinking Turbo or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 84 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Kimi K2 Thinking Turbo is cheaper at $0.40 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Kimi K2 Thinking Turbo has lower latency, with a time to first token of 0.52s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Verdict
Choose Kimi K2 Thinking Turbo for production raisonnement pipelines, automatisé code reviews, et faible-latence recherche bots, or choose Gemini 4 Argon for whole-dépôt génération de code, complet-book synthèse, vidéo compréhension, et multi-hour audio traitement.
Questions fréquentes
Which is cheaper: Kimi K2 Thinking Turbo or Gemini 4 Argon?+
Kimi K2 Thinking Turbo costs $0.6/1M input and $2.4/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Kimi K2 Thinking Turbo or Gemini 4 Argon?+
Kimi K2 Thinking Turbo supports 262K tokens whereas Gemini 4 Argon supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Moonshot AI & Google Official Documentation