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  • 1M context
  • Multimodal
  • Reasoning
  • Responses API
  • NEW · Oct 23

Kimi K3.1 Pro Highspeed on QuickSilver Pro

kimi-k3.1-pro-highspeed
Moonshot AILive availability—

Kimi K3.1 Pro Highspeed — Moonshot reports a 1M-token context and plans to release full weights by November 4, 2026.

  • 1M context
  • 944K max output
  • Vision
  • Tools
  • Reasoning · on by default
  • Streaming
  • JSON Schema output

ByRaullen ChaiUpdated

You pay · input / output per 1M

$4.00 / $20.00

At list price

same rates as OpenRouter

Pricing comparison ($/1M tokens)

Bar length = price relative to the list price

ProviderInputOutputvs QSP
QuickSilver Pro$4.00$20.00—
OpenRouter(moonshotai/kimi-k3.1-pro)$4.00$20.00same

Moonshot AI via OpenRouter list source: openrouter.ai/api/v1/models/moonshotai/kimi-k3.1-pro/endpoints

At a glance

Context
1M tokens
Input / 1M
$4.00
Output / 1M
$20.00
Thinks by default
Yes
Max output
944K
Latency · live
—

Long-context coding and image-based workflows.

When is Kimi K3.1 Pro Highspeed the right call?

Curated from catalog capabilities and published prices, not a benchmark.

When to use

Use for coding, images and tool workflows over a large context.

  • Long documents and codebases, up to 1M tokens of context
  • Agents that call tools, with strict JSON Schema output
  • Prompts that include images
  • Multi-step reasoning, on by default

When to use something else

Choose another model if you need forced named functions; these endpoints reject that tool_choice.

Compare alternatives in Models

Quickstart (curl)

Get an API key →
curl https://api.quicksilverpro.io/v1/chat/completions \
  -H "Authorization: Bearer $QSP_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3.1-pro-highspeed",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.quicksilverpro.io/v1",
    api_key=os.environ["QSP_KEY"],
)
response = client.chat.completions.create(
    model="kimi-k3.1-pro-highspeed",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.quicksilverpro.io/v1",
  apiKey: process.env.QSP_KEY,
});
const response = await client.chat.completions.create({
  model: "kimi-k3.1-pro-highspeed",
  messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);
curl https://api.quicksilverpro.io/v1/responses \
  -H "Authorization: Bearer $QSP_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3.1-pro-highspeed",
    "input": "Hello!"
  }'
python (OpenAI SDK)
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.quicksilverpro.io/v1",
    api_key=os.environ["QSP_KEY"],
)
response = client.chat.completions.create(
    model="kimi-k3.1-pro-highspeed",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

OpenAI-compatible. One-line migration via base_url.

Gateway behavior

Compatibility notes for this model.

prompt
Prompts up to 1M tokens.
max_tokens
Up to 944K output tokens.
  • /v1/chat/completions: Supported
  • /v1/responses: Supported
  • streaming: Supported
  • tools: Supported
  • image input: Supported
  • JSON Schema: Supported
  • reasoning: Supported

Where the price comes from

Catalog v65 · no separate long-context tier

Catalog verified
Prompt cache · not compared
$0.20 read

usage.cost = prompt_tokens × $4.00/1M + completion_tokens × $20.00/1M

/pricing.json

Base rates; cached prompt tokens bill at the cache rates above.

FAQ

Common questions

Per million tokens: $4.00 input / $20.00 output; cached input $0.20, at Moonshot endpoint list parity.

Kimi · Models

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Kimi K3.1 Pro Highspeed

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$4.00 / $20.00in / out per 1M tokens

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