How to Use Kimi K3.1 Pro Highspeed API: Same as Pro, Just Change the Model ID

TL;DR

• Kimi K3.1 Pro Highspeed is Pro on a faster inference pipeline, available now on HiAPI. Open the model page to try it. New users get 200 free credits.
• Integration is identical to Pro: OpenAI Chat Completions protocol, all 9 parameters unchanged. Change model from kimi-k3.1-pro to kimi-k3.1-pro-highspeed and you are done. Nothing else.
• Platform pricing: input $4.3096 / output $22.5426 (per 1M tokens, cached read $0.2156), 2x Pro pricing. Response speed: 3x of Pro (official figure).
• Quickest call: POST https://api.hiapi.ai/v1/chat/completions with model: "kimi-k3.1-pro-highspeed". If you are already using Pro, changing this one value completes the switch.

What Kimi K3.1 Pro Highspeed Is

It is not a new model. It is Pro, running on a faster inference pipeline. Moonshot AI took Pro and added a speed-tier line that is 3x faster at 2x the price. Everything else is unchanged. Three things matter for callers:

• Identical capabilities: reasoning, streaming, structured output (json_schema), tool calling (with tool_choice). Everything Pro can do, Highspeed can do.
• Identical parameters: frequency_penalty, presence_penalty, reasoning_effort, response_format, temperature, thinking, tool_choice, tools, top_p. All 9, no additions or removals.
• Identical protocol: OpenAI Chat Completions. No base_url changes, no SDK switches, no new headers.

The only variable is speed vs cost: switch to Highspeed when you need faster responses, stay on Pro when you want to save. For a detailed cost breakdown, see Pro vs Highspeed pricing.

Two Ways to Get Started

Option 1: Browser, 3 steps, zero code

1. Open the Kimi K3.1 Pro Highspeed model page, log in or sign up (new users get 200 credits).
2. Paste your prompt into the chat box.
3. Send it. Costs are deducted from your balance based on actual token usage.

Option 2: API, 3 steps, into your app

1. Create an API Key in your HiAPI dashboard.
2. Replace YOUR_HIAPI_API_KEY in the curl example below and run it.
3. Hard-code model to kimi-k3.1-pro-highspeed. Done.

curl: Your First Call

curl https://api.hiapi.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_HIAPI_API_KEY" \
  -d '{
    "model": "kimi-k3.1-pro-highspeed",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain what a CDN is in two sentences."}
    ],
    "reasoning_effort": "max",
    "max_tokens": 2000
  }'

The response body is in choices[0].message.content. Add "stream": true for SSE streaming.

Python: Non-Streaming and Streaming

Non-streaming, using requests:

import requests

resp = requests.post(
    "https://api.hiapi.ai/v1/chat/completions",
    headers={"Authorization": "Bearer YOUR_HIAPI_API_KEY"},
    json={
        "model": "kimi-k3.1-pro-highspeed",
        "messages": [
            {"role": "user", "content": "Write a Python function that merges two sorted lists into one sorted list."}
        ],
        "reasoning_effort": "high",
        "max_tokens": 4000,
    },
    timeout=120,
)
resp.raise_for_status()
data = resp.json()
print(data["choices"][0]["message"]["content"])

Streaming: usage only appears in the last chunk. Parse SSE per standard convention:

import json
import requests

with requests.post(
    "https://api.hiapi.ai/v1/chat/completions",
    headers={"Authorization": "Bearer YOUR_HIAPI_API_KEY"},
    json={
        "model": "kimi-k3.1-pro-highspeed",
        "messages": [{"role": "user", "content": "Plan a Redis-based rate limiter."}],
        "reasoning_effort": "high",
        "stream": True,
    },
    stream=True,
    timeout=300,
) as resp:
    resp.raise_for_status()
    for line in resp.iter_lines(decode_unicode=True):
        if not line or not line.startswith("data:"):
            continue
        payload = line[len("data:"):].strip()
        if payload == "[DONE]":
            break
        chunk = json.loads(payload)
        if not chunk["choices"]:
            print("\nusage:", chunk["usage"])
            continue
        delta = chunk["choices"][0]["delta"]
        if delta.get("content"):
            print(delta["content"], end="", flush=True)

Parameter Reference

Switching from Pro to Highspeed: What to Watch For

1. Change the model ID, change nothing else. kimi-k3.1-pro becomes kimi-k3.1-pro-highspeed. base_url, headers, request body structure all stay the same.
2. Output price doubles; default max reasoning burns tokens. Highspeed output is $22.5426/M with max reasoning by default. A single request can produce a significant amount of reasoning tokens. Drop to low for simple tasks and watch your bill improve immediately.
3. Cached reads offset input costs. Cached reads at $0.2156 are identical to Pro. Put invariant system prompts at the front of messages; repeated requests hitting the cache reduce input costs to zero on that portion. High-frequency agent loops benefit the most.
4. Always cap max_tokens. Output at $22.5426/M with no ceiling will inflate bills. Set max_tokens explicitly on every request.

FAQ

What is the difference between Highspeed and Pro? 3x response speed at 2x the price. Same protocol, same parameters, same capabilities. A speed choice, not a feature difference.

I am already using Pro. How do I switch? Change the model ID to kimi-k3.1-pro-highspeed. One line. Nothing else changes.

How do I choose a reasoning level? Default is max (most complete output, most tokens). Use low for simple tasks (classification, extraction), high for moderate tasks (translation, summarization), max only when complex reasoning is needed.

Does it work with OpenRouter or third-party SDKs? Any SDK compatible with the OpenAI Chat Completions protocol works directly. Set base_url to https://api.hiapi.ai/v1 and api_key to your HiAPI Key.

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If you are already using Pro, switching to Highspeed takes one line of code. Open the model page and send a test request to feel the speed difference. For pricing math, see the pricing breakdown; for 5 real-time prompt templates, see the prompt recipes; for the full Pro API reference, see the Pro API guide.