Moonshot AI has made the complete Kimi K3.1 Pro weights available on Hugging Face, about two weeks after the model’s API launch. Teams can now examine, fine-tune and self-host the model, subject to its licensing terms and the hardware they have available.
Kimi K3 architecture and more extensive training
Kimi K3.1 Pro uses the same mixture-of-experts architecture as its predecessor, Kimi K3, activating only a subset of its parameters. It has approximately 2.8 trillion parameters in total, with about 104 billion active per token. The model accepts text and images and supports a context window of 1,048,576 tokens.
Moonshot says the model received substantially more intensive post-training, including joint reinforcement learning across hundreds of domains. The work aimed to improve tool use, planning and task completion. The company also says it removed cyber-related data from both training stages while keeping material for general-purpose programming.
Comparisons in agent benchmarks
In Moonshot’s published results, Kimi K3.1 Pro scored 65.7 on Terminal-Bench 4.0, compared with 66.4 for Claude Opus 5.5. On SWE-Marathon v1.1, Kimi scored 51.7 against Claude’s 46.3. The comparisons concern specific agent configurations and tasks.
Moonshot reported a score of 38.1 for Kimi on Terminal-Bench Science 0.1, versus 63.3 for Claude Opus 5.5 and 68.1 for GPT-6 Astra. In the company’s other comparisons, Kimi also trailed Claude Opus 5.5 on OSWorld V2.1 and BenchCAD, and the Claude and GPT models on ProgramBench and Humanity’s Last Exam without tools.
Artificial Analysis separately reported a 67.2% result for Kimi on Terminal-Bench 4.0. In that evaluation, Kimi ran mainly through Claude Code, while other models may have used different agent environments; the result reflects that setup as well as the model.
Self-hosting and API access
Self-hosting requires access to the complete set of weights. Those weights alone occupy approximately 5.6 TB at BF16 precision or 2.8 TB at 8-bit precision, excluding runtime overhead and the cache needed for long contexts.
At publication, Moonshot had not announced a separate license for Kimi K3.1 Pro. The earlier Kimi K3 uses a custom license that is not approved by the OSI; teams should check the repository for terms governing commercial use, modification and redistribution.
Alongside self-hosting, developers can access the model through Moonshot’s API as kimi-k3.1-pro or kimi-k3.1-pro-highspeed. Moonshot says Highspeed is functionally identical to the standard variant and two to three times faster. Both operate only in thinking mode; disabling it produces an HTTP 400 response.
The standard API costs $2 for one million input tokens and $10 for one million output tokens, a third less than Kimi K3’s stated rates; Highspeed is approximately twice as expensive per token.
