Llm Leaderboard Rankings And Performance Comparison

This LLM leaderboard displays the latest public benchmark performance for SOTA model versions released after April 2024. The data comes from model providers as well as independently run evaluations by Vellum or the open-source community. We feature results from non-saturated benchmarks, excluding outdated benchmarks (e.g. MMLU). If you want to use these models in your agents, try Vellum. Helping leaders make confident, well-informed decisions with clear benchmarks across different LLMs.

Trusted, independent rankings of large language models across performance, red teaming, jailbreaking safety, and real-world usability. A comprehensive list of the best LLMs in the world, ranked by their performance, price, and features, updated daily. Comprehensive testing across 57 subjects including mathematics, history, law, and medicine to evaluate LLM knowledge breadth. Graduate-level expert knowledge evaluation designed to test advanced reasoning in specialized domains. Software engineering tests including code generation, debugging, and algorithm design to measure programming capabilities. Extended version of HumanEval with more complex programming challenges across multiple languages to test code quality.

Compare leading models by quality, cost, and performance metrics in one place. Real-time Klu.ai data powers this leaderboard for evaluating LLM providers, enabling selection of the optimal API and model for your needs. The latest version of the AI model has significantly improved dataset demand and speed, ensuring more efficient chat and code generation, even across multilingual contexts like German, Chinese, and Hindi. Google's open LLM repository provides benchmarks that developers can use to identify wrong categories, especially in meta-inspired tests and other benchmarking efforts. However, latency issues remain a concern for AI models, particularly when processing large context windows or running complex comparisons between models in cost-sensitive environments. With the growing demand for datasets in various languages such as Spanish, French, Italian, and Arabic, benchmarking the quality and breadth of models against other benchmarks is essential for ensuring accurate metadata handling.

The Klu Index Score evaluates frontier models on accuracy, evaluations, human preference, and performance. It combines these indicators into one score, making it easier to compare models. This score helps identify models that best balance quality, cost, and speed for specific applications. Powered by real-time Klu.ai data as of 1/8/2026, this LLM Leaderboard reveals key insights into use cases, performance, and quality. GPT-4 Turbo (0409) leads with a 100 Klu Index score. o1-preview excels in complex reasoning with a 99 Klu Index.

GPT-4 Omni (0807) is optimal for AI applications with a speed of 131 TPS. Claude 3.5 Sonnet is best for chat and vision tasks, achieving an 82.25% benchmark average. Gemini Pro 1.5 is noted for reward modeling with a 73.61% benchmark average, while Claude 3 Opus excels in creative content with a 77.35% benchmark average. Providing a clear, data-driven comparison of today's leading large language models. We present standardized benchmark results for top contenders like Meta's Llama 4 series, Alibaba's Qwen3, and the latest from DeepSeek, focusing on critical performance metrics that measure everything from coding ability to general knowledge. If your primary goal is coding and software development, the benchmark data suggests that Qwen3-235B-A22B is a top performer, scoring an impressive 69.5% on LiveCodeBench.

For tasks requiring strong general knowledge and reasoning, the Qwen3 models also lead, with Qwen3-235B-A22B achieving 80.6% on the MMLU Pro benchmark. However, if you are looking for a more balanced or efficient model, DeepSeek-R1-Distill-Llama-70B offers very competitive performance across the board (51.8% on LiveCodeBench, 71.2% on MMLU Pro) and may be less resource-intensive than the... We recommend using the table above to weigh the performance on the benchmarks that matter most to your project. If you are interested in the parameters used, here is the Github Readme.

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