Kimi K3.1 ProOuvert
Moonshot’s 2.8T-parameter MoE flagship with 1M context, native multimodality and frontier reasoning.
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Les axes du radar sont approximés depuis les scores par catégorie lorsque les mesures directes manquent — voir Méthodologie.
Points forts & compromis
Là où il gagne
- Top-3 podium score — frontier reasoning and agentic tool use
- 1M context with open weights
- Strong SWE-bench / Terminal-Bench results for long-horizon coding
- Multimodal document and chart understanding at 96+
Points de vigilance
- Multi-node cluster required for self-hosting (2.8T MoE)
- 45 tok/s output — slower than flash-tier peers
- Premium pricing vs. smaller open models
Cas d'usage clés
Démarrage rapide développeur & API
MOONSHOT_API_KEY — obtenir une clé APIcurl https://api.moonshot.ai/v1/chat/completions \
-H "Authorization: Bearer $MOONSHOT_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "kimi-k3-1-pro",
"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-pro",
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-pro",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);Ressources & liens officiels
Comparer hôtes et latence
Débit, TTFT et tarifs de cache chez Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock et Azure.
Scores par Source
Scores par Catégorie
Résultats de Benchmarks
Duels en direct
Specs en direct & architecture
- Sparse MoE routing — ~40B active per token out of 2.8T total
- Native multimodality: text + vision in a single weight set
- Reasoning effort levels with a 131K max output window
- INT4 checkpoints for cheaper multi-node serving
Facture mensuelle estimée
Citer et intégrer
Loading badge markdown...@misc{llmpodium2026kimik31pro,
title = {Benchmark Analysis and Intelligence Evaluation of Kimi K3.1 Pro},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/kimi-k3-1-pro}
}Intégrer le badge de score
Ajoutez un badge Podium Score en direct à votre README GitHub ou Hugging Face — il renvoie vers le classement en direct.
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