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  • 256K context
  • Reasoning
  • Responses API

Kimi K2.7 Code on QuickSilver Pro

kimi-k2.7-code
Moonshot AILive availability—

Kimi K2.7 Code is Moonshot's coding-tuned K2 — the same trillion-parameter architecture (32B active, 256K context) as K2.6, retuned for long-horizon agentic coding. On QuickSilver Pro it is $0.6712 input / $3.35 output per million tokens, at parity with the OpenRouter endpoint selected by this route, with no markup or discount. Moonshot reports it uses ~30% fewer reasoning tokens per task than K2.6 while scoring higher on coding evals.

  • 256K context
  • Tools
  • Reasoning · on by default
  • Streaming
  • JSON Schema output

ByRaullen ChaiUpdated

You pay · input / output per 1M

$0.6712 / $3.35

At list price

same rates as OpenRouter

Pricing comparison ($/1M tokens)

Bar length = price relative to the list price

ProviderInputOutputvs QSP
QuickSilver Pro$0.6712$3.35—
OpenRouter(moonshotai/kimi-k2.7-code)$0.6712$3.35same

OpenRouter list source: openrouter.ai/api/v1/models/moonshotai/kimi-k2.7-code/endpoints

At a glance

Context
256K tokens
Input / 1M
$0.6712
Output / 1M
$3.35
Thinks by default
Yes
Latency · live
—

Long-horizon agentic coding — Moonshot's coding-tuned K2 with leaner reasoning-token overhead than K2.6.

When is Kimi K2.7 Code the right call?

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

When to use

Reach for K2.7 Code as the engine behind a coding agent (opencode, Cline, Aider, and Claude-Code-style loops): multi-step refactors, repo-wide changes, and plan-then-act agents that coordinate many tool calls across a 256K-token working set. It's tuned for exactly the long-horizon agentic-coding trajectory where verbose reasoning gets re-read every turn — Moonshot reports higher coding-eval scores at ~30% fewer reasoning tokens than K2.6.

  • Long documents and codebases, up to 256K tokens of context
  • Agents that call tools, with strict JSON Schema output
  • Multi-step reasoning, on by default
  • Responses API clients as well as Chat Completions

When to use something else

For routine chat, short-context codegen, or non-agentic single-shot tasks, the per-token price is overkill — DeepSeek V4 Flash ($0.06/$0.18) or V4 Pro ($0.70/$2.10) land most of those for less. For pure mathematical reasoning, DeepSeek V4 Pro. For general (non-coding) Opus-class agentic planning, Kimi K2.6 is the sibling to A/B against.

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-k2.7-code",
    "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-k2.7-code",
    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-k2.7-code",
  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-k2.7-code",
    "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-k2.7-code",
    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 256K tokens.
  • /v1/chat/completions: Supported
  • /v1/responses: Supported
  • streaming: Supported
  • tools: Supported
  • image input: Not listed for this model
  • JSON Schema: Supported
  • reasoning: Supported

Where the price comes from

Catalog v67 · no separate long-context tier

Catalog verified
Prompt cache · not compared
$0.18 read

usage.cost = prompt_tokens × $0.6712/1M + completion_tokens × $3.35/1M

/pricing.json

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

FAQ

Common questions

Same K2 architecture — a trillion parameters, 32B active, 256K context — but K2.7 Code is retuned specifically for long-horizon agentic coding. Moonshot's published numbers show higher coding-eval scores (Kimi Code Bench v2, Program Bench, MLS Bench Lite) while emitting ~30% fewer reasoning tokens per task than K2.6. In agent loops the reasoning from each turn is re-read as input on the next, so fewer reasoning tokens means lower cost per completed task, not just per call. K2.6 is still the better pick for general (non-coding) agentic planning; A/B the two on your workload.

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Kimi K2.7 Code

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$0.6712 / $3.35in / out per 1M tokens

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