AIREITER
MoonshotText Chat

Kimi K3.1 Pro

在線上試用 Kimi K3.1 Pro,或透過相容 OpenAI 或 Anthropic 的 API 進行調用。具備 1M-token 上下文、全時開啟推理、Tool Calling 與圖片輸入功能。

输入官方 $2.00 每 100 萬 TokensAIReiter $0.60 每 100 萬 Tokens输出官方 $10.00 每 100 萬 TokensAIReiter $3.00 每 100 萬 Tokens缓存读取官方 $0.20 每 100 萬 TokensAIReiter $0.06 每 100 萬 Tokens缓存创建官方 $2.00 每 100 萬 TokensAIReiter $0.60 每 100 萬 Tokens
使用 API 執行

輸入

輸出

Example
Generated in
42.7 seconds
輸入 Token
134
輸出 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

模型詳情

在 Playground、API 請求與內部工作流程中使用相同的模型鍵。

模型 ID
kimi-k3.1-pro
供應商
Moonshot
協定
OpenAI Chat Completions · Anthropic Messages
上下文視窗
1,048,576 Token
最大輸出
131,072 Token
輸入 Token
60 credits / 1M Token
輸出 Token
300 credits / 1M Token
快取讀取
6 credits / 1M Token
快取寫入
60 credits / 1M Token

Last updated: 5 November 2026. Specs and benchmark scores are from Moonshot's Kimi K3.1 Pro guide and the Kimi K3.1 Pro tech blog.

What Kimi K3.1 Pro is

Kimi K3.1 Pro is the first model in Moonshot AI's K3.1 series. It is a post-trained version of Kimi K3, trained with much more reinforcement learning across coding, 3D creation, financial research, and other agent tasks. The architecture and the context window stay the same as K3; the gains come from post-training.

Key specs

  • Context window: 1,048,576 tokens, shared by input and output.
  • Reasoning: thinking is always on. Streaming responses carry the reasoning in reasoning_content, separate from the final answer in content.
  • Input: text and images. Images must be sent as base64 data URLs; public image URLs are not accepted by the model.
  • Output length: up to 131,072 completion tokens per request in the playground.
  • Tools: function calling with tool_choice, and JSON Schema structured output with strict: true.
  • Fixed sampling: temperature 1.0 and top_p 0.95 are fixed by Moonshot, so the playground does not show those sliders.

Where Kimi K3.1 Pro is strongest

Moonshot reports the scores below. They are the vendor's own numbers, not AIReiter measurements, so run your own prompts in the playground above before switching production traffic.

BenchmarkWhat it testsKimi K3.1 ProReference
Terminal-Bench 4.0Coding agents on real terminal tasks65.7%Claude Opus 5.5 66.4%, GPT-6 Astra 58.2%
Kimi Code Bench 3.0 Long-horizonMulti-step coding with tools (Moonshot internal)61.2%Kimi K3 55.95%
BenchCAD, with tools3D reconstruction through CAD code92.7%Claude Opus 5.5 96.2%, GPT-6 Astra 95.9%
OfficeQA Pro v2Fiscal data analysis across real financial documents64.4Reported as state of the art
Kimi Hallu Bench Hard, with toolsClaim-level hallucination rate, lower is better8.3%Kimi K3 23.4%

Good fits

  • Long-horizon coding agents: repository-wide fixes and full project builds, including game work in Unity, Unreal Engine, and Godot.
  • 3D and CAD scripting: writing and running Blender or CAD scripts to build parametric parts, furniture, and architectural models from a text brief or a reference image.
  • Financial research and modeling: retrieval and multi-step numerical reasoning across filings, where the lower hallucination rate matters most.

Kimi K3.1 Pro vs Kimi K3

Kimi K3.1 ProKimi K3
BaseK3 with much larger post-trainingK3
Context window1,048,576 tokens1,048,576 tokens
ReasoningAlways onAlways on
Kimi Code Bench 3.0 Long-horizon61.2%55.95%
Kimi Hallu Bench Hard (with tools)8.3%23.4%
API model IDkimi-k3.1-prokimi-k3

Both models take the same request shape, so moving from K3 to K3.1 Pro is a one-line change of the model ID. Keep K3 for workloads you have already tuned and evaluated; try K3.1 Pro first for new coding agents, 3D scripting, and finance tasks.

Calling Kimi K3.1 Pro through AIReiter

  • Model ID: kimi-k3.1-pro.
  • Endpoints: OpenAI-compatible https://aireiter.com/api/v1/chat/completions and Anthropic-compatible https://aireiter.com/api/v1/messages. Existing OpenAI or Anthropic clients work after changing the base URL and the key.
  • Leave sampling at default: do not send temperature, top_p, n, presence_penalty, or frequency_penalty. Moonshot fixes them for this model.
  • Multi-turn and tool loops: send back the complete assistant message from the previous turn, including its reasoning and tool calls, not only content.
  • Images: put them in a content array as image_url with a base64 data URL.
  • Output tokens: reasoning is billed as output, so set max_tokens high enough for the reasoning plus the answer.

Create a key on the API keys page. The request sample above this section already uses the right model ID.

三步驟試用 Kimi K3.1 Pro

無需安裝,也無需設定。上方的 Playground 調用的是與您程式碼相同的模型。

01

貼上真實任務

使用您實際需要完成的工作:未通過的測試及其相關程式碼、想在 Blender 中建模的零件,或是需要核對的一組數據。簡單的示範提示詞無法展現推理模型的真正價值。

02

給它足夠的思考空間

推理功能為全時開啟,並計入 max_tokens。對於多步驟任務,請將 max_tokens 設為 8,192 或更高,以避免回答在推理完成後被截斷。

03

複製 API 調用代碼

在相容 OpenAI 或 Anthropic 的端點上,使用模型 ID kimi-k3.1-pro 將相同的請求移植到您的程式碼中。每次回覆的 Token 使用量皆顯示於回應下方。

Kimi K3.1 Pro 常見問題

能力、推理與整合相關問題。

/ 01

什麼是 Kimi K3.1 Pro?

Kimi K3.1 Pro 是 Moonshot AI 首款 K3.1 系列模型,為 Kimi K3 的後訓練版本,在程式編寫、3D 創作和金融研究方面進行了大量強化學習。它保留了 K3 的 1,048,576-token 上下文視窗,全時開啟思考功能,並支援文字與圖片輸入。

/ 02

Kimi K3.1 Pro 與 Kimi K3 有何不同?

K3.1 Pro 與 K3 採用相同的基底和上下文視窗,但增加了更大規模的後訓練。Moonshot 報告指出,Kimi Code Bench 3.0 Long-horizon 的表現從 55.95% 提升至 61.2%,而在 Kimi Hallu Bench Hard 上的宣稱層級(claim-level)幻覺率則從 23.4% 降至 8.3%。除了模型 ID 之外,請求方式完全相同。

/ 03

我可以關閉推理功能嗎?

不行。Kimi K3.1 Pro 始終在啟用思考的狀態下運行。推理過程會包含在 reasoning_content 中,與 content 中的回答分開,並計入輸出 Token 計費。對於簡短問題,推理通常很簡短,但對於多步驟任務,請預留足夠的 max_tokens,以確保最終回答不會被截斷。

/ 04

為什麼 temperature 和 top_p 無法調整?

Moonshot 將 Kimi K3.1 Pro 的 temperature 固定為 1.0,top_p 固定為 0.95,同時固定的還有 n、presence_penalty 與 frequency_penalty。Playground 隱藏了這些控制項,API 請求應省略這些參數,而非設定自訂值。

/ 05

Kimi K3.1 Pro 支援 Tool Calling 嗎?

支援。它支援帶有 tool_choice 的 function calling(包括在第一輪對話中使用 required),以及具有 strict 模式的 JSON Schema 結構化輸出。在工具循環中,請回傳包含 tool_calls 的完整 assistant 訊息,然後針對每次調用回傳一則帶有相對應 tool_call_id 的 tool 訊息。

/ 06

Kimi K3.1 Pro 可以讀取圖片嗎?

可以,透過 API 即可達成。在 content 陣列中將圖片作為 image_url 項目發送,並使用 base64 data URL。該模型不會主動擷取公開圖片 URL,因此請先下載並編碼圖片。目前此 Playground 已關閉圖片上傳功能,因此請透過 API 測試視覺功能。

/ 07

上下文視窗有多大?

上下文視窗為 1,048,576 tokens,並同時涵蓋輸入與輸出。若輸入佔用 900,000 tokens,則推理與回答僅剩不到 150,000 tokens。重複的前綴(例如系統提示詞和程式庫)會自動進行快取,進而降低長篇多輪對話的成本。