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Moonshot AI just released an open-access model that’s outperforming the biggest names in AI—and yes, it’s completely free to use.
If you’re into large language models (LLMs), this might be the moment you’ve been waiting for.
Today, Moonshot AI—a startup out of China founded just last year—released its new Kimi K2 Thinking model. It’s open source, it’s massive, and somehow it’s managing to outperform not just previous open-weight models like MiniMax-M2, but also premium proprietary models like OpenAI’s GPT-5 and Anthropic’s Claude Sonnet 4.5 on major benchmarks.
And you can use it for free.
Wait, Who Is Moonshot AI?
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Moonshot is a relatively new face in the AI space. Founded in 2023 and backed by some of China’s major tech players, the company is making waves with this latest release. Kimi K2 Thinking feels like the company planting a flag—loudly—in the frontier model landscape.
What’s So Special About Kimi K2 Thinking?
The model is fully open source and runs under a Modified MIT License. That means it’s commercially usable and remixable—pretty incredible considering its raw performance. There’s only one catch: if your app serves more than 100 million monthly active users or generates over $20 million in revenue per month, you have to visibly credit “Kimi K2” in your user interface.
Basically, if you’re not Google-scale, it’s open season.
It’s built using a sparse Mixture-of-Experts architecture: one trillion parameters total, with 32 billion active per inference. That makes it both powerful and relatively efficient.
It’s Not Just Big—It’s Smart
Here’s how Kimi K2 Thinking stacks up on real-world benchmarks:
- Humanity’s Last Exam (HLE): 44.9%
- BrowseComp (agentic web search): 60.2% (vs GPT-5’s 54.9% and Claude 4.5’s 24.1%)
- SWE-Bench Verified: 71.3%
- LiveCodeBench v6: 83.1%
- GPQA Diamond (graduate-level QA): 85.7% (GPT-5 managed 84.5%)
- Seal-0 (info retrieval): 56.3%
K2 Thinking didn’t just peek into GPT-5’s territory—it surpassed it in several reasoning, coding, and agentic benchmarks, especially on tasks that involve long tool use chains or complex workflows.
And compared to MiniMax-M2, which held the “top open-weight model” spot for about a week, K2’s margins are even wider. It even equals or beats M2 on financial and coding tasks.
Real Agentic AI, not Just a Chatbot
One standout feature is K2 Thinking’s ability to execute long reasoning chains and maintain coherence across 200 to 300 tool calls. It includes step-by-step logic traces as part of its output, which provides transparency and reliability for extended tasks.
Imagine an AI autonomously running a full news analysis routine—fetching up-to-date articles, sorting them by relevance, extracting insights, then writing a structured summary. Kimi K2 can do that, end-to-end.
Moonshot even published a reference implementation that lets the model autonomously create daily news reports through structured tool workflows.
It’s Efficient Too—and It Won’t Break the Bank
Even though it’s operating at massive scale, Moonshot’s pricing is surprisingly low:
- $0.15 per million tokens (cached)
- $0.60 per million tokens (new input)
- $2.50 per million tokens (output)
This is significantly cheaper than GPT-5, which charges $1.25 input and a whopping $10 output per million tokens.
Compared to MiniMax-M2’s rates ($0.30 input, $1.20 output), Kimi K2 costs less and performs better.
So whether you’re running research experiments or deploying to production, it’s technically and financially pretty viable.
So What Does This Really Mean?
For developers and enterprises alike, Kimi K2 Thinking is a wake-up call.
Until recently, there was a fairly clean line between free open-source models (powerful, but not quite top-tier) and proprietary systems (extremely capable, but expensive and closed off).
K2 Thinking changes that.
You’re now looking at a fully open source model that equals or outperforms the best commercial models on key metrics—at a fraction of the price.
And it’s not just a technical flex. It lands at a time when the financial stability of some of AI’s biggest players has come under scrutiny. OpenAI, for example, just had to walk back comments from their CFO suggesting the US government might need to backstop their massive infrastructure investments.
In contrast, Moonshot’s model doesn’t rely on trillion-dollar data centers. It relies on optimized design: sparse routing, quantization-aware training, and clever memory management for long context windows (up to 256K tokens).
The Bigger Picture
This shift could redefine how AI is built, distributed, and eventually, who benefits from it.
Companies like Airbnb are already experimenting with Chinese open-source models like Qwen instead of relying purely on GPT-4. With tools like Kimi K2 Thinking on the table, the case to stick with pricey APIs gets weaker.
At some point, the question becomes: Why rent a black box when you can own a transparent, high-performance model tailored to your needs?
For now, Moonshot AI has firmly placed itself in the global AI conversation, proving that you don’t need $10 billion in compute contracts to lead the frontier.
You just need to build smarter.
🧠 Try out Kimi K2 Thinking:
- Chat interface: kimi.com
- API access: platform.moonshot.ai
- Model weights/code: Hugging Face
Keywords: Kimi K2 Thinking, Moonshot AI, open source AI, GPT-5 alternative, Claude Sonnet 4.5, agentic reasoning, Mixture-of-Experts architecture, BrowseComp, open-weight LLMs, large language models, Hugging Face, affordable AI models, AI benchmarks
