Command A Plus vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Command A Plus by Cohere against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
İstediğiniz Modelleri Seçin ve Karşılaştırın
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Live comparison: no in-depth editorial review has been published for this pair yet.
Command A Plus
Açık Ağırlıklı ModelCohere tarafından geliştirilen Command A Plus, 128K token bağlam penceresi ve 1M girdi başına $3, 1M çıktı başına $12 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$3.00 / 1M
$12.00 / 1M
128K tokens
60.5%
Kimi K3
Açık Ağırlıklı ModelMoonshot AI tarafından geliştirilen Kimi K3, 1.0M token bağlam penceresi ve 1M girdi başına $0, 1M çıktı başına $0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Bir Bakışta Temel Farklar
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 128K tokens).
- Command A Plus is faster at 68 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 60.5%).
| Feature | Command A Plus | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 93 |
Provider | Cohere | Moonshot AI |
Input Price Cost per million input tokens | $3.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $12.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.33 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 33K | 131K |
Throughput Speed Tokens generated per second | 68 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 60.5% | 74.5% |
GPQA Diamond Graduate-level reasoning | 74% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 1M | $0.00 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command A Plus · Strengths & Key Advantages
- Cohere imzalı gelişmiş Flagship Multimodal Agentic MoE mimarisi ve 128K token bağlam kapasitesi
- Dengeli API maliyeti ($3/1M girdi, $12/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Kimi K3 · Strengths & Key Advantages
- Moonshot AI imzalı gelişmiş Colossus-Scale Multimodal Sparse Mixture-of-Experts mimarisi ve 1.0M token bağlam kapasitesi
- Dengeli API maliyeti ($0/1M girdi, $0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Command A Plus | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.85 | $2.00 |
Output Tokens / Task Tokens generated per task | 45K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 18K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Command A Plus | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | command-a-plus | kimi-k3 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-05 | 2026-07 |
| Knowledge Cutoff | March 2026 | March 2026 |
| Architecture | Flagship Multimodal Agentic MoE | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential (~180B+ MoE) | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($3/1M in) across full 128K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 112 GB VRAM (4-bit Quantized) · 387 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Command A Plus vs Kimi K3: Hızlı Yanıtlar (SSS)
Which is more intelligent, Command A Plus or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 58 on the composite Intelligence Index.
Which model is faster?
Command A Plus is faster, generating 68 tokens per second compared with 58 tokens per second.
Which model is cheaper to run?
Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $2.33 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command A Plus has lower latency, with a time to first token of 0.56s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 128K tokens.
Sonuç
Choose Command A Plus for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve cohere tabanlı üretim sistemleri, or choose Kimi K3 for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve moonshot ai tabanlı üretim sistemleri.
Sıkça Sorulan Sorular (SSS)
Which is cheaper: Command A Plus or Kimi K3?+
Command A Plus costs $3/1M input and $12/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Command A Plus or Kimi K3?+
Command A Plus supports 128K tokens whereas Kimi K3 supports 1.0M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Resmi kaynaklar: Cohere & Moonshot AI Official Documentation