kimi-k2.7-code-highspeedMoonshot AI
- endpoints
- 1
- regions
- 1
- api
- chat
- released
- June 2026
capabilities 5/8
| # | flags | ||||||
|---|---|---|---|---|---|---|---|
| 1 | Global | 262K | $1.90 | $8.00 | $0.38 | no-train |
no measured traffic for this model yet. the endpoints table above carries provider pricing
no published benchmark scores for this model
tencent/kimi-k2.7-code-highspeedthis model has no managed policy yet, so call the provider endpoint directly. every id in the endpoints table works the same way
Base url is https://router.requesty.ai/v1 for every id here. One key reaches the whole catalog.
from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1",) response = client.chat.completions.create( model="tencent/kimi-k2.7-code-highspeed", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ],) print(response.choices[0].message.content)
Change the base url, use your Requesty key, set the model to any id in the call pane. Existing OpenAI SDK code needs no other edit, and the same key reaches every other model in the catalog, so switching later is a one-parameter change. Browse all models
Kimi K2.7 Code HighSpeed is the higher-throughput variant of Kimi K2.7 Code, Moonshot's coding model with a 256K context window that follows instructions reliably across long agentic sessions.
questions 5
Which providers serve kimi-k2.7-code-highspeed?
How much does kimi-k2.7-code-highspeed cost?
Is kimi-k2.7-code-highspeed open weights?
What is the context window of kimi-k2.7-code-highspeed?
How do I use kimi-k2.7-code-highspeed with the OpenAI SDK?
more from moonshot ai 6
| model | context | from /M | providers |
|---|---|---|---|
| kimi-k3.1-pro | 1.0M | $2.00 | 1 |
| kimi-k3.1-pro-highspeed | 1.0M | $4.00 | 1 |
| kimi-k3 | 1.0M | $3.00 | 7 |
| kimi-k2.7-code | 262K | $0.75 | 5 |
| kimi-k2.6 | 262K | $0.60 | 5 |
| kimi-k2.5 | 262K | $0.45 | 3 |
route kimi-k2.7-code-highspeed through one endpoint
One key for 1 provider on this model and 600+ others. No markup on provider prices, automatic failover, caching built in. Weekly aggregates in the measured pane come from production traffic routed through Requesty, one line per provider on a shared axis. Methodology
