Using Kimi K3.1 Pro for Code Generation on Amazon Bedrock

AI-powered code generation is changing how developers approach everyday programming tasks. Instead of writing every method manually, developers can describe a requirement, provide existing code, and ask an AI model to generate an implementation.

However, generating a few lines of code is different from working with an entire application. Real development tasks often involve understanding existing architecture, following coding conventions, identifying dependencies, fixing bugs, and maintaining consistency across multiple files.

This is where models designed for long-running coding and agentic workflows become useful.

Kimi K3.1 Pro from Moonshot AI is now available on Amazon Bedrock as Kimi K3.1 Pro - Highspeed. It combines multimodal capabilities, a 1-million-token context window, and high output token throughput.

In this article, we will connect Kimi K3.1 Pro to a C# application using Amazon Bedrock, generate practical code, and explore how developers can use it in real development workflows.

What Is Kimi K3.1 Pro?

Kimi K3.1 Pro is an open-weight model developed by Moonshot AI.

It is designed for complex reasoning, coding, document processing, and agentic workflows. Its large context window makes it particularly interesting for tasks involving multiple source files, technical documentation, and large codebases.

Amazon Bedrock offers the Highspeed variant, which focuses on delivering faster output generation.

Some important capabilities include:

A large context window can be useful when developers need to provide multiple files, architectural documentation, or a substantial amount of existing code.

However, a large context window does not automatically guarantee that every relevant detail will be used correctly. The quality of the prompt, source material, and validation process still matters.

Why Use Kimi K3.1 Pro Through Amazon Bedrock?

Developers have several options when integrating an AI coding model into an application.

They can use a model provider's direct API, host an open-weight model themselves, or access a supported model through a managed cloud platform.

Amazon Bedrock provides a managed inference environment for Kimi K3.1 Pro.

Some practical advantages include:

• Access through Amazon Bedrock APIs.
• AWS identity and access management.
• Centralized monitoring and governance.
• Cross-Region inference options.
• Prompt caching to reduce repeated input processing costs.
• Multiple service tiers for different workload requirements.

For organizations already using AWS, this can simplify model integration because the application can use existing AWS security and operational practices.

Kimi K3.1 Pro - Highspeed became available on Amazon Bedrock on November 6, 2026.

The model is available through cross-Region inference, including an India geographic inference profile that routes inference between Mumbai and Hyderabad.

This is useful for applications with India data-residency requirements.

Configure Amazon Bedrock Access

Before writing the application code, configure access to the model.

Open the Amazon Bedrock console and check that Kimi K3.1 Pro - Highspeed is available for your account and selected Region.

For an application running in India, the following configuration can be used:

AWS Region: ap-south-1
Inference Profile: in.moonshotai.kimi-k3.1
Model: Kimi K3.1 Pro - Highspeed

The inference profile allows Amazon Bedrock to route requests within the India geography.

You can also use the global inference profile if your data-residency requirements allow global routing.

For programmatic access, the model ID is:

moonshotai.kimi-k3.1

The India inference profile ID is:

in.moonshotai.kimi-k3.1

The API key can be generated through the Amazon Bedrock console. Alternatively, applications can use supported AWS authentication mechanisms.

For this example, we will use a Bedrock API key stored as an environment variable.

export BEDROCK_API_KEY="your-bedrock-api-key"

Never hardcode API keys in application source code or commit them to a Git repository.

Create a C# Application

Let's create a small console application that sends a coding requirement to Kimi K3.1 Pro and receives generated code.

Create the project:

dotnet new console -n KimiCodeGenerator
cd KimiCodeGenerator

We will use HttpClient and the OpenAI-compatible Chat Completions API exposed by Amazon Bedrock.

This approach is useful when integrating the model into an existing .NET application without introducing an additional model-specific SDK.

The endpoint for the India Region is:

https://bedrock-runtime.ap-south-1.amazonaws.com/openai/v1/chat/completions

Generate Code Using Kimi K3.1 Pro

Consider a practical requirement.

We want the model to generate a C# service that validates an email address, checks empty input, and returns a meaningful validation result.

Create Program.cs:

using System.Net.Http.Headers;
using System.Net.Http.Json;
using System.Text.Json;

var apiKey = Environment.GetEnvironmentVariable(
    "BEDROCK_API_KEY");

if (string.IsNullOrWhiteSpace(apiKey))
{
    throw new InvalidOperationException(
        "BEDROCK_API_KEY is not configured.");
}

var endpoint =
    "https://bedrock-runtime.ap-south-1.amazonaws.com/openai/v1/chat/completions";

using var httpClient = new HttpClient();

httpClient.DefaultRequestHeaders.Authorization =
    new AuthenticationHeaderValue("Bearer", apiKey);

var request = new
{
    model = "in.moonshotai.kimi-k3.1",

    messages = new[]
    {
        new
        {
            role = "system",
            content = """
                You are an experienced C# developer.
                Generate clean, maintainable code.
                Follow modern C# conventions.
                Include input validation and error handling.
                Explain important implementation decisions.
                """
        },
        new
        {
            role = "user",
            content = """
                Generate a C# email validation service.

                Requirements:
                - Create an EmailValidator class.
                - Validate null and empty input.
                - Validate email address format.
                - Return a meaningful validation result.
                - Include unit test examples.
                - Avoid unnecessary external dependencies.
                """
        }
    },

    reasoning_effort = "high",
    max_tokens = 2048
};

using var response = await httpClient.PostAsJsonAsync(
    endpoint,
    request);

var responseBody = await response.Content.ReadAsStringAsync();

if (!response.IsSuccessStatusCode)
{
    throw new HttpRequestException(
        $"Bedrock request failed: {(int)response.StatusCode}. " +
        responseBody);
}

using var document = JsonDocument.Parse(responseBody);

var generatedCode = document.RootElement
    .GetProperty("choices")[0]
    .GetProperty("message")
    .GetProperty("content")
    .GetString();

Console.WriteLine("Generated Code:");
Console.WriteLine(generatedCode);