The Tool Desk
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Use Moonshot AI’s Kimi API Platform for the authoritative route to Kimi K3.1 Pro. Sign in, create an API key, add at least $1 to your account to unlock the flagship model, and test prompts in the Playground. For code, send requests to https://api.moonshot.ai/v1 with the model ID kimi-k3.1-pro. The endpoint works with the OpenAI Python SDK, direct HTTP, Moonshot’s Responses and Chat Completions interfaces, and an Anthropic-compatible Messages API.
K3.1 Pro has a 1-million-token context window, always-on thinking, visual understanding, text/image/video input, streaming, tool calls, multi-turn history, and JSON mode. Its documented list price is $2 per million input tokens and $10 per million output tokens from Moonshot AI in 2026; cache pricing and account limits can change, so verify them before deploying.
What is the official way to access Kimi K3.1 Pro?
The direct route is Moonshot AI’s Kimi API Platform. It gives you an account, API keys, a browser-based Playground, model access, usage controls, and production API endpoints under one Moonshot account. The Playground is useful when you want to evaluate prompts without writing code; the API is the better fit for applications, automation, and repeatable tests.
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- Chat Completions: the familiar messages-based interface used by the Python example below.
- Responses: a newer request style for applications that need a unified response object.
- Anthropic-compatible Messages: useful when an existing client or agent is built around Anthropic’s message format.
All three are access methods to the same Moonshot service. Choose one interface per integration and follow its request schema rather than mixing fields between formats.
How do I create an account, unlock the model, and make my first request?
- Sign in to the Kimi API Platform. Use the account area associated with your Moonshot billing and model access.
- Open API Keys. Create a key and copy it when it is shown. Treat the value as a secret; it may not be displayed again.
- Add at least $1. Moonshot states that a successful top-up of at least $1 unlocks Kimi K3.1 Pro. Your cumulative top-up amount also determines account tier, concurrency, requests per minute (RPM), tokens per minute (TPM), and tokens per day (TPD) limits.
- Open the Playground. Select K3.1 Pro, enter a prompt, and inspect the response, latency, token usage, and any structured output options exposed by the interface.
- Move the same prompt into code. Use the exact model ID
kimi-k3.1-proand the API base URLhttps://api.moonshot.ai/v1.
The $1 requirement is an access threshold, not a statement that every account has the same limits or that prices are fixed. Check your account’s current tier and billing page after topping up.
How should I store the API key?
Keep the key on a server or in a local environment variable, never in browser JavaScript, a mobile app bundle, a public repository, or a screenshot. Moonshot specifically advises against sharing keys or hard-coding them directly in an application.
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What are the endpoint and model names?
Use the following values for a direct integration:
| Setting | Value | Why it matters |
|---|---|---|
| Base URL | https://api.moonshot.ai/v1 |
OpenAI-compatible root for direct Moonshot requests |
| Chat route | POST https://api.moonshot.ai/v1/chat/completions |
HTTP route for messages-based Chat Completions calls |
| Standard model | kimi-k3.1-pro |
Flagship K3.1 Pro model identifier |
| Faster model | kimi-k3.1-pro-highspeed |
Separate, faster variant with the same documented capabilities |
| Authentication | Authorization: Bearer YOUR_KEY |
Send the API key in the HTTP authorization header |
Model IDs are case-sensitive strings in the request body. A model name intended for a different provider, a missing top-up, or an account tier without access can produce an authorization or model-availability error even when the URL is correct.
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How do I use Kimi K3.1 Pro with the OpenAI Python SDK?
Moonshot’s documented pattern works with Python 3.9 or newer and openai>=1.0. Install the SDK, set the environment variable, and point the client’s base_url at Moonshot instead of the default service.
python -m pip install "openai>=1.0"import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["MOONSHOT_API_KEY"],
base_url="https://api.moonshot.ai/v1",
)
completion = client.chat.completions.create(
model="kimi-k3.1-pro",
messages=[
{"role": "user", "content": "Introduce Kimi K3.1 Pro in one sentence."}
],
)
print(completion.choices[0].message.content)Run the script in the same shell where MOONSHOT_API_KEY is set. The returned text is in completion.choices[0].message.content. For a web service, create the client once during application startup and keep the key in the server environment.
How do I make the equivalent HTTP request?
Send JSON to the Chat Completions route with a Bearer token, a content-type header, the model ID, and a messages array.
curl https://api.moonshot.ai/v1/chat/completions
-H "Authorization: Bearer $MOONSHOT_API_KEY"
-H "Content-Type: application/json"
-d '{
"model": "kimi-k3.1-pro",
"messages": [
{"role": "user", "content": "Introduce Kimi K3.1 Pro in one sentence."}
]
}'The response follows the Chat Completions shape, including a choices collection. Check the HTTP status and response body before trying to parse content; authentication, billing, quota, and malformed JSON errors are returned as API errors rather than successful completions.
How do I test prompts in the Playground?
Use the Playground when you need a quick, visible experiment rather than a coded integration.
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- Sign in to the Kimi API Platform and open Playground.
- Choose Kimi K3.1 Pro, not the Highspeed model, when you are evaluating the standard flagship behavior.
- Enter a system instruction and user prompt if the interface exposes separate fields; otherwise include the needed context in the conversation.
- Try a short factual prompt first, then a representative long-context, coding, visual, or tool-use prompt.
- Record the prompt, model ID, reasoning setting, output format, and observed token usage before comparing runs.
Playground results are useful for prompt design, but they do not establish an independent benchmark or guarantee the same latency under production concurrency.
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What reasoning controls does K3.1 Pro provide?
Thinking is always enabled. Moonshot documents a top-level reasoning_effort request field with three accepted values: low, high, and max. max is the default.
| Value | When to use it | Trade-off |
|---|---|---|
low |
Short, routine tasks where response speed matters | Less deliberate reasoning than the higher settings |
high |
Complex analysis, coding, or multi-step instructions | More reasoning work and potentially more latency or output tokens |
max |
Most demanding tasks; this is the default | Highest reasoning setting, with usage and latency implications |
Set the field at the top level of a request when the selected interface supports it. Keep the value explicit in evaluation scripts so a later default change does not invalidate comparisons.
What can Kimi K3.1 Pro handle?
The model has a 1-million-token context window and native visual understanding. Moonshot documents strengths in coding, 3D creation, financial research, and agentic workflows. It supports text, image, and video input, streaming output, multi-turn message history, function or tool calls, and JSON mode.
Long-context conversations
Include earlier turns in the message history when the answer depends on them. A large context window does not remove the need to manage cost, retrieval quality, and irrelevant material: send the documents and turns that the current task actually needs.
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Images and video
Vision requests use structured content arrays containing separate text and image parts. Do not serialize the array as one quoted string; the service needs to receive each content part with its type and associated media data. Validate media size, format, and access permissions before sending a production request.
Streaming output
Streaming lets a client render partial output as it arrives instead of waiting for the complete response. It is useful for interactive interfaces, but your code must handle an interrupted stream, assemble chunks in order, and record a final usage event when the interface provides one.
Tools and JSON mode
Function or tool calls allow the model to request an application-defined operation, while your code remains responsible for validating arguments, authorizing the operation, executing it, and returning the result. JSON mode can constrain the response format for downstream parsing; still validate the returned JSON and handle refusals or incomplete output.
How much does Kimi K3.1 Pro cost?
Moonshot AI’s 2026 model catalog lists K3.1 Pro at $2 per million input tokens and $10 per million output tokens. These are token prices, not a flat monthly subscription, and they do not include any separate cache-write or cache-hit rates listed by the provider.
| Charge type | Published figure | Qualification |
|---|---|---|
| Input tokens | $2 per million | Moonshot AI catalog figure for 2026; verify current pricing before publication or deployment |
| Output tokens | $10 per million | Moonshot AI catalog figure for 2026; generated text and reasoning usage may affect totals according to the provider’s accounting |
| Cache write | Not stated here | Moonshot lists cache pricing separately; consult the current model catalog |
| Cache hit | Not stated here | Moonshot lists cache pricing separately; consult the current model catalog |
Actual spend depends on prompt length, retained history, generated output, reasoning effort, caching, and retries. Set a budget, monitor token usage, and avoid automatic retries for non-retryable authentication or validation errors.
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What is the difference between K3.1 Pro and K3.1 Pro Highspeed?
They are separate model IDs. Moonshot documents kimi-k3.1-pro-highspeed as roughly two to three times faster while retaining the same capabilities; the standard flagship identifier is kimi-k3.1-pro.
| Characteristic | K3.1 Pro | K3.1 Pro Highspeed |
|---|---|---|
| Model ID | kimi-k3.1-pro |
kimi-k3.1-pro-highspeed |
| Reasoning | Thinking always enabled; low, high, or max |
Documented as retaining the same capabilities; verify current option support in the catalog |
| Speed | Standard flagship behavior | Provider-described as approximately 2–3× faster |
| Best fit | Default evaluation and quality-sensitive workloads | Interactive or latency-sensitive workloads where the faster variant meets your quality needs |
The two-to-three-times figure is a provider description, not an independent test result. Measure your own end-to-end latency, output length, and error rate with representative prompts before switching a production workload.
Should I use direct Moonshot access or Vercel AI Gateway?
Direct Moonshot access keeps the account, billing, and model relationship with Moonshot. Vercel announced Kimi K3.1 Pro and Highspeed in AI Gateway on 23 October 2026; that route adds gateway-level controls but introduces a separate service relationship.
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| Decision factor | Direct Moonshot API | Vercel AI Gateway |
|---|---|---|
| Account and billing | Moonshot account and top-up; at least $1 unlocks K3.1 Pro | Gateway account and its billing or BYOK arrangement; confirm how Moonshot charges are passed through |
| API compatibility | Chat Completions, Responses, and Anthropic-compatible Messages | AI SDK, Chat Completions, Responses, Anthropic Messages, and coding-agent integrations |
| Operations | Configure your own retries, routing, and monitoring | Vercel says it provides usage tracking, budgets, routing rules, retries, and failover |
| Inference platform fee | Not a gateway comparison point | Vercel says there is no platform fee on inference |
| Privacy and retention | Review Moonshot’s current terms | Review both gateway and underlying provider terms; they may not be identical |
| Limits and availability | Controlled by Moonshot tier, RPM, TPM, TPD, and regional availability | May have gateway-specific limits, regions, and model availability |
| Price | Moonshot catalog pricing listed above | Compare the current gateway and provider rates before choosing a route |
Use the gateway when its observability, budgets, routing, or failover solve a concrete operational need. Use direct access when minimizing intermediaries and keeping billing and data-flow decisions within Moonshot is more important.
What should I check when a request fails?
401 or authentication errors
- Confirm
MOONSHOT_API_KEYis present in the process that is making the request. - Ensure the header is exactly
Authorization: Bearer ...and that no surrounding quotes or line breaks were copied into the key. - Revoke and replace a key that may have been exposed.
Model unavailable or access denied
- Verify the exact model string:
kimi-k3.1-proorkimi-k3.1-pro-highspeed. - Check that the account has completed the required minimum $1 top-up.
- Review cumulative top-up tier and current concurrency, RPM, TPM, and TPD limits.
400 validation errors
- Use the request schema belonging to the selected interface.
- Send
messagesas an array for Chat Completions. - For vision, send structured content parts rather than a serialized JSON string.
- Validate tool definitions and JSON-mode requirements before retrying.
429, timeout, or quota errors
- Reduce concurrency or token volume and inspect the account’s current limits.
- Use bounded exponential backoff only for transient failures; do not retry invalid keys or malformed requests.
- Consider the Highspeed model for latency-sensitive traffic, then verify quality with your own prompts.
What is a safe production checklist?
- Store the API key in a server-side secret manager or environment variable.
- Pin the model ID and reasoning setting in configuration.
- Log request IDs, status codes, latency, token usage, and model name without logging secrets or sensitive prompt content.
- Set application-level timeouts, retry limits, and spend alerts.
- Validate tool-call arguments and JSON responses before executing or storing them.
- Test text, multimodal, long-context, streaming, and failure paths separately.
- Recheck pricing, model availability, top-up rules, limits, and gateway terms immediately before launch because these operational details can change.
Frequently Asked Questions
Is the $1 top-up a recurring subscription?
Moonshot documents the $1 amount as the minimum successful top-up needed to unlock K3.1 Pro, but the cited access information does not establish that it is a subscription or describe refund rules. Check the billing terms shown in your account for recurrence, expiration, and refunds.
Can an existing Anthropic Messages client call Kimi K3.1 Pro?
Yes, Moonshot documents an Anthropic-compatible Messages API. Point the client at Moonshot’s service, use a Moonshot API key, and verify any provider-specific fields or multimodal content rules before moving traffic.
Do gateway requests use the same rate limits as direct Moonshot requests?
Not necessarily. Direct requests are governed by Moonshot account tier and RPM, TPM, TPD, and concurrency limits, while a managed gateway can add its own quotas or routing behavior; confirm both sets of limits with the services you choose.
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Use Moonshot’s live model catalog and billing view immediately before estimating costs. The $2-per-million input and $10-per-million output figures are Moonshot’s 2026 listing and may change, as can cache rates and account allowances.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
