Moonshot AI
Kimi K3.1 ProOpen
Moonshot’s 2.8T-parameter MoE flagship with 1M context, native multimodality and frontier reasoning.
Intelligence
94.2 #3
AAQI 68.0
Output Speed
45 t/s
ITL 22.2 ms/tok
Blended price (3:1)
$8 /1M
In $4/M · Out $20/M
Context
1.0M
≈ 2,500 A4 pages · max out 131,072
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Kimi K3.1 Pro
Claude Mythos Preview (#1)
Average (68)
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- 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+
Trade-offs to watch
- 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
Killer Use Cases
Long-horizon coding agents
131K output window rewrites whole files; leads SWE-bench-class leaderboards.
Agentic browsing & research
Tool-use loops over 1M-token contexts — the K-line’s signature workload.
Multimodal enterprise RAG
Charts, scans and PDFs ingested natively alongside text.
Developer Quickstart & API
Set
MOONSHOT_API_KEY first — get an API keycurl 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);Official Resources & Links
API deployment
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Scores by Source
Category Scores
Benchmark Results
Head-to-head Live Matchups
Live Specs & Architecture
Under the hood
Kimi K3.1 Pro — 2.8T MoE, ~40B active
- 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
Release date
Oct 20, 2026
Architecture
Kimi K3.1 Pro — 2.8T MoE, ~40B active
Parameters
2.8T MoE
License
Open Weights
Context Window
1,000,000 tokens
Max Output
131,072 tokens
Modalities
text + image
Cache Pricing
read $0.4 · write $5 /1M
Reasoning
Effort levels
Input Price
$4/M
Output Price
$20/M
Output Speed
45 t/s
Time to First Token
—
Data Updated
Oct 22, 2026
Hardware & Local Inference
Data: Convly AI (CC BY 4.0) ↗Min GPU
Multi-node cluster (e.g. 2× 8-GPU H200 141GB nodes)
Estimated monthly bill
Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...
Cite & embed
GitHub Markdown badge
Loading badge markdown...BibTeX
@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}
}Embed Score Badge
Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.
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