Câți parametri are GPT-5?

library(tidyverse)
library(rvest)
library(knitr)

html <- read_html("aaai-table")
table <- html_table(html)((1))
headers <- colnames(table)
subheaders <- table(1, )
joint_headers <- map2(
  headers,
  subheaders,
  ~ if_else(
    .x == "",
    .y,
    sprintf("%s::%s", .x, .y)
  )
)
colnames(table) <- joint_headers
table <- table(2:nrow(table), )

read_percent <- function(val) {
  val |> str_replace("%", "") |> as.integer()
}
read_context <- function(val) {
  val |> str_replace("k", "000") |> as.integer()
}

scoreboard <- table |>
  mutate(
    across(starts_with("Intelligence"), read_percent),
    ContextWindow = read_context(ContextWindow),
    # TODO not quite good enough
    Reasoning = str_detect(
      Model,
      "Reasoning|Thinking|high|medium|low|o3|o1|Magistral"
    ),
    Reasoning = factor(
      if_else(Reasoning, "Reasoning", "Standard"),
      levels = c("Standard", "Reasoning")
    ),
    Family = case_when(
      str_detect(Model, "Gemma") ~ "Gemma",
      str_detect(Model, "Gemini") ~ "Gemma",
      str_detect(Model, "Command-R") ~ "Command-R",
      str_detect(Model, "DeepSeek") ~ "DeepSeek",
      str_detect(Model, "Claude") ~ "Claude",
      str_detect(Model, "GPT") ~ "GPT",
      str_detect(Model, "gpt") ~ "GPT",
      str_detect(Model, "Grok") ~ "Grok",
      str_detect(Model, "Granite") ~ "Granite",
      str_detect(Model, "Phi") ~ "Phi",
      str_detect(Model, "Ministral") ~ "Ministral",
      str_detect(Model, "Mistral") ~ "Mistral",
      str_detect(Model, "Mixtral") ~ "Mixtral",
      str_detect(Model, "EXAONE") ~ "EXAONE",
      str_detect(Model, "Aya") ~ "Aya",
      str_detect(Model, "Qwen") ~ "Qwen",
      str_detect(Model, "QwQ") ~ "QwQ",
      str_detect(Model, "LFM") ~ "LFM",
      str_detect(Model, "Llama") ~ "Llama",
      .default = Model
    ),
    Family = factor(Family),
    Parameters = as.integer(str_extract(
      Model,
      "((0-9)+)(BM)",
      group = 1
    )),
    License = `Features::License`
  ) |>
  select(
    -starts_with("Intelligence::Arti"),
  ) |>
  select(
    Family,
    Model,
    License,
    ContextWindow,
    Parameters,
    Reasoning,
    starts_with("Intelligence")
  ) |>
  rename_with(
    (x) str_replace(x, "\s+\(.*\)", ""),
    .cols = starts_with("Intelligence::")
  )

scoreboard |>
  head() |>
  kable()

ggplot(
  scoreboard |>
    filter(!is.na(Parameters)) |>
    pivot_longer(
      starts_with("Intelligence::"),
      names_to = "Benchmark",
      values_to = "Score"
    ) |>
    mutate(Benchmark = str_replace(Benchmark, "Intelligence::", ""))
) +
  aes(
    x = Parameters,
    y = Score,
    colour = Family
  ) +
  facet_grid(
    rows = vars(Benchmark),
    cols = vars(Reasoning),
    scales = "free_y"
  ) +
  scale_x_log10() +
  geom_smooth(
    method = "lm",
    formula = y ~ x,
    se = FALSE,
    inherit.aes = FALSE,
    mapping = aes(
      x = Parameters,
      y = Score,
    )
  ) +
  geom_point() +
  labs(
    title = "Benchmark scores of open source LLMs",
    x = "Size (billions of parameters)",
    y = "Benchmark performance"
  ) +
  theme_minimal()

reg <- lm(
  I(log10(Parameters)) ~
    Family +
      Reasoning +
      `Intelligence::MMLU-Pro` +
      `Intelligence::GPQA Diamond` +
      `Intelligence::Humanity's Last Exam` +
      `Intelligence::LiveCodeBench` +
      `Intelligence::SciCode` +
      `Intelligence::IFBench` +
      `Intelligence::AIME 2025` +
      `Intelligence::AA-LCR`,
  # These help the model, but there's no GPT family scores so I've had to remove them.
  #`Intelligence::AIME 2024` +
  #`Intelligence::Math 500` +
  #`Intelligence::HumanEval`,
  data = scoreboard |> filter(!is.na(Parameters))
)

summary(reg)

hist(resid(reg))

#|
GPTs <- scoreboard |> filter(Family == "GPT")
gpt_preds <- predict(reg, newdata = GPTs)
gpt_family_params <- tibble(
  Model = GPTs$Model,
  Parameters = 10^gpt_preds,
) |>
  drop_na() |>
  arrange(desc(Parameters))

kable(gpt_family_params, digits = 0)