Open Source Showdown: Kimi K2 versus Llama 4 - Which Performs Superior as a Model?
In the rapidly evolving world of language models, two standout contenders have emerged: Kimi K2 and Llama 4. Both models show impressive capabilities, but each excels in different areas.
Kimi K2, a 32B parameter model, has garnered attention for its versatility and higher performance, particularly in multi-domain reasoning, coding, tool use, and multilingual understanding. With a large context window of 130k tokens, Kimi K2 is well-equipped to handle complex tasks that require a deep understanding of long inputs.
On the other hand, Llama 4, available in three variants - Scout, Maverick, and Behemoth - boasts a wider range of language support compared to Kimi K2. Llama 4 Maverick, for instance, scores 67.7% in GPQA-Diamond, a test of advanced physics reasoning.
Performance Highlights
The following table summarises the key strengths of each model across various tasks and capabilities:
| Task/Capability | Kimi K2 Highlights | Llama 4 Highlights | Notes | |-------------------------|------------------------------------------------------|-----------------------------------------------|---------------------------------------------------------------| | Multimodality | No explicit data in sources; both are primarily LLMs | Not specifically evaluated for multimodality | Neither model's multimodal abilities are detailed in results | | Agentic Behavior | Strong agentic code repair (SWE-bench 65.8%) | Not explicitly benchmarked on agentic repair | Kimi K2 shows significantly better agentic code repair ability[3] | | Tool Use & API Planning | Tau2 Retail Avg@4 score 70.6 (competitive) | Not directly compared on tool use benchmarks | Kimi K2 shows strong tool use and API planning capabilities[3] | | Multilingual Capabilities | High MMLU scores (~87.8 to 92.7 on variants) | Llama 4 Maverick around 63.3% on MMLU variant | Kimi K2 has much stronger broad knowledge and multilingual reasoning[1][3][4] | | General Reasoning & Knowledge | MMLU 87.8 to 92.7; MMLU-Pro 69.2 to 74.4; GPQA-Diamond strong | Lower range ~63.3% accuracy (MMLU) | Kimi K2 demonstrates superior reasoning and broad knowledge[1][3][4] | | Mathematics & Coding | MATH score 70.2 to 97.4; GSM8k 92.1; Pass@1 coding 80.3-85.7 | Not matching Kimi K2's reported coding/math results | Kimi K2 clearly leads in math/coding benchmarks and complex reasoning[1][3] | | Cost | Around $1.07 per 1M tokens (blended); input $0.60, output $2.50 | Not detailed but generally slightly more costly for similar scale | Kimi K2 is cheaper per token but slower output speed (36.8 tps) [2] | | Speed & Latency | Slower than average (36.8 tokens/sec) but low latency (0.57s TTFT) | Latency ~7.7s reported (likely API/hosting difference) | Kimi K2 has lower initial latency; Llama 4 latency depends on deployment[2][4] | | Context Window | Very large context window (~130k tokens) | Smaller, not specified exactly | Kimi K2 supports unusually large context sizes for complex tasks[2] |
Cost and Flexibility
In terms of cost, Kimi K2 is more affordable, with a price of around $1.07 per 1M tokens (blended), compared to Llama 4, which is generally slightly more costly for similar scale. However, Kimi K2's slower output speed (36.8 tokens per second) should be considered.
Llama 4 is only available under a community license and may have regional restrictions, requiring higher infrastructure requirements due to its context size. This, coupled with its larger model size, can sometimes make it less flexible for self-hosted, production use cases.
Limitations
Both models struggle with image analysis and are unable to read complex images properly. In terms of multimodality, Kimi K2 excels while Llama 4 struggles.
These conclusions are based on independent benchmark results and pricing/performance analyses detailed in recent evaluations from July 2025[1][2][3][4].
In conclusion, Kimi K2 is a strong contender, offering a more versatile, cost-effective, and higher-performing open-source language model, particularly in multi-domain reasoning, coding, tool use, and multilingual understanding. Llama 4, on the other hand, boasts a wider range of language support and stronger performance in certain specific tasks, such as physics reasoning. Ultimately, the choice between the two depends on the specific needs and requirements of the user.
Data science, technology, and artificial-intelligence all play a crucial role in the development and evaluation of advanced language models like Kimi K2 and Llama 4. Kimi K2, built with a data science approach, showcases impressive performance in various areas, including multi-domain reasoning, coding, tool use, and multilingual understanding, enabled by its powerful technology and AI capabilities. On the other hand, Llama 4, also a product of advanced AI and technology, excels in specific tasks such as physics reasoning, demonstrating the diverse applications and potential of these cutting-edge research areas.
