K2-Think: A Parameter-Efficient Reasoning System

K2-Think is a 32 billion parameter open-weights model developed by Zhoujun Cheng et al., primarily focused on general reasoning and competitive mathematical problem solving. It is distinguished by its strong performance in complex analytical tasks and its parameter-efficient design. The model leverages a substantial 131072-token context window, enabling it to process extensive inputs for intricate problems.

Key Capabilities & Performance

Exceptional Mathematical Reasoning: Achieves high scores on challenging math benchmarks such as AIME 2024 (90.83%), AIME 2025 (81.24%), HMMT 2025 (73.75%), and OMNI-Math-HARD (60.73%).
Code and Science Proficiency: Demonstrates solid performance in LiveCodeBench v5 (63.97%) and GPQA-Diamond (71.08%), indicating broader reasoning capabilities beyond pure mathematics.
High-Speed Inference: Optimized for deployment on Cerebras Wafer-Scale Engine (WSE) systems, achieving throughputs of approximately 2,000 tokens/sec, significantly faster than typical cloud setups.
Safety Considerations: Evaluated across four safety dimensions, with an overall Safety-4 Macro average of 0.75, indicating efforts to mitigate harmful outputs.

Ideal Use Cases

Advanced Mathematical Problem Solving: Suited for applications requiring high accuracy in competitive math and complex quantitative analysis.
General Reasoning Tasks: Effective for scenarios demanding robust logical inference and problem-solving across various domains.
High-Throughput Inference Environments: Particularly beneficial for deployments where rapid generation of long responses is critical, especially with specialized hardware.