kimi-k2Moonshot AI
- endpoints
- 1
- regions
- 1
- api
- chat
- released
- July 2025
capabilities 4/8
| # | flags | ||||||
|---|---|---|---|---|---|---|---|
| 1 |  Google LLC (Vertex AI) | Global | 262K | $0.60 | $2.50 | $0.06 | zdr |
no measured traffic for this model yet. the endpoints table above carries provider pricing
| eval | Score as a share of 100 | /100 |
|---|---|---|
| Math Index | 94.7 | |
| AIME 2025 | 94.7 | |
| τ²-Bench | 93.0 | |
| LiveCodeBench | 85.3 | |
| MMLU Pro | 84.8 | |
| GPQA Diamond | 83.8 | |
| Terminal-Bench Hard | 31.1 | |
| Humanity's Last Exam | 23.8 | |
| Intelligence Index | 22.0 | |
| 9 evals | mean | 68.1 |
Scores from Artificial Analysis and public leaderboards, normalised to 100. Bar colour is the band, not the rank: green 80 and up, blue 55 and up, amber 30 and up. Benchmarks measure narrow skills, so test on your own workload before committing. Released 2025-11-06.
vertex/kimi-k2this 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="vertex/kimi-k2", 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 Thinking is an open-source model that operates as a "thinking agent," reasoning step-by-step while using tools to achieve state-of-the-art performance on various benchmarks. It is capable of executing up to 200-300 sequential tool calls without human intervention, allowing it to solve complex problems across a wide range of tasks. The model uses Quantization-Aware Training (QAT) to support INT4 inference, which provides a roughly 2x improvement in generation speed.
questions 5
Which providers serve kimi-k2?
How much does kimi-k2 cost?
Is kimi-k2 open weights?
What is the context window of kimi-k2?
How do I use kimi-k2 with the OpenAI SDK?
more from moonshot ai 5
| model | context | from /M | providers |
|---|---|---|---|
| kimi-k3 | 1.0M | $3.00 | 5 |
| kimi-k2.7-code | 262K | $0.75 | 4 |
| kimi-k2.6 | 262K | $0.60 | 5 |
| kimi-k2.5 | 262K | $0.45 | 3 |
| kimi-k2-instruct | 131K | $0.57 | 1 |
route kimi-k2 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