How can we design AI programs that may plan, purpose, and act over lengthy sequences of choices with out fixed human steering? Moonshot AI has launched Kimi K2 Considering, an open supply considering agent mannequin that exposes the complete reasoning stream of the Kimi K2 Combination of Consultants structure. It targets workloads that want deep reasoning, lengthy horizon device use, and steady agent habits throughout many steps.
What’s Kimi K2 Considering?
Kimi K2 Considering is described as the newest, most succesful model of Moonshot’s open supply considering mannequin. It’s constructed as a considering agent that causes step-by-step and dynamically invokes instruments throughout inference. The mannequin is designed to interleave chain of thought with operate calls so it will probably learn, suppose, name a device, suppose once more, and repeat for a whole bunch of steps.
The mannequin units a brand new cutting-edge on Humanity’s Final Examination and BrowseComp, whereas sustaining coherent habits throughout about 200 to 300 sequential device calls with out human interference.
On the similar time, K2 Considering is launched as an open weights mannequin with a 256K token context window and native INT4 inference, which reduces latency and GPU reminiscence utilization whereas preserving benchmark efficiency.
K2 Considering is already reside on kimi.com in chat mode and is accessible via the Moonshot platform API, with a devoted agentic mode deliberate to show the complete device utilizing habits.
Structure, MoE design, and context size
Kimi K2 Considering inherits the Kimi K2 Combination of Consultants design. The mannequin makes use of a MoE structure with 1T whole parameters and 32B activated parameters per token. It has 61 layers together with 1 dense layer, 384 specialists with 8 specialists chosen per token, 1 shared professional, 64 consideration heads, and an consideration hidden dimension of 7168. The MoE hidden dimension is 2048 per professional.
The vocabulary dimension is 160K tokens and the context size is 256K. The eye mechanism is Multi head Latent Consideration, and the activation operate is SwiGLU.
Take a look at time scaling and lengthy horizon considering
Kimi K2 Considering is explicitly optimized for check time scaling. The mannequin is skilled to develop its reasoning size and gear name depth when dealing with tougher duties, fairly than counting on a hard and fast quick chain of thought.
On Humanity’s Final Examination within the no instruments setting, K2 Considering scores 23.9. With instruments, the rating rises to 44.9, and within the heavy setting it reaches 51.0. On AIME25 with Python, it studies 99.1, and on HMMT25 with Python it studies 95.1. On IMO AnswerBench it scores 78.6, and on GPQA it scores 84.5.
The testing protocol caps considering token budgets at 96K for HLE, AIME25, HMMT25, and GPQA. It makes use of 128K considering tokens for IMO AnswerBench, LiveCodeBench, and OJ Bench, and 32K completion tokens for Longform Writing. On HLE, the utmost step restrict is 120 with a 48K reasoning finances per step. On agentic search duties, the restrict is 300 steps with a 24K reasoning finances per step.
Benchmarks in agentic search and coding
On agentic search duties with instruments, K2 Considering studies 60.2 on BrowseComp, 62.3 on BrowseComp ZH, 56.3 on Seal 0, 47.4 on FinSearchComp T3, and 87.0 on Frames.
On basic data benchmarks, it studies 84.6 on MMLU Professional, 94.4 on MMLU Redux, 73.8 on Longform Writing, and 58.0 on HealthBench.
For coding, K2 Considering achieves 71.3 on SWE bench Verified with instruments, 61.1 on SWE bench Multilingual with instruments, 41.9 on Multi SWE bench with instruments, 44.8 on SciCode, 83.1 on LiveCodeBenchV6, 48.7 on OJ Bench within the C plus plus setting, and 47.1 on Terminal Bench with simulated instruments.
Moonshot group additionally defines a Heavy Mode that runs eight trajectories in parallel, then aggregates them to supply a ultimate reply. That is utilized in some reasoning benchmarks to squeeze out additional accuracy from the identical base mannequin.
Native INT4 quantization and deployment
K2 Considering is skilled as a local INT4 mannequin. The analysis group applies Quantization Conscious Coaching through the publish coaching stage and makes use of INT4 weight solely quantization on the MoE parts. This helps INT4 inference with roughly a 2x era pace enchancment in low latency mode whereas sustaining cutting-edge efficiency. All reported benchmark scores are obtained below INT4 precision.
The checkpoints are saved in compressed tensors format and might be unpacked to increased precision codecs resembling FP8 or BF16 utilizing the official compressed tensors instruments. Really useful inference engines embrace vLLM, SGLang, and KTransformers.
Key Takeaways
- Kimi K2 Considering is an open weights considering agent that extends the Kimi K2 Combination of Consultants structure with specific lengthy horizon reasoning and gear use, not simply quick chat fashion responses.
- The mannequin makes use of a trillion parameter MoE design with about tens of billions of energetic parameters per token, a 256K context window, and is skilled as a local INT4 mannequin with Quantization Conscious Coaching, which supplies about 2x quicker inference whereas conserving benchmark efficiency steady.
- K2 Considering is optimized for check time scaling, it will probably perform a whole bunch of sequential device calls in a single job and is evaluated below giant considering token budgets and strict step caps, which is necessary while you attempt to reproduce its reasoning and agentic outcomes.
- On public benchmarks, it leads or is aggressive on reasoning, agentic search, and coding duties resembling HLE with instruments, BrowseComp, and SWE bench Verified with instruments, displaying that the considering oriented variant delivers clear beneficial properties over the bottom non considering K2 mannequin.
Kimi K2 Considering is a powerful sign that check time scaling is now a first-class design goal for open supply reasoning fashions. Moonshot AI is just not solely exposing a 1T parameter Combination of Consultants system with 32B energetic parameters and 256K context window, it’s doing so with native INT4 quantization, Quantization Conscious Coaching, and gear orchestration that runs for a whole bunch of steps in manufacturing like settings. Total, Kimi K2 Considering reveals that open weights reasoning brokers with lengthy horizon planning and gear use have gotten sensible infrastructure, not simply analysis demos.
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