Kimi K3 release date: API launch vs open weights drop
16 июля 2026 Moonshot задеployил Kimi K3 через Kimi App, Kimi Work, Kimi Code и API. Artificial Analysis опубликовал independent review в тот же день. 27 июля — полные веса (Modified MIT) плюс technical report (architecture, training, eval) на Hugging Face.
Bottom line: K3 — не Fable 5 killer. AA Intelligence Index 57,1, rank #3 из 189, позади Claude Fable 5 (59,9) и GPT-5.6 Sol (58,9). Зато frontier-adjacent capability примерно за треть цены плюс два артефакта, которых closed models не дают: downloadable open weights и 1M-token context.
| Дата | Событие |
|---|---|
| 2026-07-16 | K3 API и Kimi product suite; Artificial Analysis benchmark release |
| 2026-07-17 | WAIC в Shanghai; state media — national AI milestone |
| 2026-07-27 | Full weights на Hugging Face (Modified MIT) + technical report |
API уже live: model ID kimi-k3 на platform.kimi.ai — weights ждать не обязательно.
Weights ≠ instant local run: vLLM support для KDA и prefix caching прилетает с weight release; Ollama/GGUF ports — по паттерну K2.
Vendor unusually honest: Moonshot публично признаёт отставание от Fable 5 и Sol — harness sensitivity и over-eager behavior на ambiguous prompts.
Geopolitical angle: timing совпадает с WAIC. US regulators кратко сняли Anthropic Fable/Mythos в июне (restore 1 июля) — narrative: API ban бьёт closed, не published weights.
Moonshot comeback: после DeepSeek R1 (domestic rank упал до #7 в начале 2025) roadmap K2 → K2.5 → K3 восстановил credibility.
Что такое Kimi K3? Key specs
Архитектура: Stable LatentMoE — 896 experts, 16 activated per token (~1,8% sparsity). Attention stack: Kimi Delta Attention (KDA, hybrid linear attention) + Attention Residuals (AttnRes) + Gated MLA. Weights — MXFP4, activations MXFP8 (native low-precision training). Scaling efficiency ~2,5× vs K2; KDA даёт до 6,3× decode speedup на 1M context.
| Spec | Value |
|---|---|
| Total parameters | 2,8 trillion — largest open-weight model to date |
| Architecture | Sparse MoE: Stable LatentMoE, 896 experts / 16 active per token |
| Attention | KDA + AttnRes + Gated MLA |
| Context window | 1 048 576 tokens (1M) |
| Modalities | Native vision (text + image; video in product), text output |
| Weight format | MXFP4 weights + MXFP8 activations |
| Training stability | Quantile Balancing, Per-Head Muon optimizer, SiTU activation |
| License (27.07) | Modified MIT — verify LICENSE on release day |
В заголовках пишите open weights, не open source. На r/LocalLLaMA и Hacker News это не pedantry — это high-intent query «kimi k3 open weights». Community различает термины жёстко.
Kimi K3 benchmarks: vs Claude Fable 5 и GPT-5.6 Sol
Data cross-ref: Artificial Analysis (16.07) + Moonshot release materials. Harnesses разные — всегда указывайте source и date.
| Model | AA Intelligence Index | Rank |
|---|---|---|
| Claude Fable 5 | 59,9 | #1 |
| GPT-5.6 Sol | 58,9 | #2 |
| Kimi K3 | 57,1 | #3 / 189 |
Где K3 wins или ties:
Frontend Code Arena: #1 — blind dev preference для UI code над Fable и Sol
Automation Bench, SpreadsheetBench 2: #1
BrowseComp: 91,2 (#1) — 90,4+ с 1M context no-compression strategy
SWE Marathon: 42,0 — far ahead (GPT-5.5 / GLM-5.2 collapse to teens)
Terminal Bench 2.1: 88,3 — near parity с Sol (88,8)
Program Bench: 77,8 — edges Sol (77,6)
FrontierSWE: 81,2 — beats Sol (71,3), trails Fable 5 (86,6)
Где K3 still lags:
GDPval v2 Elo: 1668–1687 vs Fable 5 (1760), Sol (1748) — long-horizon judgment остаётся Fable territory
DeepSWE: 67,5 vs Sol 73,0
Hallucination rate up vs K2.6 (vendor acknowledged); Reddit — больше hallucinations чем у top closed models в self-hosted apps
Conversation polish и session variance ниже Fable 5 / Sol
| Dimension | Kimi K3 | Claude Fable 5 | GPT-5.6 Sol |
|---|---|---|---|
| Intelligence Index | 57,1 (#3) | 59,9 (#1) | 58,9 (#2) |
| Cost per task (AA) | $0,94 | ~$2,75 | ~$1,04 |
| vs Fable 5 | 65,8% cheaper; 9,4% below Sol | ||
| Open weights | 27.07 | Closed | Closed |
| Context | 1M tokens | ~200K class | 128K–1M by tier |
Kimi K3 API pricing и release checklist 27.07
Pricing на уровне Western quality-tier API — highest among Chinese vendors, но всё ещё well below Claude Opus 4.8 per-task. Coding workloads report 90%+ cache hit — effective spend падает резко.
| Item | Price (per 1M tokens) |
|---|---|
| Input (cache miss) | $3,00 |
| Input (cache hit, automatic) | $0,30 |
| Output | $15,00 |
Что происходит 27 июля — six-step checklist:
Full 2,8T weights на Moonshot Hugging Face org под Modified MIT (terms TBC)
Technical report — architecture, training, evaluation details
vLLM ecosystem — KDA + prefix caching support ships with weights
Community follow-ons — Ollama, GGUF quant builds expected like K2 series
Verify LICENSE text — commercial redistribution scope on release day
Independent long-context retest — cross-check community benchmarks vs official numbers
Sources: kimi.com/en/blog/kimi-k3, platform.kimi.ai, Artificial Analysis (16.07), VentureBeat, Northflank self-hosting analysis, r/LocalLLaMA, Hacker News.
Local run Kimi K3? Hardware requirements & industry impact
Not on consumer hardware. 4-bit weights ~1,4 TB. Moonshot recommends 64+ accelerator super-node с expert parallelism + tensor parallelism. Reference: K2.7 Code (1T params) needed ~577 GB VRAM at INT4; K3 — 2,8× that scale.
Для большинства команд answer — API ($3/$15). Self-hosting имеет смысл при strict data residency, fine-tuning на proprietary data или volume где owned hardware beats cloud inference bills.
2,8T params, 896 experts / 16 active, 1M context — quick reference line one
~1,4 TB at 4-bit; 64+ accelerators recommended — любой «run on your laptop» headline = clickbait
Frontend Code Arena #1; SWE Marathon 42,0 — long-session coding lead over closed rivals
Industry impact — four angles:
Open vs closed gap nearly closed at frontier — от percentage-point chasms к 2–3 index points; closed-model premium на capability alone труднее оправдать
Regulatory narrative — published weights outlive API bans
Chinese vendor wave — GLM-5.2, DeepSeek V4 Pro, MiniMax closing in; K3 — parameter peak
Moonshot strategic reset — open-weight cadence после DeepSeek R1 disruption
64-GPU inference cluster — hidden ops cost: power, networking, version pinning, on-call. Если нужны stable Mac hosts для iOS CI/CD и AI Agent orchestration, а K3 вызываете через API — MESHLAUNCH cloud Mac Mini rental обычно лучший production path: dedicated Apple Silicon, 24/7 uptime, flexible daily/weekly/monthly terms. Цены: тарифы аренды; help: центр помощи.
27 июля 2026 на Moonshot Hugging Face. API live с 16 июля — два разных milestone (download vs call).
27 июля — open weights, не fully open source (нет training code/data). Modified MIT terms читать в release day. Architecture depth: обзор Kimi K3.
$3/M input, $0,30/M cached input, $15/M output. ~$0,94 per task в AA testing. Agent dev environments: тарифы аренды.
Нет. ~1,4 TB at 4-bit требует datacenter-scale GPU cluster. Ждите community distilled GGUF builds — с major capability tradeoffs.
Overall index: no (#3). Coding/automation benches: often yes. Long-horizon reasoning/stability: Fable/Sol territory. Moonshot говорит это публично. Hybrid setup: центр помощи.