KIMI K3
OPEN_WEIGHTS_
27.07.2026.
Direct answer: Moonshot AI (月之暗面) выкладывает full checkpoint Kimi K3 — 2.8T sparse MoE, 1,048,576-token context window — на Hugging Face 27.07.2026 под Modified MIT. API live с 16.07 (kimi-k3). До drop'а осталось 5 дней. Этот hardcore-разбор: timeline (API vs weights), full spec table, benchmark matrix vs Fable 5/Sol, pricing с cache math, open weights vs open source distinction, 1.4 TB @4-bit hardware reality, Jul 27 checklist, industry signal, FAQ. Архитектура KDA/AttnRes — в обзоре от 17.07.
Spec sheet · 30 сек
| API live | 2026-07-16 · Kimi App/Work/Code + API · AA independent eval same day |
| Weights drop | 2026-07-27 · HF org moonshotai · Modified MIT · tech report |
| Scale | 2.8T total params · largest open-weight release ever |
| Intelligence | AA Index 57.1 (#3/189) · per-task cost $0.94 (−65.8% vs Fable 5) |
| Self-host | ~1.4 TB @4-bit · 64+ accelerators · expert + TP parallelism |
1. Timeline: API launch ≠ weights release
Главная путаница в поиске: kimi k3 release date возвращает два события. Разделяйте.
| Date | Event | Actionable |
|---|---|---|
| 2026-07-16 | K3 API + product stack live; Artificial Analysis publishes independent eval | Call kimi-k3 via platform.kimi.ai, kimi.com, OpenRouter — zero local weights |
| 2026-07-17 | WAIC Shanghai opens; Xinhua frames K3 as national milestone | Industry context; possible supplementary announcements |
| 2026-07-27 | Full weights + technical report + vLLM KDA/prefix caching support | HF download, fine-tune, on-prem inference — if you have the hardware |
2. Model spec — full table
| Field | Value |
|---|---|
| Total parameters | 2.8 trillion (2.8T) |
| Architecture | Sparse MoE Stable LatentMoE — 896 experts, 16 active/token (1.8% sparsity) |
| Attention stack | KDA (hybrid linear, 3:1 linear:full ratio) + AttnRes + Gated MLA |
| Context window | 1,048,576 tokens |
| Modalities | Native vision (text + image); video on product side; text output |
| Weight format | MXFP4 weights + MXFP8 activations (quantization-aware training) |
| Scaling efficiency | ~2.5× vs Kimi K2 at equal compute budget |
| Long-ctx decode | KDA: up to 6.3× faster @1M ctx; KV cache −75% |
| Training stability | Quantile Balancing, Per-Head Muon, SiTU activation |
| API model ID | kimi-k3 |
| License (weights) | Modified MIT — full text on release day |
3. Benchmarks: wins, losses, honest caveats
3.1 Artificial Analysis Intelligence Index (2026-07-16)
| Model | Score | Rank |
|---|---|---|
| Claude Fable 5 | 59.9 | #1 |
| GPT-5.6 Sol | 58.9 | #2 |
| Kimi K3 | 57.1 | #3 / 189 |
Gap to #1 = 2.8 points. Moonshot publicly admitted trailing Fable 5 / Sol on aggregate intelligence — unusual honesty. Known weaknesses: harness-sensitive reasoning content passthrough, over-proactive on ambiguous intent, hallucination rate up vs K2.6 (Reddit reports on self-hosted integrations).
3.2 Full benchmark matrix
| Benchmark | Kimi K3 | Claude Fable 5 | GPT-5.6 Sol |
|---|---|---|---|
| Frontend Code Arena | #1 (blind dev preference) | — | — |
| SWE Marathon | 42.0 | 35.0 | 39.0 |
| Program Bench | 77.8 | 76.8 | 77.6 |
| Terminal Bench 2.1 | 88.3 | 84.6 | 88.8 |
| FrontierSWE | 81.2 | 86.6 | 71.3 |
| BrowseComp | 91.2 | 88.0 | 90.4 |
| Automation Bench | 30.8 | 29.1 | 29.7 |
| SpreadsheetBench 2 | #1 | — | — |
| DeepSWE | 67.5 | 70.0 | 73.0 |
| GDPval v2 Elo | 1668–1687 | 1760 | 1748 |
| GPQA-Diamond | 93.5 | 92.6 | 94.1 |
| OmniDocBench | 91.1 | 89.8 | 85.8 |
Caveat: Moonshot self-reported + AA cross-check. Different harnesses (Kimi Code vs Claude Code vs Codex). Independent reproductions ongoing — treat as directional, not gospel.
4. API pricing + real cost with caching
| Line item | Price ($/M tokens) |
|---|---|
| Input (cache miss) | $3.00 |
| Input (cache hit, auto) | $0.30 |
| Output | $15.00 |
- AA measured per-task cost: $0.94 — −65.8% vs Fable 5, −9.4% vs Sol
- Coding workloads: 90%+ cache hit rate (Mooncake split-inference) → effective input ~$0.55/M
- Comparison baseline: DeepSeek V4 Pro output $3.48/M — 4.3× cheaper, but 128K ctx + 1.6T params
- vs Claude Opus 4.8: K3 beats several benchmarks at 60% input / 40% output cost
5. Open weights vs open source — don't conflate
r/LocalLLaMA will flag you if title says "open source" when Moonshot ships open weights only. This H2 exists because the distinction is SEO + credibility.
| Dimension | Open weights (K3) | Open source (strict) |
|---|---|---|
| Model checkpoint | Yes — 2.8T downloadable | Yes |
| Training code | No | Yes |
| Training data | No | Yes / documented |
| License | Modified MIT | MIT / Apache 2.0 typical |
| Valid use cases | Inference, fine-tune, on-prem deploy | Full reproducibility |
Modified MIT full text drops 27.07. Read commercial redistribution clauses before prod. Expect same pattern as K2 series weights — not K2 training pipeline.
6. Hardware requirements: 1.4 TB reality check
"Run Kimi K3 on your laptop" = clickbait. Numbers:
| Metric | Value |
|---|---|
| 4-bit quantized weights | ~1.4 TB |
| Official deployment spec | 64+ accelerators (H100-class supernode) — expert parallelism + tensor parallelism |
| Reference: K2.7 Code (1T, INT4) | ~577 GB VRAM — K3 = 2.8× scale |
| Native quant format | MXFP4 / NVFP4 — day-0 in transformers, vLLM, SGLang |
Self-host only makes sense when:
- Strict data residency — zero cloud API egress
- High-volume fine-tuning on proprietary corpus
- Inference volume amortizes hardware capex
Default answer for 99%: API at $3/$15. Full stop.
7. Jul 27 release checklist — copy to runbook
- HF repo moonshotai/Kimi-K3 — all weight shards present, no missing index
- LICENSE file — Modified MIT full text, legal review done
- MXFP4 / NVFP4 quant builds available
- Technical report published (architecture, training, eval methodology)
- vLLM / SGLang launch commands documented (KDA, prefix caching)
- Minimum viable hardware config confirmed by Moonshot or inference partners
- Independent 1M-context reproduction (not just short-context bench reruns)
- Track Ollama / GGUF community builds (K2 series precedent)
8. Industry significance — why Jul 27 matters
- Open/closed gap at frontier ≈ closed. From "hundreds of points" to "2–3 points" on AA Index — closed-source premium can't justify on capability alone.
- Regulatory resilience. Jun 2026: US briefly pulled Anthropic Fable/Mythos from foreign users (restored Jul 1). Published open weights can't be export-controlled off.
- China OSS wave peak. GLM-5.2, DeepSeek V4 Pro (1.6T), MiniMax M3 — K3 at 2.8T is the high-water mark.
- Moonshot comeback. Post DeepSeek R1 shock (domestic rank #7 early 2025) → K2 (Jul 2025) → K2.5 (Jan 2026) → K3 open-weight commitment rebuilds credibility.
Post Jul 27: first 2T+ downloadable foundation for enterprise on-prem, vertical fine-tuning, API-independent agent stacks. Most significant open release of 2026 for infra teams.
9. API access now — while waiting for weights
- kimi.com — free tier, max reasoning default
- platform.kimi.ai — API key, OpenAI-compatible
- OpenRouter —
moonshotai/kimi-k3, official pricing, full 1M ctx
10. FAQ — technical reference
When weights drop?
2026-07-27 on Hugging Face + tech report + vLLM support. API live since 2026-07-16.
Is K3 open source?
Open weights yes, open source strict no. Modified MIT; no training code/data.
API cost?
$3/$15 per 1M tokens; $0.30/M cache hit. Per-task ~$0.94 (AA).
Consumer GPU?
No. ~1.4 TB @4-bit. Watch MLX/GGUF community quants for distilled variants — not full 2.8T.
vs Fable 5 / Sol?
Aggregate: no (57.1 vs 59.9/58.9). SWE Marathon, Frontend Code Arena, BrowseComp: yes. ~1/3 Fable 5 cost per task.
License?
Modified MIT — full text Jul 27. Legal review before commercial deploy.
11. Verdict: API today, agent stress on remote Mac
kimi.com signup, API key, OpenRouter routing — any OS works. For Cursor + K3 1M ctx, SWE Marathon regression, MLX quant validation post-drop — Apple Silicon unified memory + Metal = lowest-friction dev path.
Before Jul 27 weights land: isolate heavy agent workloads on MACGPU remote Mac mini M4 — on-demand SSH, agent RAM/SSD load off primary machine — while infra teams prep 1.4 TB supernode deploy.