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LLM benchmark: GPT-4o and Claude 3.5 Sonnet lead MMLU, GPQA and MATH

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DataAIOpenAIAnthropicGoogleMetaArtificial Analysis

Relays Artificial Analysis benchmarking of leading LLMs on MMLU, GPQA and MATH, prompted by the Reflection Llama 3.1 70B controversy. GPT-4o and Claude 3.5 Sonnet top all three categories.

Key claims
GPT-4o and Claude 3.5 Sonnet are the best performing models across MMLU, GPQA and MATH
๐”๐ฌ๐ข๐ง๐  ๐€๐ˆ ๐œ๐š๐ง ๐Ÿ๐ŸŽ๐ฑ ๐ฒ๐จ๐ฎ๐ซ ๐ฉ๐ซ๐จ๐๐ฎ๐œ๐ญ๐ข๐ฏ๐ข๐ญ๐ฒ. ๐Ÿค– โœจ The question is, which AI tool should you use? What it all comes down to, is the performance of the underlying LLM. LLM performance analysis featuring @OpenAI, @AnthropicAI, @Google, @Meta & co. ๐Ÿ“Š โคต๏ธ Due to the recent controversy around @OthersideAI's Reflection Llama 3.1 70B model performance (see link in comments), @ArtificialAnlys made an independent assessment of the performance of leading LLMs. The analysis is based on MMLU, GPQA, and MATH, which are all essential metrics for evaluating LLM performance across distinct reasoning and knowledge domains: โ˜ž ๐— ๐— ๐—Ÿ๐—จ (๐— ๐—ฎ๐˜€๐˜€๐—ถ๐˜ƒ๐—ฒ ๐— ๐˜‚๐—น๐˜๐—ถ๐˜๐—ฎ๐˜€๐—ธ ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐—จ๐—ป๐—ฑ๐—ฒ๐—ฟ๐˜€๐˜๐—ฎ๐—ป๐—ฑ๐—ถ๐—ป๐—ด): ๐– ๐—Œ๐—Œ๐–พ๐—Œ๐—Œ๐–พ๐—Œ ๐—€๐–พ๐—‡๐–พ๐—‹๐–บ๐—… ๐—‹๐–พ๐–บ๐—Œ๐—ˆ๐—‡๐—‚๐—‡๐—€ ๐–บ๐—‡๐–ฝ ๐–ฟ๐–บ๐–ผ๐—๐—Ž๐–บ๐—… ๐—„๐—‡๐—ˆ๐—๐—…๐–พ๐–ฝ๐—€๐–พ ๐–บ๐–ผ๐—‹๐—ˆ๐—Œ๐—Œ ๐Ÿง๐Ÿฉ ๐—Œ๐—Ž๐–ป๐—ƒ๐–พ๐–ผ๐—๐—Œ, ๐–ฟ๐—‹๐—ˆ๐—† ๐—๐—Ž๐—†๐–บ๐—‡๐—‚๐—๐—‚๐–พ๐—Œ ๐—๐—ˆ ๐—Œ๐–ผ๐—‚๐–พ๐—‡๐–ผ๐–พ๐—Œ. ๐–จ๐— ๐—†๐–พ๐–บ๐—Œ๐—Ž๐—‹๐–พ๐—Œ ๐—๐—ˆ๐— ๐—๐–พ๐—…๐—… ๐–บ ๐—†๐—ˆ๐–ฝ๐–พ๐—… ๐—๐–บ๐—‡๐–ฝ๐—…๐–พ๐—Œ ๐–ฝ๐—‚๐—๐–พ๐—‹๐—Œ๐–พ ๐–ฝ๐—ˆ๐—†๐–บ๐—‚๐—‡๐—Œ ๐–บ๐—‡๐–ฝ ๐–บ๐—‰๐—‰๐—…๐—‚๐–พ๐—Œ ๐—„๐—‡๐—ˆ๐—๐—…๐–พ๐–ฝ๐—€๐–พ. โ˜ž ๐—š๐—ฃ๐—ค๐—” (๐—š๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐—น-๐—ฃ๐˜‚๐—ฟ๐—ฝ๐—ผ๐˜€๐—ฒ ๐—ค๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป ๐—”๐—ป๐˜€๐˜„๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด): ๐–ค๐—๐–บ๐—…๐—Ž๐–บ๐—๐–พ๐—Œ ๐—Œ๐–ผ๐—‚๐–พ๐—‡๐—๐—‚๐–ฟ๐—‚๐–ผ ๐—‹๐–พ๐–บ๐—Œ๐—ˆ๐—‡๐—‚๐—‡๐—€ ๐–บ๐—‡๐–ฝ ๐—๐–พ๐–ผ๐—๐—‡๐—‚๐–ผ๐–บ๐—… ๐—„๐—‡๐—ˆ๐—๐—…๐–พ๐–ฝ๐—€๐–พ. ๐–จ๐— ๐—๐–พ๐—Œ๐—๐—Œ ๐—๐—๐–พ ๐—†๐—ˆ๐–ฝ๐–พ๐—…โ€™๐—Œ ๐–บ๐–ป๐—‚๐—…๐—‚๐—๐—’ ๐—๐—ˆ ๐—‹๐–พ๐–บ๐—Œ๐—ˆ๐—‡ ๐—๐—๐—‹๐—ˆ๐—Ž๐—€๐— ๐—Œ๐—‰๐–พ๐–ผ๐—‚๐–บ๐—…๐—‚๐—“๐–พ๐–ฝ ๐–ฝ๐—ˆ๐—†๐–บ๐—‚๐—‡๐—Œ ๐—…๐—‚๐—„๐–พ ๐—Œ๐–ผ๐—‚๐–พ๐—‡๐–ผ๐–พ, ๐–พ๐—‡๐—€๐—‚๐—‡๐–พ๐–พ๐—‹๐—‚๐—‡๐—€, ๐–บ๐—‡๐–ฝ ๐–ฟ๐–บ๐–ผ๐—๐—Ž๐–บ๐—… ๐–บ๐–ผ๐–ผ๐—Ž๐—‹๐–บ๐–ผ๐—’. โ˜ž๐— ๐—”๐—ง๐—›: ๐–ฅ๐—ˆ๐–ผ๐—Ž๐—Œ๐–พ๐—Œ ๐—ˆ๐—‡ ๐—Š๐—Ž๐–บ๐—‡๐—๐—‚๐—๐–บ๐—๐—‚๐—๐–พ ๐—‹๐–พ๐–บ๐—Œ๐—ˆ๐—‡๐—‚๐—‡๐—€ ๐–บ๐—‡๐–ฝ ๐—†๐–บ๐—๐—๐–พ๐—†๐–บ๐—๐—‚๐–ผ๐–บ๐—… ๐—‰๐—‹๐—ˆ๐–ป๐—…๐–พ๐—†-๐—Œ๐—ˆ๐—…๐—๐—‚๐—‡๐—€, ๐–บ๐—Œ๐—Œ๐–พ๐—Œ๐—Œ๐—‚๐—‡๐—€ ๐–บ ๐—†๐—ˆ๐–ฝ๐–พ๐—…โ€™๐—Œ ๐–บ๐–ป๐—‚๐—…๐—‚๐—๐—’ ๐—๐—ˆ ๐—‰๐–พ๐—‹๐–ฟ๐—ˆ๐—‹๐—† ๐—…๐—ˆ๐—€๐—‚๐–ผ๐–บ๐—… ๐–ฝ๐–พ๐–ฝ๐—Ž๐–ผ๐—๐—‚๐—ˆ๐—‡๐—Œ ๐–บ๐—‡๐–ฝ ๐—‡๐—Ž๐—†๐–พ๐—‹๐—‚๐–ผ๐–บ๐—… ๐–ผ๐—ˆ๐—†๐—‰๐—Ž๐—๐–บ๐—๐—‚๐—ˆ๐—‡๐—Œ ๐—๐—‚๐—๐— ๐—‰๐—‹๐–พ๐–ผ๐—‚๐—Œ๐—‚๐—ˆ๐—‡. These metrics collectively provide a comprehensive overview of an LLM's ability to handle broad knowledge, specialized science, and complex quantitative tasks. The results show that @OpenAI's GPT-4o and @AnthropicAI's Claude 3.5 Sonnet are the best performing models across all three categories. A lot of money being spent on this btw. ๐Ÿ’ธ ๐Ÿ‘‡ x.com/zerokn0wledge_/status/1826089584133804150

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