Moonshot AI

Kimi K3.1 HighspeedAperto

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

#13rilasciato il 20 ott 2026·Open Weights·2.8T MoE
textimage1.0M ctxreasoning
Provider APIOttieni chiave API →vs Kimi K3.1 ProClassifica
Intelligenza
91.5 #13
AAQI 60.0
Velocità Uscita
130 t/s
ITL 7.7 ms/tok
Prezzo combinato (3:1)
$3.13 /1M
Input $1.5/M · Output $8/M
Contesto
1.0M
≈ 2,500 pagine A4 · output max 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)

Gli assi radar sono approssimati dai punteggi di categoria dove mancano misurazioni dirette — vedi Metodologia.

Punti di forza e compromessi

Dove vince

  • 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

A cosa fare attenzione

  • 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

Casi d'uso 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 per sviluppatori e API

Imposta prima MOONSHOT_API_KEY — ottieni una chiave 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."}]
  }'

Risorse e link ufficiali

Deployment API

Confronta host e latenza

Throughput, TTFT e prezzi cache su Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.

GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Vedi matrice provider →

Punteggi per Fonte

Scontri diretti live

Specifiche live e architettura

Sotto il cofano
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
Data di Rilascio
20 ott 2026
Architettura
Kimi K3.1 Highspeed — 2.8T MoE, speculative decode
Parametri
2.8T MoE
Licenza
Open Weights
Finestra di Contesto
1,000,000 tokens
Output max
65,536 tokens
Modalità
text + image
Prezzi cache
lettura $0.15 · scrittura $1.88 /1M
Ragionamento
Livelli di sforzo
Prezzo Ingresso
$1.5/M
Prezzo Uscita
$8/M
Velocità Uscita
130 t/s
Tempo al Primo Token
—
Dati aggiornati
22 ott 2026
Hardware e inferenza locale
Dati: Convly AI (CC BY 4.0) ↗
GPU min.
Multi-node cluster (e.g. 2× 8-GPU H200 141GB nodes)

Stima bolletta mensile

Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…

Cita e incorpora

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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}
}

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Aggiungi un badge Podium Score live al tuo README su GitHub o Hugging Face — rimanda alla classifica live.

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Kimi K3.1 Highspeed LLMPodium Score
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