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مقارنة Kimi K3 مع Claude Opus 4.8 (2026): المواصفات، والأسعار، والنتيجة النهائية

مُحدَّث · Originally published July 17, 2026

Moonshot’s Kimi K3 arrived in July 2026 as the most serious open-weight challenger yet to Anthropic’s flagship Claude Opus 4.8 — and the two models represent opposite philosophies of frontier AI. One is a 2.8-trillion-parameter open mixture-of-experts you will (from July 27) be able to download; the other is a closed, polished agentic workhorse you rent through an API. Here is how they actually compare on specs, pricing, and practical deployment, using data from our live AI models database.

حقائق رئيسية

  • Kimi K3: 2.8T total parameters (MoE, 16 of 896 experts active), 1M context, open weights due July 27, 2026
  • Claude Opus 4.8: Anthropic’s proprietary flagship, 1M context, up to 128K output tokens
  • API pricing: K3 at $3/$15 per million tokens vs Opus 4.8 at $5/$25 — ~40% cheaper blended ($6.00 vs $10.00 on our 3:1 index)
  • Self-hosting K3 is theoretical for most: ~1.4 TB of VRAM at 4-bit — a multi-node H200 cluster, not a workstation
  • Moonshot’s published benchmarks show K3 edging past Opus 4.8 on several agentic and coding suites — treat vendor numbers as claims until independent benchmarks land

Specs, Side by Side

Head to Head

Kimi K3 Claude Opus 4.8
المطوّر مون شوت آي أنثروبيك
عدد المعايير (البارامترات) 2.8T total, MoE (16/896 experts active) غير مُعلَّن
نافذة السياق مليون رمز مليون رمز
الحد الأقصى للإخراج 128K tokens
الترخيص Open weights (from Jul 27, 2026) Proprietary API
API price (in/out per 1M) $3 / $15 $5 / $25
Run it locally? ~1.4 TB VRAM (4-bit) — multi-node cluster Not available

The Pricing Math

On our blended index (3:1 input-to-output, the ratio of typical production traffic), Kimi K3 costs $6.00 per million tokens against $10.00 for Opus 4.8 — a 40% saving at near-frontier capability. Run a real workload through it: at 100M input + 20M output tokens a month, K3 bills $600 while Opus bills $1,000. Over a year that difference funds an entire additional AI project. You can model your own volumes in our حاسبة تكلفة واجهات برمجة تطبيقات الذكاء الاصطناعي (API).

The context that matters: both models sit at the premium end of our مؤشر الأداء بالنسبة للسعر, where efficiency-tier models deliver far more capability per dollar for routine work. The K3-vs-Opus question is really about the top 10% of tasks — long-horizon agents, complex refactors, high-stakes reasoning — where flagship quality pays for itself.

Open Weights Change the Question

From July 27, K3’s weights are downloadable — but “open” does not mean “runnable at home.” At roughly 1.4 TB of VRAM in 4-bit quantization, you need a multi-node cluster (think two 8-GPU H200 nodes) before the first token flows. For almost everyone, K3 will be consumed through APIs just like Opus. The strategic difference is real, though: open weights mean multiple competing providers, price pressure over time, fine-tuning rights, and no single-vendor kill switch — the same dynamics that made DeepSeek the value king of our index.

رأي Convly

Choose Opus 4.8 when reliability under agentic load is the product — its 128K output ceiling and mature tooling remain the safest bet for production agents. Choose K3 when cost at frontier scale is the constraint, when you need fine-tuning rights, or when vendor independence matters strategically. And if your workload is routine, choose neither: the efficiency tier does 90% of tasks at a tenth of the price. The most expensive mistake in 2026 isn’t picking the wrong flagship — it’s using any flagship for work a $0.20 model handles.

الأسئلة الشائعة

هل نموذج كيمي K3 أفضل من كلاود أوبس 4.8؟

Moonshot’s published results show K3 ahead on several agentic and coding benchmarks, but independent verification is still landing. What is certain: K3 costs ~40% less blended ($6 vs $10 per 1M tokens) and its weights go open on July 27, 2026.

هل يمكنني تشغيل نموذج كيمي K3 محليًّا؟

Only with data-center hardware: ~1.4 TB of VRAM at 4-bit quantization, i.e. a multi-node GPU cluster such as two 8×H200 nodes. Check any open model’s requirements in our حاسبة ذاكرة VRAM المطلوبة لنماذج اللغة الكبيرة.

Which is cheaper for production use?

Kimi K3: $3/$15 per million tokens vs $5/$25 for Opus 4.8. At a typical 3:1 input:output mix, that is $6.00 vs $10.00 blended — 40% cheaper for the same volume.

Go Deeper

بقلم مصطفى إحسان

مصطفى إحسان هو مؤسس ومُحرِّر موقع Convly.ai. وقد أنشأ قاعدة بيانات النماذج الحية للذكاء الاصطناعي الخاصة بالموقع، ومؤشر الأداء السعري الخاص به، بالإضافة إلى الحاسبات المجانية لحساب متطلبات الذاكرة VRAM، وتكاليف واجهة برمجة التطبيقات (API)، والاقتصاديات المرتبطة بالاستضافة الذاتية. ويكتب مصطفى عن أسعار النماذج، ونتائج الاختبارات المعيارية، والأجهزة اللازمة لتشغيل نماذج الذكاء الاصطناعي محليًّا، ويعطي دائمًا الأولوية للأرقام المُقاسة بدقة على الادعاءات التي تطلقها الشركات المصنِّعة.

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