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Kimi K2.7 Code

Moonshot AI · OpenAI-compatible API

moonshotai/kimi-k2.7-code

USD / 1M tokens

Input$0.69
Cached Input$0.15
Output$3.29
Context
262K
Tool Calling
✓
Reasoning
✓
Vision
✗
Streaming
✓
OpenAI-compatible API
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.iteracompute.com/v1",
    api_key=os.environ["ITERACOMPUTE_API_KEY"],
)
response = client.chat.completions.create(
    model="moonshotai/kimi-k2.7-code",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Catalog snapshot: 2026-10-09. Live prices have not been verified yet.

Overview

Text input, streaming, reasoning, tool calling and structured outputs; up to 262,144 output tokens.

Tasks and selection tradeoffs

Test-driven bug repair

Supply a failing test and the relevant source; require a minimal diff and a test rerun.

Repository navigation

Locate callers and explain a change boundary before permitting edits.

Task suggestions based on catalog features and price, not model quality benchmarks.

Choose your tool

Choose a model and tool for your task →

Pricing

Prices in USD per 1M tokens.

Input$0.69
Cached Input$0.15
Output$3.29

Example task: Repair a function across several agent steps

Assume 30,000 aggregate input tokens and 3,000 billable output tokens across all calls. No cache hit.

30,000 × $0.69 / 1M + 3,000 × $3.29 / 1M = $0.030570

If usage actually reports 22,500 of those input tokens as cached, replace that portion at the cached-input rate ($0.15/1M): $0.018420. Cached tokens are part of total input, never added on top.

Illustrative calculation using the October 9, 2026 catalog snapshot, not a measured bill or promised task size. Live rates above may differ. Include every agent call and retry, repeated history and billable reasoning output; settle against actual usage and Console records.

Known limits and verification scope

  • This text-only catalog entry has 262,144 context tokens, smaller than Kimi K3’s 1M. Filter repository files; use a vision-capable model for screenshots.
  • Catalog context: 262,144 tokens. Input and requested output share the context budget; this is not a measured usable boundary.
  • The context and capability flags describe the public catalog. They do not establish a particular tool workflow or a quality score.
  • Start with one representative task. Check correctness, actual usage and response time before increasing traffic.
  • This page has no recorded end-to-end tool validation for this model. The integration examples let you run and record your own check.

Verified parameters and open questions

Parameter / protocol fieldEvidence status
model / messagesRequest example only; not live-verified for this model
tools / tool_calls / tool messagesCatalog lists tool calling; argument and result cycle unverified
chat_template_kwargs.enable_thinking=falseQwen-specific option; do not copy to this model
stream / temperature / top_p / max_tokens / response_format / reasoning_effortNo verified values recorded for this exact model ID

A catalog capability flag is not a tested parameter value. Omit optional controls until checked with your exact model ID; an HTTP 200 alone does not prove the option took effect.

API

Base URL
https://api.iteracompute.com/v1
Endpoint
POST /v1/chat/completions
Model ID
moonshotai/kimi-k2.7-code
Authentication
Authorization: Bearer ITERACOMPUTE_API_KEY

View Docs

FAQ

How do I use Kimi K2.7 Code with the OpenAI SDK?

Set base_url to https://api.iteracompute.com/v1 and model to moonshotai/kimi-k2.7-code. Authenticate with your IteraCompute API key.

What does Kimi K2.7 Code cost?

See the pricing section for current input, cached-input and output rates in USD per 1M tokens. Check the refresh time before use.

What context length is available?

262,144 tokens.

Ready for your first request?

Create your key, choose a tool and get a non-empty first reply.