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Kimi K3.1 Pro

在线体验 Kimi K3.1 Pro,或通过兼容 OpenAI 与 Anthropic 的 API 进行调用。具备 1M-token 上下文、常驻推理、工具调用以及图像输入功能。

输入官方 $2.00 每 100 万 TokensAIReiter $0.60 每 100 万 Tokens输出官方 $10.00 每 100 万 TokensAIReiter $3.00 每 100 万 Tokens缓存读取官方 $0.20 每 100 万 TokensAIReiter $0.06 每 100 万 Tokens缓存创建官方 $2.00 每 100 万 TokensAIReiter $0.60 每 100 万 Tokens
使用 API 运行

输入

输出

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

模型详情

在 Playground、API 请求和内部工作流中使用相同的模型 key。

模型 ID
kimi-k3.1-pro
供应商
Moonshot
协议
OpenAI Chat Completions · Anthropic Messages
上下文窗口
1,048,576 Token
最大输出
131,072 Token
输入 Token
60 credits / 1M Token
输出 Token
300 credits / 1M Token
缓存读取
6 credits / 1M Token
缓存写入
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.

三步体验 Kimi K3.1 Pro

无需安装,无需配置。上方的 Playground 调用的正是您代码中将要使用的相同模型。

01

粘贴真实任务

使用您实际需要完成的工作:失败的测试及其周边代码、希望在 Blender 中建模的零件,或一组需要核对的数据。玩具式提示词无法体现推理模型的真正价值。

02

留出足够的思考空间

推理过程始终处于开启状态,并计入 max_tokens。对于多步骤任务,建议将 max_tokens 设置为 8,192 或更高,以避免回答在推理结束后被截断。

03

复制 API 调用代码

使用模型 ID kimi-k3.1-pro 将相同请求迁移至您的代码中,支持兼容 OpenAI 或 Anthropic 的端点。每次回复的 Token 使用量都会显示在响应下方。

Kimi K3.1 Pro 常见问题解答

关于模型能力、推理机制和集成的常见问题。

/ 01

什么是 Kimi K3.1 Pro?

Kimi K3.1 Pro 是 Moonshot AI 推出的首款 K3.1 系列模型,它是 Kimi K3 的后训练版本,在编程、3D 创作和金融研究方面进行了大幅强化的强化学习训练。它保留了 K3 的 1,048,576-token 上下文窗口,常驻思考模式,并支持文本和图像输入。

/ 02

Kimi K3.1 Pro 与 Kimi K3 有何不同?

K3.1 Pro 与 K3 采用相同基座和上下文窗口,但增加了更大量的后训练。Moonshot 数据显示,Kimi Code Bench 3.0 Long-horizon 得分从 55.95% 提升至 61.2%,而在 Kimi Hallu Bench Hard 上的断言级幻觉率从 23.4% 降至 8.3%。除模型 ID 外,请求方式完全一致。

/ 03

我可以关闭推理功能吗?

不能。Kimi K3.1 Pro 始终在启用思考的状态下运行。推理过程通过 reasoning_content 返回,与 content 中的回答内容分离,且作为输出 Token 计费。对于简短问题,推理通常较简短,但对于多步骤任务,请预留足够的 max_tokens 以确保最终回答不会被截断。

/ 04

为什么 temperature 和 top_p 不可调节?

Moonshot 将 Kimi K3.1 Pro 的 temperature 固定为 1.0,top_p 固定为 0.95,同时锁定的还有 n、presence_penalty 和 frequency_penalty。Playground 隐藏了这些控件,API 请求也应直接省略这些参数,而非设置自定义值。

/ 05

Kimi K3.1 Pro 支持工具调用吗?

支持。它支持带有 tool_choice 的函数调用,包括第一轮中的 required,以及带有 strict 模式的 JSON Schema 结构化输出。在工具循环中,请传回包含 tool_calls 的完整 assistant 消息,然后为每次调用传回包含匹配 tool_call_id 的对应 tool 消息。

/ 06

Kimi K3.1 Pro 能读取图像吗?

可以,通过 API 支持。在 content 数组中以带有 base64 data URL 的 image_url 项发送图像。该模型不会主动拉取公开的图像 URL,因此请先下载并编码图像。目前本 Playground 中暂未开启图像上传功能,请通过 API 测试视觉能力。

/ 07

上下文窗口有多大?

上下文窗口为 1,048,576 个 Token,同时涵盖输入与输出。如果有 900,000 个 Token 的输入,留给推理和回答的 Token 将不足 150,000 个。重复的前缀(如系统提示词和代码库)会自动缓存,从而降低长多轮会话的成本。