How to Use Kimi K3.1 Pro API: Complete Guide 2026

TL;DR

Kimi K3.1 Pro is Moonshot AI’s flagship 3T-class model (2.8T total / 104B active parameters, 1M-token context) released on October 23, 2026. It is accessed via an OpenAI-compatible API using model IDs kimi-k3.1-pro (standard) or kimi-k3.1-pro-highspeed (2–3× faster).

For the simplest multi-model workflow and often lower pricing, use Kimi K3.1 Pro API in CometAPI with the same model IDs. The model always reasons (use reasoning_effort: low / high / max). Ideal for long-horizon coding, 3D/CAD, financial analysis, and agentic workflows. Full open weights expected by November 4, 2026.

This guide is for developers who can make a basic HTTP request and want to build with Kimi K3.1 Pro through CometAPI. You will need a CometAPI account with model access, an API key, and either cURL, Python 3.9+, or a supported Node.js environment. Start with a text request, then add the features your application needs.

Key Takeaways

• Get started in under 5 minutes with the OpenAI Python/Node SDK by changing only base_url and model.
• Two access paths: official Moonshot API or unified gateways like CometAPI (recommended for most teams).
• Always-on reasoning — control depth with reasoning_effort (low, high, max; default is max).
• Highspeed variant trades higher cost for 2–3× output speed; best for interactive coding.
• Supports vision (image + video), tool calling, structured JSON output, and 1M-token context.
• Start new sessions when switching models to preserve cache hits and output quality.
• CometAPI currently offers Kimi K3.1 Pro at competitive rates (~20% below official on standard tier) with one key for 500+ models.

Kimi K3.1 Pro API specifications

Start with model and messages. Add reasoning_effort or stream when needed; use tools and response_format for tool calling and structured output. Set an output budget supported by your route, and test optional features before deployment.

What Is the Kimi K3.1 Pro API?

Kimi K3.1 Pro is a frontier reasoning model developed by Moonshot AI and introduced on October 23, 2026. It builds on the Kimi K3 foundation with substantially increased post-training reinforcement learning, improving complex software engineering, 3D modeling, financial analysis, and multi-step agent workflows.

The API allows applications to use these capabilities programmatically without hosting the model. Developers can incorporate Kimi into coding assistants, document-analysis systems, research pipelines, and applications that require extended context and external tools.

Why Learn to Use Kimi K3.1 Pro Now?

Kimi K3.1 Pro sits within a few points of the strongest proprietary models on coding agents (Terminal-Bench 4.0: 65.7% vs Claude Opus 5.5 at 66.4%) while offering a full 1-million-token context window and open-weight plans. Its strengths in long-horizon software engineering, parametric 3D modeling, and reduced hallucination on financial tasks make it a high-value addition to any developer’s toolkit.

How does reasoning effort work?

• low: Start here for classification, extraction, and short code explanations when minimal reasoning overhead matters.
• high: Use it for debugging, code review, and document reasoning that benefits from a balance of depth and response time.
• max: Reserve the largest reasoning budget for difficult engineering decisions, planning, and extended agent tasks.

Thinking stays enabled at every effort level. If your existing client sends a setting such as none or disabled, remove it before making a K3.1 Pro request.

Leave sampling controls out of the initial request. Moonshot documents fixed values for temperature, top_p, n, presence_penalty, and frequency_penalty, so carrying settings over from another model can cause a validation error.

Access Options Compared

How to Use Kimi K3.1 Pro API ?

Follow these steps to get a working text response before introducing tools or multimodal content. The Python examples in later sections reuse the client initialized in the Python setup example.

Step 1: Get your API key

• Official path: Go to the Kimi API Platform → Console → API Keys → Create a new key. Store it as the environment variable MOONSHOT_API_KEY. Flagship models unlock after a minimum $1 top-up.
• CometAPI path (recommended for most users): Sign in to the CometAPI website, create an API key, and confirm your account can access the selected Kimi model. Keep the key on your server; a key issued by Moonshot is not a substitute for a CometAPI key.

Store the key in an environment variable rather than embedding it in source code.

Bash

export COMETAPI_API_KEY="your-cometapi-api-key"

PowerShell

$env:COMETAPI_API_KEY="your-cometapi-api-key"

Use a server-side environment variable in deployed applications. Do not expose API keys in browser JavaScript, public repositories, or client-side configuration files.

Step 2: Make your first request with cURL

This OpenAI-compatible design means existing LangChain, LlamaIndex, AutoGen, CrewAI, or Claude Code configurations work with a simple base_url and model-name swap.

Send a minimal request to the Chat Completions endpoint.

Bash

curl https://api.cometapi.com/v1/chat/completions \  -H "Authorization: Bearer $COMETAPI_API_KEY" \  -H "Content-Type: application/json" \  -d '{    "model": "kimi-k3.1-pro",    "messages": [      {        "role": "user",        "content": "Explain the purpose of API rate limiting in three sentences."      }    ]  }'

A successful request should return a completion containing the model response in choices[0].message.content.

Inspect the response status, finish_reason, and token usage before adding advanced options.

Step 3: Use Kimi K3.1 Pro API with Python

Install the OpenAI Python SDK.

Bash

python -m pip install --upgrade openai

Initialize the client with CometAPI’s base URL.

Python

import osfrom openai import OpenAI​client = OpenAI(    api_key=os.environ["COMETAPI_API_KEY"],    base_url="https://api.cometapi.com/v1",)​response = client.chat.completions.create(    model="kimi-k3.1-pro",    messages=[        {            "role": "system",            "content": "You are a software engineering assistant."        },        {            "role": "user",            "content": "Explain how to debug a Python memory leak."        }    ])​print(response.choices[0].message.content)print("Finish reason:", response.choices[0].finish_reason)print("Token usage:", response.usage)

This implementation is suitable for an existing OpenAI-compatible backend. Changing the model value lets you compare different models without restructuring the entire application.

Step 5: Switch to Kimi K3.1 Pro Highspeed

To compare response time without changing your client configuration, select the Kimi K3.1 Pro Highspeed API in CometAPI.

Simply change the model ID:

Python

response = client.chat.completions.create(    model="kimi-k3.1-pro-highspeed",    messages=[        {            "role": "user",            "content": "Find potential bugs in this API design."        }    ],)​print(response.choices[0].message.content)

Keep the prompt and output budget unchanged during the comparison. Record the observed latency and billed usage for both runs; the pricing section explains when the extra speed may justify the higher rate.

Build Kimi K3.1 Pro Agents with Streaming, Tools and Vision

Controlling Reasoning Effort

Kimi K3.1 Pro always reasons. The reasoning_effort parameter (top-level) controls depth:

• low — faster, cheaper, sufficient for simple coding or extraction
• high — balanced
• max — deepest reasoning (default), best for complex architecture, debugging, or multi-step planning

Lowering effort is often a better first optimization than switching to Highspeed.

Streaming for Better UX

Add stream=True (or stream: true in JSON) for progressive output. Essential for long coding sessions and agent loops. Long reasoning responses can take time. Streaming allows an application to process response increments without waiting for the entire completion.

Kimi separates reasoning increments from final-answer increments. Depending on the API route, reasoning can appear in reasoning_content, while user-facing output appears in content.

Multi-Turn Conversations & Tool Calling

Always return the full assistant message (including any reasoning_content) in subsequent turns to maintain cache hits and coherent reasoning. The model supports standard function calling and structured JSON output. Official tools (web search, code execution, etc.) are available via the Formula API channel.

messages.append(completion.choices[0].message)  # whole message, not .contentmessages.append({"role": "user", "content": "Now add tests."})

Vision and Multimodal Input

Pass images or video frames in the messages array using the standard OpenAI vision format. Excellent for 3D reconstruction, UI-to-code, diagram analysis, and document understanding.

Kimi K3.1 Pro supports visual understanding, making it useful for analyzing screenshots, diagrams, scanned documents, and design references.

For compatibility, local images can be Base64-encoded in a structured message.

import base64from pathlib import Path​image_data = base64.b64encode(    Path("dashboard.png").read_bytes()).decode("utf-8")​response = client.chat.completions.create(    model="kimi-k3.1-pro",    messages=[        {            "role": "user",            "content": [                {                    "type": "image_url",                    "image_url": {                        "url": f"data:image/png;base64,{image_data}"                    }                },                {                    "type": "text",                    "text": (                        "Review this dashboard screenshot. "                        "Identify three usability problems "                        "and suggest improvements."                    )                }            ]        }    ])​print(response.choices[0].message.content)

The code assumes dashboard.png exists in the working directory. Test the CometAPI route’s image-format support and upload-size limits before using it with larger visual inputs.

For video, Moonshot also documents file-based inputs. Video upload and file identifiers are more provider-specific, so avoid assuming that a Moonshot-native ms:// identifier will work unchanged through every gateway.

Using with Popular Coding Agents

• Kimi Code (official): Select K3.1 Pro or Highspeed via /model. Start a fresh session after switching.
• Claude Code / Codex / Cursor / Continue: Point the base URL to either Moonshot or CometAPI and set the model ID.
• Best practice: Keep the same model and reasoning effort for an entire session to maximize context caching (cache hits cost only $0.20 / M tokens).

How Much Does the Kimi K3.1 Pro API Cost?

Kimi K3.1 Pro uses token-based pricing. CometAPI offers both standard and Highspeed versions at rates below the published Moonshot API prices.

The following comparison uses USD per one million uncached input tokens or generated output tokens, based on CometAPI pricing checked on October 26, 2026.

The standard K3.1 Pro API is approximately 33.3% cheaper than Kimi K3 on uncached input and output token rates.

At these rates, Highspeed costs twice as much per token as standard. Compare the additional spend with the response-time improvement your users actually experience.

Kimi K3.1 Pro API cost example

Suppose an application processes 100,000 uncached input tokens and generates 10,000 output tokens.

The estimated cost is:

Cost = (Input tokens × Input rate + Output tokens × Output rate) / 1,000,000

For this example, moving from Kimi K3 to standard K3.1 Pro saves $0.12 per request before caching and additional charges.

Actual spending depends on generated reasoning tokens, context reuse, retries, tool operations, and the number of successful task completions.

Developers should check the latest Kimi K3.1 Pro API pricing in CometAPI before establishing production budgets.

How can caching reduce Kimi K3.1 Pro API costs?

Kimi supports automatic context caching for reusable input prefixes. Its official caching mechanism offers a default five-minute lifetime and an optional one-hour lifetime.

For applications that repeatedly submit the same instructions or reference materials, caching can reduce the cost of repeated context.

A practical request design places stable information first and frequently changing content last:

1. Stable system instructions.
2. Reusable tool definitions.
3. Reference documents or repository context.
4. Current user question.

For instance, a document-analysis assistant can reuse a fixed policy document while answering many different questions. A coding agent can retain stable repository instructions while changing the active issue or test result.

Do not assume every repeated request becomes a cache hit. Confirm cached-token usage and the applicable gateway billing rules. The provider’s cache prices and TTL options do not automatically establish identical CometAPI cache charges.

When to Choose Standard vs Highspeed

Best Practices for Cost and Quality

1. Start a new conversation when changing models or major reasoning effort.
2. Leverage prompt caching aggressively — keep system prompts and large codebases stable.
3. Use low reasoning effort first; escalate only when quality drops.
4. Monitor token usage (especially reasoning tokens) via the response metadata.
5. For production, implement fallback routing (CometAPI makes this trivial across providers).
6. Prefer native OpenAI or Anthropic protocol endpoints over heavy proxy translation for maximum stability.

Conclusion

Kimi K3.1 Pro is a strong option for developers building reasoning-intensive applications, coding agents, financial-document assistants, and long-context workflows. It combines improved model capability with familiar API integration and lower standard token prices than its predecessor.

The most effective adoption strategy is to start with a minimal CometAPI request, verify reasoning and streaming behavior, then add structured outputs, visual inputs, tools, and caching as the application requires.

Choose the standard K3.1 Pro model for cost-sensitive workflows and evaluate Highspeed when response latency matters. Before full deployment, compare both against real workloads and preserve a tested fallback.