Moonshot AI's flagship 3T-class reasoning model — 1M-token context, vision, tool use, and agentic coding that rivals the strongest proprietary models.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kimi-k3-1-pro",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Overview
Kimi K3.1 Pro is Moonshot AI's flagship 3T-class reasoning model, released on October 23, 2026 as a post-trained upgrade to Kimi K3. It combines a 1M-token context window with vision input, tool use, web search, and code-optimized performance built for long-horizon coding, 3D creation, and financial research.
On Venice, Kimi K3.1 Pro runs under the anonymized privacy tier: your prompts are not stored, profiled, or used for training, and nothing ties a conversation to a personal history. You get the same frontier reasoning model Moonshot ships, billed per token instead of through a subscription. Because Venice specializes in private access, you can use its vision, tool-calling, and web-search capabilities without feeding a surveillance ad stack.
Agent quickstart
The API is OpenAI-compatible: change the base URL and the model id and existing client code works unchanged.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kimi-k3-1-pro",
"stream": true,
"messages": [{ "role": "user", "content": "Draft the release note." }]
}'curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kimi-k3-1-pro",
"messages": [{ "role": "user", "content": "Find the rate limits." }],
"tools": [{
"type": "function",
"function": {
"name": "search_docs",
"description": "Search the API documentation.",
"parameters": {
"type": "object",
"properties": { "query": { "type": "string" } },
"required": ["query"]
}
}
}],
"tool_choice": "auto"
}'import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
resp = client.chat.completions.create(
model="kimi-k3-1-pro",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Set the reasoning effort explicitly — lower it for quick Q&A to cut verbosity and cost, raise it for multi-step coding or research tasks.
Paste the whole file tree or full documents at once; the 1M-token context is there to be used, and retrieval across long context is one of its strengths.
Attach multiple screenshots in one request when reviewing UI or comparing designs — multi-image input is supported natively.
Define your tools with a JSON schema and let the model call them; it is trained for function calling and end-to-end task completion.
Enable web search for financial or factual research prompts so answers are grounded in current sources instead of training data.
Reuse a cached system prompt for repeated agent runs — cached input on Venice is billed at $0.30 per 1M tokens instead of $3.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| Kimi K3.1 Pro | Agentic coding & long-horizon tasks | 1M tokens | No | $3 in · $15 out / 1M |
| Kimi K3 | Open-weight general reasoning | 1M tokens | Yes | $3.75 in · $18.75 out / 1M |
| Qwen 3.8 Max | Frontier reasoning at lower cost | 1M tokens | No | $2.50 in · $7.50 out / 1M |
| Gemini 3.8 Flash | Fast, cheap everyday tasks | 1M tokens | No | $0.94 in · $4.69 out / 1M |
Kimi K3.1 Pro is the right pick when a task has to be finished — multi-step coding, tool-driven research, or analysis across a full codebase — not just answered. For high-volume routine generation, Gemini 3.8 Flash or Qwen 3.8 Max do the same job for far less.
Pricing
Billed per token on Venice: $3 per 1M input tokens and $15 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Getting a key
Create a key in API settings. Nothing else is required to start.
Export it as VENICE_API_KEY so the snippets above run unedited.
Point an existing OpenAI client at https://api.venice.ai/api/v1. The scheme is part of the value: an OpenAI client given a bare host does not resolve it.
Pass kimi-k3-1-pro as the model and send the request.
FAQ
Kimi K3.1 Pro is Moonshot AI's flagship 3T-class reasoning model, released October 23, 2026. It is a post-trained upgrade to Kimi K3 with a 1M-token context window, vision input, tool use, web search, and code-optimized performance aimed at long-horizon coding, 3D creation, and financial research.
Venice bills Kimi K3.1 Pro per token: $3 per 1M input tokens and $15 per 1M output tokens, with cached input at $0.30 per 1M. There is no subscription required — you pay for what you use.
It is neither free-software nor open-source today. The model is proprietary at launch, though Moonshot has announced that full weights will be published by November 4, 2026. On Venice, usage is pay-per-token rather than free.
Moonshot AI released Kimi K3.1 Pro on October 23, 2026, alongside a faster K3.1 Pro Highspeed variant. It has been available on Venice since October 2026.
Yes. Kimi K3.1 Pro supports function calling, structured JSON-schema output, and web search, and it is explicitly optimized for agentic workflows that chain many tool calls across long tasks. All of these work through the Venice API.
Yes. It accepts text and image input, including multiple images in a single request, which makes it useful for UI review, diagram analysis, and document understanding. It outputs text only — it does not generate images.
K3.1 Pro is a post-trained version of Kimi K3 with substantially more post-training compute and reinforcement learning across hundreds of domains. Moonshot reports it beats K3 on agentic coding benchmarks, and independent analysis measures large gains in agentic performance. Kimi K3's weights are already open; K3.1 Pro's are pledged for release by November 4, 2026.
Venice runs it under the anonymized privacy tier: prompts are not stored, profiled, or used for training, and conversations are not tied to a personal history. Note that this model does not run in a TEE or with end-to-end encryption — the anonymization policy is the privacy guarantee.
1,000K tokens (1 million), with up to 128K tokens of output per response. That is roughly 1,500 A4 pages of text in a single context, enough for whole codebases or large document sets.
One key, free to start, no credit card.