Moonshot AI
Kimi K3.1 HighspeedOffen
Latency-optimized serving tier of Kimi K3.1 — 1M context at flash-class throughput.
Intelligenz
91.5 #13
AAQI 60.0
Ausgabegeschwindigkeit
130 t/s
ITL 7.7 ms/tok
Blended-Preis (3:1)
$3.13 /1M
Input $1.5/M · Output $8/M
Kontext
1.0M
≈ 2,500 A4-Seiten · max. Output 65,536
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Kimi K3.1 Highspeed
Claude Mythos Preview (#1)
Average (68)
Radar-Achsen sind aus Kategorie-Scores approximiert, wo direkte Benchmark-Messungen fehlen — siehe Methodik.
Stärken & Trade-offs
Wo das Modell gewinnt
- 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
Worauf achten
- 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
Killer-Anwendungsfälle
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.
Developer-Quickstart & API
Setzen Sie zuerst
MOONSHOT_API_KEY — API-Schlüssel holencurl 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."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.moonshot.ai/v1",
api_key=os.environ["MOONSHOT_API_KEY"],
)
resp = client.chat.completions.create(
model="kimi-k3-1-highspeed",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://api.moonshot.ai/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.MOONSHOT_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "kimi-k3-1-highspeed",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);Offizielle Ressourcen & Links
API-Bereitstellung
Hosts & Latenz vergleichen
Durchsatz, TTFT und Cache-Preise bei Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock und Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Ergebnisse nach Quelle
Kategorie-Punkte
Benchmark-Ergebnisse
Direkte Live-Duelle
Live-Spezifikationen & Architektur
Unter der Haube
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
Veröffentlichungsdatum
20. Okt. 2026
Architektur
Kimi K3.1 Highspeed — 2.8T MoE, speculative decode
Parameter
2.8T MoE
Lizenz
Open Weights
Kontextfenster
1,000,000 tokens
Max. Ausgabe
65,536 tokens
Modalitäten
text + image
Cache-Preise
Lesen $0.15 · Schreiben $1.88 /1M
Reasoning
Effort-Stufen
Eingabepreis
$1.5/M
Ausgabepreis
$8/M
Ausgabegeschwindigkeit
130 t/s
Zeit bis zum ersten Token
—
Daten aktualisiert
22. Okt. 2026
Hardware & lokale Inferenz
Daten: Convly AI (CC BY 4.0) ↗Min. GPU
Multi-node cluster (e.g. 2× 8-GPU H200 141GB nodes)
Geschätzte Monatsrechnung
Geschätzte Monatsausgaben
$0.00
Ersparnis ggü. GPT-4o wird berechnet…
Zitieren & Einbetten
GitHub-Markdown-Badge
Loading badge markdown...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}
}Score-Badge einbetten
Fügen Sie Ihrem GitHub- oder Hugging-Face-README ein Live-Podium-Score-Badge hinzu — es verlinkt auf das Live-Ranking.
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