Moonshot AI / Curated model
Kimi K2.6
The value recommendation for document workloads in the supplied Arena table.
Usage-based pricing
Pay for what you use.
Supplied reference prices per 1M input and output tokens. No additional percentage discount is applied.
Get API AccessModel capabilities
Built for your workload.
Modalities
text, document → text
Best fit
Document extraction, summaries and document reasoning at volume
Roten recommendations.
Where this model sits in the current curated selection.
Performance reference.
External benchmark data, with its source and verification status.
| Functionality | Source | Score | Snapshot |
|---|---|---|---|
| Documents | Arena | 1451 | Supplied Arena snapshot · date pending |
Arena scores are external benchmark signals. Scores and score gaps are taken from the supplied recommendation table. The external leaderboard source and snapshot date still need confirmation. They do not describe a literal percentage difference in real-world quality.
Read the methodologyImplementation
Test this model.
Copy a model test, replace the API key with your own, and run.
Copy the Python or cURL test for Kimi K2.6. Replace the API-key placeholder with your own key and run.
Document text test · The sample document is included in the request as text.
import json
from urllib.error import HTTPError
from urllib.request import Request, urlopen
API_KEY = "YOUR_ROTEN_API_KEY"
def send(url, payload=None):
data = json.dumps(payload).encode("utf-8") if payload is not None else None
request = Request(url, data=data, headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"})
try:
with urlopen(request, timeout=180) as response:
return json.load(response)
except HTTPError as error:
raise SystemExit(f"HTTP {error.code}: {error.read().decode('utf-8')}")
payload = json.loads(r'''{
"messages": [
{
"role": "user",
"content": "Summarize this sample project memo in two bullets.\n\nThe product team is evaluating an AI API for customer-support replies. The trial will compare response usefulness and request cost. Engineering will record the results before selecting a model."
}
],
"max_tokens": 256,
"stream": false,
"model": "kimi-k2.6"
}''')
result = send("https://api.roten.ai/v1/chat/completions", payload)
print(json.dumps(result, indent=2, ensure_ascii=False))API_KEY='YOUR_ROTEN_API_KEY'
curl --fail-with-body --silent --show-error 'https://api.roten.ai/v1/chat/completions' \
-H "Authorization: Bearer $API_KEY" \
-H 'Content-Type: application/json' \
--data '{
"messages": [
{
"role": "user",
"content": "Summarize this sample project memo in two bullets.\n\nThe product team is evaluating an AI API for customer-support replies. The trial will compare response usefulness and request cost. Engineering will record the results before selecting a model."
}
],
"max_tokens": 256,
"stream": false,
"model": "kimi-k2.6"
}'Python tests use the standard library. Video cURL tests also use Python 3 to read task responses.
Consider the alternative.
A different choice for the capabilities this model supports.
Claude Fable 5 High
AnthropicMaximum benchmark performance for documents.
Built for production
Running AI at scale?
Talk to us about volume pricing, model selection and migration.