2.8兆パラメータ · オープンウェイト · MoonEP/FlashKDA/AgentEnv · ライセンス · 米中AI争い
結論から言うと厳密な定義ではオープンソースではない——Moonshot AI自身もそう主張していません。2026年7月27日夜、同社はKimi K3の完全ウェイトと技術レポートを公開し、MoonEP・FlashKDA・AgentEnvも同時にオープンソース化しました。オープンウェイトとオープンソースの違い、ライセンスの商用ゲート、GPT-5.6 Sol・Claude Fable 5との比較に悩む方へ、タイムライン、アーキテクチャ、ベンチマーク表、6ステップRunbook、ライセンス分析で実行可能な結論を示します。データ基準日：2026-07-28
By the Open Source Initiative definition, a genuinely open-source model requires public training data, public training code, and a reproducible pipeline — not just trained weights. Kimi K3 ships weights, a technical report, and training-adjacent infrastructure tools, but not training data or full training code. Moonshot consistently calls this an open-weight release, never open source.
The license has also tightened versus the K2 family. It is no longer branded Modified MIT — it is a fully custom document with two commercial thresholds:
Model-as-a-Service revenue gate: If you or affiliates run a MaaS business on K3 generating more than $20 million in aggregate trailing-12-month revenue, you must negotiate a separate commercial agreement with Moonshot AI.
Attribution gate: If your product exceeds 100 million MAU or $20 million monthly revenue, you must prominently display Kimi K3 in the UI.
July 16 API launch: K3 went live on kimi.com and the API; weights were not yet public.
July 22–23 distillation dispute: White House adviser Michael Kratsios accused Moonshot of industrial distillation against Anthropic Fable; Treasury Secretary Scott Bessent floated sanctions.
July 27 full release: ~1.56TB weights hit Hugging Face trending #1 within 30 minutes; MoonEP and AgentEnv open-sourced (FlashKDA was already out).
Teams evaluating K3 often hit five blind spots before shipping:
Semantic confusion: Media says open source; Moonshot says open weight — training data and code are not public.
License traps: New MaaS revenue gate did not exist in the K2 Modified MIT era.
Self-hosting fantasy: 2.8T parameters need 64+ accelerator supernodes — not a prosumer GPU rig.
Benchmark trust: Vendor numbers diverge; cross-check Vals AI and Artificial Analysis independently.
Cost vs capability: K3 is the open-weight capability ceiling at ~$0.95/task, but GLM-5.2 runs ~$0.47/task.
| Spec | Value |
|---|---|
| Total parameters | 2.8 trillion |
| Active parameters | ~104 billion |
| Architecture | Mixture-of-Experts (MoE) |
| Experts | 896 routed, 16 activated per token, plus shared experts |
| Attention | Kimi Delta Attention (KDA) + Gated MLA |
| Context window | 1,000,000 tokens |
| Multimodality | Native vision (ViT-V2, 27 layers) |
| Weight format | MXFP4 weights, MXFP8 activations (QAT from SFT) |
| Download size | ~1.56TB (Hugging Face) |
| License | Custom — open weight, not open source |
Standard Gated DeltaNet applies one scalar forgetting gate to an entire memory state. KDA uses channel-wise gating: every feature dimension gets its own decay rate. Implemented as chunkwise Diagonal-Plus-Low-Rank (DPLR), it keeps recurrence linear in time while staying hardware-efficient. K3 interleaves KDA with Gated MLA global-attention layers — the hybrid that supports 1M context while keeping KV cache small.
Uniform residual accumulation dilutes early-layer signal at depth. AttnRes replaces it with selective, input-dependent aggregation across preceding layers — one RMSNorm and one pseudo-query vector per layer, roughly 25% training efficiency gain for under 2% added cost.
Per-Head Muon optimizes attention heads independently. The MoE layer routes 896 experts, activates 16 per token (~1.8% sparsity), and uses Quantile Balancing plus MoonEP to prove a mathematical upper bound on redundant experts per rank.
Kimi K3 rebuilt three long-standing defaults — attention, residual connections, and the optimizer — rather than simply scaling an existing recipe.
Confirm compliance boundaries: Read the custom license. MaaS revenue over $20M trailing 12 months requires a separate Moonshot agreement; 100M+ MAU or $20M+ monthly revenue requires prominent Kimi K3 attribution.
Get an API key: Create a project at platform.moonshot.ai and verify balance and rate limits.
Configure the OpenAI-compatible SDK: Set base_url=https://api.moonshot.ai/v1, model ID kimi-k3 (see code below).
Optional OpenRouter path: Model ID moonshotai/kimi-k3 — seven providers already host it, mostly at official pricing.
Optimize cache hit rate: Mooncake disaggregated serving reportedly exceeds 90% cache hits on coding workloads — real input cost lands near $0.30/M, not the $3 headline.
Self-hosting feasibility check: Moonshot recommends 64+ accelerator supernodes. Individual developers should use API or OpenRouter, not local weights.
from openai import OpenAI
client = OpenAI(
api_key="your_moonshot_api_key",
base_url="https://api.moonshot.ai/v1"
)
response = client.chat.completions.create(
model="kimi-k3",
messages=[{"role": "user", "content": "Analyze this code..."}]
)
| Technology | Role | Key numbers |
|---|---|---|
| MoonEP | MoE communication at extreme scale | Duplicates overloaded experts for equal token counts; proves redundant-expert upper bound per rank |
| FlashKDA | CUTLASS KDA kernels | 1.72–2.22× faster prefill on H20 vs flash-linear-attention; drop-in via chunk_kda |
| AgentEnv | Firecracker microVM agent sandbox | Checkpoint ~133ms, resume ~49ms, up to 6.5× memory overcommit (Moonshot-reported) |
| Model | Score | Release |
|---|---|---|
| Claude Opus 5 | 97% | 2026-07-24 |
| GPT-5.6 Sol | 96.2% | 2026-07-09 |
| Claude Fable 5 | 95% | 2026-06-09 |
| Kimi K3 | 93.4% | 2026-07-16 |
| Qwen3.7-Max | 79.4% | 2026-05-19 |
| DeepSeek-V4 | 76.2% | 2026-04-23 |
Artificial Analysis Intelligence Index (max reasoning): Kimi K3 scores ~57 — #3 overall, #1 among open-weight models, behind Claude Fable 5 (60) and GPT-5.6 Sol (59). ~$0.95/task vs Fable 5 ~$2.40 (~60% cheaper) but above GLM-5.2 ~$0.47. K3 ranks #1 on Arena.ai Frontend Code Arena.
| Token type | Price per million |
|---|---|
| Input (cache hit) | $0.30 |
| Input (cache miss) | $3.00 |
| Output (incl. reasoning) | $15.00 |
License gates matter if you resell inference at scale. For nearly every startup or mid-size company, neither threshold is practical — you can ship commercial products on K3 today. The clause that bites is the MaaS revenue gate, and only if you compete directly with Moonshot API.
The release landed during WAIC 2026 and days after the US-China distillation dispute escalated. China's Ministry of Commerce pushed back July 28, accusing Washington of AI hegemonism. Three days later Alibaba — a Moonshot investor — unveiled Qwen3.8-Max-Preview (2.4T parameters), pushing the race into what some call the 3T club.
In short: Kimi K3 is the capability ceiling of the open-weight tier, not the value pick.
APIアクセスはK3推論をカバーしますが、iOS署名チェーン、Xcodeビルド、Metalワークロード、24時間CIには実macOSノードが必要です。本番iOS CI/CDとAI Agent自動化には、VpsMeshのMac Miniクラウドレンタルが通常より適した選択です。詳細はMac Mini M4レンタル料金とヘルプセンターをご覧ください。
Sources: Moonshot AI official blog, Hugging Face, GitHub moonshotai/FlashKDA, Vals AI SWE-bench, Artificial Analysis, VentureBeat, Simon Willison. Verify against official data before production decisions.
OSIの厳密な定義ではいいえ。オープンウェイトです：学習済みウェイト、技術レポート、一部インフラは公開されていますが、学習データと完全な学習コードは公開されていません。
Yes, for the vast majority of use cases. Restrictions apply only if you run a Model-as-a-Service business on K3 generating over $20 million in trailing 12-month revenue (requires a separate Moonshot agreement), or if you exceed 100 million MAU or $20 million monthly revenue (requires displaying Kimi K3 in your UI).
Moonshotは64以上のアクセラレータを備えたスーパーノード構成を推奨しています。個人開発者は公式APIまたはOpenRouterをご利用ください。24時間ビルド環境はMac Mini M4レンタル料金をご確認ください。
It trails Claude Opus 5, GPT-5.6 Sol, and Claude Fable 5 on independent SWE-bench Verified (93.4% vs 95–97%) and ranks #3 on the Artificial Analysis Intelligence Index, but it is the strongest open-weight model available, ahead of GLM-5.2 and DeepSeek V4 Pro.
K3 has roughly 3× the parameters of K2.5, adds Attention Residuals and Per-Head Muon, and significantly expands context and multimodal capability. Its license adds a new Model-as-a-Service revenue threshold not present in K2. More at our help center.