How will we design AI methods that may plan, motive, and act over lengthy sequences of choices with out fixed human steerage? Moonshot AI has launched Kimi K2 Considering, an open supply considering agent mannequin that exposes the total reasoning stream of the Kimi K2 Combination of Consultants structure. It targets workloads that want deep reasoning, lengthy horizon instrument use, and steady agent conduct throughout many steps.
What’s Kimi K2 Considering?
Kimi K2 Considering is described as the most recent, 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 may learn, assume, name a instrument, assume once more, and repeat for tons of of steps.
The mannequin units a brand new cutting-edge on Humanity’s Final Examination and BrowseComp, whereas sustaining coherent conduct throughout about 200 to 300 sequential instrument 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 dwell on kimi.com in chat mode and is accessible by the Moonshot platform API, with a devoted agentic mode deliberate to reveal the total instrument utilizing conduct.
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 skilled, 64 consideration heads, and an consideration hidden dimension of 7168. The MoE hidden dimension is 2048 per skilled.
The vocabulary measurement is 160K tokens and the context size is 256K. The eye mechanism is Multi head Latent Consideration, and the activation operate is SwiGLU.
Check 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 more durable duties, moderately than counting on a set brief 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 price range per step. On agentic search duties, the restrict is 300 steps with a 24K reasoning price range 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 normal 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 crew additionally defines a Heavy Mode that runs eight trajectories in parallel, then aggregates them to provide a last reply. That is utilized in some reasoning benchmarks to squeeze out further accuracy from the identical base mannequin.
Native INT4 quantization and deployment
K2 Considering is skilled as a local INT4 mannequin. The analysis crew applies Quantization Conscious Coaching throughout the put up coaching stage and makes use of INT4 weight solely quantization on the MoE elements. This helps INT4 inference with roughly a 2x technology velocity enchancment in low latency mode whereas sustaining cutting-edge efficiency. All reported benchmark scores are obtained underneath 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 helpful 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 brief 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 maintaining benchmark efficiency steady.
- K2 Considering is optimized for check time scaling, it may perform tons of of sequential instrument calls in a single activity and is evaluated underneath massive considering token budgets and strict step caps, which is vital if 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, exhibiting that the considering oriented variant delivers clear positive factors 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 shouldn’t be 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 tons of of steps in manufacturing like settings. Total, Kimi K2 Considering exhibits that open weights reasoning brokers with lengthy horizon planning and gear use have gotten sensible infrastructure, not simply analysis demos.
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its recognition amongst audiences.
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