Moonshot AI

Kimi K3.1 HighspeedTerbuka

Latency-optimized serving tier of Kimi K3.1 — 1M context at flash-class throughput.

#13dirilis 20 Okt 2026·Open Weights·2.8T MoE
textimage1.0M ctxpenalaran
Inteligensi
91.5 #13
AAQI 60.0
Kecepatan output
130 t/s
ITL 7.7 ms/tok
Harga gabungan (3:1)
$3.13 /1M
Input $1.5/M · Output $8/M
Konteks
1.0M
≈ 2,500 halaman A4 · output maks 65,536

Quality Domain Radar

6-Axis Frontier Evaluation vs Category Average & Flagship

Kimi K3.1 Highspeed
Claude Mythos Preview (#1)
Average (68)
Coding (60)Mathematics (88)Hard Science (GPQA) (92)Agentic & Tool Use (74)Knowledge (MMLU) (38)Vision / Multimodal (90)

Sumbu radar diaproksimasi dari skor kategori jika pengukuran benchmark langsung tidak tersedia — lihat Metodologi.

Kelebihan & Trade-off

Di mana ia menang

  • 130 tok/s — fastest 1M-context open model
  • Roughly half the Pro tier’s blended price
  • Same agentic tool-use lineage as K3.1 Pro

Hal yang perlu diperhatikan

  • Caps reasoning effort vs. Pro — drops on the hardest tasks
  • 65K output window vs. 131K on Pro
  • Still needs multi-node serving to self-host

Kasus Penggunaan Killer

Real-time agent loops
High-throughput tool calling for interactive copilots.
High-volume RAG
Cheap 1M-context reads over large corpora.
Voice-adjacent LLM backends
Sub-second first token keeps spoken agents snappy.

Quickstart Developer & API

Atur MOONSHOT_API_KEY dulu — dapatkan kunci API
curl https://api.moonshot.ai/v1/chat/completions \
  -H "Authorization: Bearer $MOONSHOT_API_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "kimi-k3-1-highspeed",
    "messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
  }'

Sumber Daya & Tautan Resmi

Deployment API

Bandingkan host & latensi

Throughput, TTFT, dan harga cache di Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock, dan Azure.

GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Lihat matriks penyedia →

Skor per sumber

Duel Head-to-head Live

Spesifikasi Live & Arsitektur

Di balik kap
Kimi K3.1 Highspeed — 2.8T MoE, speculative decode
  • Same K3.1 weights served with aggressive speculative decoding
  • Sub-600ms TTFT at 130 tok/s on dedicated capacity
  • 1M context retained; 65K max output
  • Lower reasoning-effort ceiling than the Pro tier
Tanggal rilis
20 Okt 2026
Arsitektur
Kimi K3.1 Highspeed — 2.8T MoE, speculative decode
Parameter
2.8T MoE
Lisensi
Open Weights
Jendela konteks
1,000,000 tokens
Output maks
65,536 tokens
Modalitas
text + image
Harga cache
baca $0.15 · tulis $1.88 /1M
Penalaran
Level effort
Harga input
$1.5/M
Harga output
$8/M
Kecepatan output
130 t/s
Waktu ke token pertama
—
Data diperbarui
22 Okt 2026
Perangkat Keras & Inferensi Lokal
Data: Convly AI (CC BY 4.0) ↗
GPU Min.
Multi-node cluster (e.g. 2× 8-GPU H200 141GB nodes)

Estimasi tagihan bulanan

Estimasi pengeluaran bulanan
$0.00
Menghitung penghematan vs GPT-4o…

Kutip & sematkan

Badge Markdown GitHub
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BibTeX
@misc{llmpodium2026kimik31highspeed,
  title = {Benchmark Analysis and Intelligence Evaluation of Kimi K3.1 Highspeed},
  author = {LLMPodium Research},
  year = {2026},
  url = {https://llmpodium.com/models/kimi-k3-1-highspeed}
}

Sematkan Lencana Skor

Tambahkan lencana Podium Score langsung ke README GitHub atau Hugging Face Anda — menautkan ke peringkat langsung.

Pratinjau
Kimi K3.1 Highspeed LLMPodium Score
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