Kimi K2 Thinking model launches, touted to outperform GPT-5 via API
Why does this matter? Because a new language model just hit the public API scene, and the code snippet below shows exactly how you can talk to it. While the hype claims Kimi K2 Thinking can outpace GPT‑5, the real test lies in the request you send and the response you get.
Here’s the thing: the endpoint is hosted at api.moonshot.cn, a domain that suggests a Chinese‑based service, and it expects a standard OpenAI‑style payload. The curl command demonstrates the required headers—Content‑Type set to application/json and an Authorization token placeholder—followed by a JSON body that names the model, feeds a simple “hello” prompt, and sets temperature to 1.0. If you’re familiar with RESTful calls, the structure will look familiar; if not, the snippet is a concrete entry point.
For anyone wanting to verify the performance claims or integrate the model into an app, this is the first line of code you’ll need to run. The details that follow spell out the exact request format.
model parameter:$ curl https://api.moonshot.cn/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $MOONSHOT_API_KEY" \ -d '{ "model": "kimi-k2-thinking", "messages": [ {"role": "user", "content": "hello"} ], "temperature": 1.0 }' For more details on API use, checkout this guide. Also Read: Kimi OK Computer: A Hands-On Guide to the Free AI Agent Prompt: "Simulate a structured debate between Nikola Tesla and Thomas Edison on the ethics of AI today. Ground their arguments in their actual writings, then extend their worldviews to comment on issues like deepfakes, automation, and open-source models." Output: My Take: Kimi K2 Thinking delivered an outstanding performance on the task of simulating a historically grounded debate between Nikola Tesla and Thomas Edison on the ethics of modern AI.
Could Kimi K2 Thinking truly outpace GPT‑5? The article asserts it does, citing slide generation from a single prompt and autonomous web‑browsing as evidence. Yet the claim rests on a single author’s preference rather than systematic benchmarks, leaving the comparison open to question.
Built as an open‑source thinking agent, Kimi K2 Thinking expands its reasoning steps at test time and integrates tools dynamically, a design that promises flexible problem‑solving. Its API is already exposed; a simple curl request with the model name “kimi‑k2‑thinking” and a user message demonstrates immediate accessibility. Because the model’s parameters and training data are not disclosed, assessing its depth of understanding remains difficult.
Moreover, the description of “test‑time scaling” lacks concrete metrics, so whether the model consistently delivers superior performance across diverse tasks is unclear. For developers who value an extensible, self‑acting system, the offering may be attractive, but the evidence presented does not definitively prove superiority over existing large‑language models. Further independent evaluation would be needed to confirm the article’s enthusiastic appraisal.
Further Reading
- Kimi K2 Thinking - Simon Willison's Weblog - Simon Willison's Weblog
- Introducing Kimi K2 Thinking - Moonshot - Moonshot AI
- Kimi K2 Thinking is Moonshot AI's most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. - OpenRouter
- Using the kimi-k2-thinking model - Moonshot AI Open Platform - Moonshot AI Open Platform
- NEW Kimi K2 Thinking - Best Open Model? - YouTube
Common Questions Answered
What endpoint and payload format does the Kimi K2 Thinking API use?
The Kimi K2 Thinking model is accessed via the endpoint https://api.moonshot.cn/v1/chat/completions and expects an OpenAI‑style JSON payload. The request must include a Content‑Type header of application/json, an Authorization Bearer token, and fields such as "model", "messages", and "temperature".
How does the article claim Kimi K2 Thinking outperforms GPT‑5?
The article asserts that Kimi K2 Thinking can surpass GPT‑5 by generating slide decks from a single prompt and performing autonomous web‑browsing, showcasing more advanced reasoning steps. However, it notes that these claims rely on a single author's preference rather than systematic benchmark testing.
What role does the "temperature" parameter play in the Kimi K2 Thinking API request shown?
In the curl example, the "temperature" parameter is set to 1.0, which instructs the model to produce more diverse and creative outputs. Higher temperature values increase randomness, while lower values make responses more deterministic.
What capabilities does Kimi K2 Thinking’s open‑source design provide at test time?
Kimi K2 Thinking’s open‑source architecture expands its reasoning steps dynamically during inference and can integrate external tools such as web browsers on the fly. This design aims to enable flexible problem‑solving and more complex, multi‑step tasks compared to static models.