Model reference · open weights
Kimi-K2 is an open-weight language model from moonshotai, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
0. Changelog 2025.8.11 - Messages with name field are now supported. We’ve also moved the chat template to a standalone file for easier viewing. 2025.7.18 - We further modified our chat template to improve its robustness. The default system prompt has also been updated. 2025.7.15 - We have updated our tokenizer implementation. Now special tokens like [EOS] can be encoded to their token ids. - We fixed a bug in the chat template that was breaking multi-turn tool calls. 1. Model Introduction Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities. Key Features - Large-Scale Training: Pre-trained a 1T parameter MoE model on 15.5T tokens with zero training instability. - MuonClip Optimizer: We apply the Muon optimizer to an unprecedented scale, and develop novel optimization techniques to resolve instabilities while scaling up. - Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving. Model Variants - Kimi-K2-Base: The foundation model, a strong start for researchers and builders who want full control for fine-tuning and custom solutions. - Kimi-K2-Instruct: The post-trained model best for drop-in, general-purpose chat and agentic experiences. It is a reflex-grade model without long thinking. 2. Model Summary 3. Evaluation Results Instruction model evaluation results • Bold denotes global SOTA, and underlined denotes open-source SOTA. • Data points marked with are taken directly from the model's tech report or blog. • All metrics, except for SWE-bench Verified (Agentless), are evaluated with an 8k output token length. SWE-bench Verified (Agentless) is limited to a 16k output token length. • Kimi K2 achieves 65.8% pass@1 on the SWE-bench Verified tests with bash/editor tools (single-attempt patches, no test-time compute). It also achieves a 47.3% pass@1 on the SWE-bench Multilingual tests under the same conditions. Additionally, we report results on SWE-b
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | moonshotai |
|---|---|
| Type | Language models |
| Parameters (lead) | 1026.4B |
| Context | 128k tokens |
| Variants | 2 |
| Runs with | transformers |
| Released | 2025-07-11 |
| Popularity | 163k downloads / month |
| Likes | 2,376 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys kimi-k2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (kimi-k2 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"kimi-k2","messages":[{"role":"user","content":"Hello"}]}'
Details
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Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗