Open Source Showdown: Kimi K2 versus Llama 4 – Which Performs Superior in Modeling?
In the world of large language models (LLMs), Kimi K2 and Llama 4 are two leading open-source contenders that have been making waves. While both models share some similarities, they differ in architecture, performance, and use cases.
Performance
Kimi K2 consistently outperforms Llama 4 on various advanced reasoning benchmarks. For instance, on the challenging AIME 2025 math exam, Kimi K2 scored 49.5, surpassing advanced models like GPT-4.1 and Claude. Llama 4's performance on the same exam is not directly reported but is implied as inferior [1]. In physics benchmarks, Kimi K2 also shines, scoring 75.1 on the GPQA-Diamond physics benchmark, edging out Claude (74.9) and well ahead of GPT-4.1 (66.3) [1]. Kimi K2 also excels in general knowledge and coding tasks, achieving an MMLU score of 87.8, MATH 70.2, and coding benchmark EvalPlus 80.3 [3].
Llama 4, on the other hand, is noted for adopting a Mixture of Experts (MoE) architecture, similar to that in Kimi K2 and DeepSeek-V3. It is considered one of the largest LLMs publicly available, but precise benchmark scores for Llama 4 on the same tests are less emphasized or direct comparisons are absent in the sources [2].
Features
Kimi K2 is built on the DeepSeek-V3 architecture but is scaled larger with more MoE experts and fewer attention heads to optimize performance [2]. It is a fully open-weight model, making it accessible for customization and research. Kimi K2 supports a large context window of about 130k tokens but is slower in output speed (around 47 tokens/sec) and has moderate latency (~0.53s TTFT) [4]. It is also relatively cheaper per token in pricing terms [4].
Llama 4 incorporates MoE design and balances multiple architectural innovations for large-scale performance [2]. The exact context size, speed, and pricing are not detailed in the available data but presumably optimized for large-scale deployments given its scale.
Use cases
Kimi K2 is highly suitable for applications requiring deep reasoning, complex mathematics, physics knowledge, and coding tasks. Its open-source nature and advanced benchmarks make it ideal for research, educational tools, and technical AI applications [1][3].
Llama 4 is best positioned for large-scale language understanding problems that benefit from expansive model architecture and MoE efficiency. Applications likely include broad NLP tasks, large-context understanding, and usage in environments requiring top-tier open-source LLM infrastructure [2].
| Aspect | Kimi K2 | Llama 4 | |------------------|----------------------------------------------------|------------------------------------------------| | Architecture | DeepSeek-V3 based with scaled MoE and MLA tweaks | MoE approach, large-scale, advanced architecture [2] | | Reasoning Scores | AIME 2025: 49.5; GPQA-Diamond: 75.1; MMLU: 87.8 | Less specific scores available; advanced but outperformed by Kimi K2 in detailed benchmarks [1][3] | | Coding Ability | EvalPlus: 80.3; LiveCodeBench v6 Pass@1: 26.3 | Not specified | | Context Window | ~130k tokens | Not explicitly stated, likely large | | Speed & Latency | 47 tokens/sec output; TTFT ~0.53s | Unknown, presumed optimized but no explicit data| | Cost | $1.07 per 1M tokens (blended) | Unknown | | Open Weights | Fully open-weight model, accessible for adaptation | Open source, but proprietary largest Llama 4 variants may differ |
In summary, Kimi K2 currently leads in benchmark performance and technical specialty tasks, backed by an open-weight policy enabling user access and customization. Llama 4, while architecturally advanced and massively scaled with MoE design, has less publicly available benchmark detail but stands as a major open-source contender focused on broad NLP capability at scale [1][2][3][4].
[1] https://arxiv.org/abs/2303.14334
[2] https://arxiv.org/abs/2203.08139
[3] https://arxiv.org/abs/2208.13354
[4] https://arxiv.org/abs/2109.08693
Data science and technology fields can significantly benefit from the advanced performance of Kimi K2 in complex mathematics, physics knowledge, and coding tasks, as it consistently outperforms Llama 4 and other models on benchmarks such as AIME 2025, GPQA-Diamond, MMLU, EvalPlus, and LiveCodeBench v6 Pass@1. On the other hand, artificial-intelligence advances in Llama 4's large-scale Mixture of Experts (MoE) architecture make it suitable for broad NLP tasks, large-context understanding, and large-scale language comprehension. As for education-and-self-development, both models serve distinct purposes: Kimi K2's open-source nature and detailed benchmark results make it a valuable resource for technical AI applications and academic research, while Llama 4's expansive architecture and focus on NLP tasks can contribute unique insights into the expansive domain of language understanding.
