"use client" import { Fragment, useMemo } from "react" import Link from "next/link" import { ChevronDown, ExternalLink, FlaskConical, ShieldCheck, TriangleAlert, } from "lucide-react" import type { BenchmarkEvaluationCardData } from "@/components/benchmark-evaluation-card" import { Badge } from "@/components/ui/badge" import { Button } from "@/components/ui/button" import { Collapsible, CollapsibleContent, CollapsibleTrigger, } from "@/components/ui/collapsible" import { Dialog, DialogContent, DialogDescription, DialogHeader, DialogTitle, } from "@/components/ui/dialog" import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from "@/components/ui/table" function formatDate(isoString: string) { const numeric = Number(isoString) const parsedDate = !Number.isNaN(numeric) && !isoString.includes("-") ? new Date(numeric * 1000) : new Date(isoString) try { return parsedDate.toLocaleDateString("en-US", { year: "numeric", month: "short", day: "numeric", }) } catch { return isoString } } function parseParamsBillionsFromModelName(modelName: string | null | undefined) { if (!modelName) return null // Parse explicit size tokens like 7B, 40B, or 560M from model names. const sizeTokens = Array.from(modelName.matchAll(/\b(\d+(?:\.\d+)?)\s*([bm])\b/gi)) if (sizeTokens.length === 0) return null const lastToken = sizeTokens[sizeTokens.length - 1] const numericValue = Number(lastToken[1]) if (!Number.isFinite(numericValue)) return null const unit = lastToken[2].toLowerCase() if (unit === "b") return numericValue if (unit === "m") return numericValue / 1000 return null } function formatParamsBillions(value: number | null | undefined, modelName?: string) { const resolvedValue = value == null || Number.isNaN(value) ? parseParamsBillionsFromModelName(modelName) : value if (resolvedValue == null || Number.isNaN(resolvedValue)) return "Not reported" if (resolvedValue >= 100) return `${Math.round(resolvedValue)}B` return `${resolvedValue.toFixed(1)}B` } function formatBenchmarkScore(score: number, unit?: string) { if (unit === "accuracy" || unit === "pass@1" || (!unit && score >= 0 && score <= 1)) { return `${(score * 100).toFixed(1)}` } if (unit === "points") { return score.toFixed(1) } return score.toFixed(2) } function getReproducibilityLabel(status: BenchmarkEvaluationCardData["reproducibility_status"]) { switch (status) { case "complete": return "Full config coverage" case "partial": return "Partial config coverage" default: return "Config mostly missing" } } function getIndependentLabel(model: BenchmarkEvaluationCardData) { if (model.independent_verification_ratio >= 0.75) return "Mostly third-party" if (model.independent_verification_ratio > 0) return "Mixed reporting" return "Self-reported only" } function getIndependentValue(model: BenchmarkEvaluationCardData) { return `${Math.round(model.independent_verification_ratio * 100)}% independent` } function getBenchmarkSection(name: string) { const value = name.toLowerCase() if ( value.includes("tau") || value.includes("swe-bench") || value.includes("browsecomp") || value.includes("agent") ) { return "Agentic" } if ( value.includes("aime") || value.includes("gpqa") || value.includes("hmmt") || value.includes("beyond aime") || value.includes("reason") ) { return "Reasoning" } if ( value.includes("math") || value.includes("mmlu") || value.includes("ifeval") || value.includes("arena") || value.includes("live code") || value.includes("humaneval") || value.includes("mbpp") || value.includes("code") ) { return "General" } return "Other" } const SECTION_ORDER = ["General", "Reasoning", "Agentic", "Other"] const CONTEXT_ROWS = [ { key: "developer", label: "Developer" }, { key: "params", label: "Parameter range" }, { key: "benchmarks", label: "Benchmark coverage" }, { key: "reporting", label: "Reporting orgs" }, { key: "independence", label: "Reporting posture" }, { key: "reproducibility", label: "Reproducibility" }, { key: "latest", label: "Latest report" }, { key: "updated", label: "Updated" }, ] as const interface ModelCompareDialogProps { models: BenchmarkEvaluationCardData[] open: boolean onOpenChange: (open: boolean) => void } export function ModelCompareDialog({ models, open, onOpenChange, }: ModelCompareDialogProps) { const benchmarkRows = useMemo(() => { const rows = new Map< string, { benchmark: string metric: string section: string values: Record order: number } >() let order = 0 for (const model of models) { for (const score of model.top_scores) { const existing = rows.get(score.benchmark) if (!existing) { rows.set(score.benchmark, { benchmark: score.benchmark, metric: score.metric, section: getBenchmarkSection(score.benchmark), values: { [model.id]: { score: score.score, unit: score.unit, }, }, order, }) order += 1 continue } existing.values[model.id] = { score: score.score, unit: score.unit, } } } return Array.from(rows.values()).sort((a, b) => { const sectionDiff = SECTION_ORDER.indexOf(a.section) - SECTION_ORDER.indexOf(b.section) if (sectionDiff !== 0) { return sectionDiff } return a.order - b.order }) }, [models]) const benchmarkSections = useMemo(() => { return SECTION_ORDER.map((section) => ({ section, rows: benchmarkRows.filter((row) => row.section === section), })).filter((group) => group.rows.length > 0) }, [benchmarkRows]) return (
Side-By-Side Comparison
Compare Selected Models Start with the benchmark table. Use the context table only when you need reporting, reproducibility, or provenance detail.
Benchmark Comparison
Rows are drawn from the most relevant surfaced benchmarks across the selected models, closer to how release posts present comparison tables.
Benchmark {models.map((model) => (
{model.model_name}
{model.developer || "Unknown developer"}
{formatParamsBillions(model.params_billions, model.model_name)}
))}
{benchmarkSections.map((group) => ( {group.section} {group.rows.map((row) => { const rowValues = Object.values(row.values).map((value) => value.score) const maxScore = rowValues.length > 0 ? Math.max(...rowValues) : null return (
{row.benchmark}
{row.metric}
{models.map((model) => { const value = row.values[model.id] const isBest = value && maxScore != null && value.score === maxScore return ( {value ? (
{formatBenchmarkScore(value.score, value.unit)}
) : (
--
)}
) })}
) })}
))}
Signal {models.map((model) => (
{model.model_name}
))}
{CONTEXT_ROWS.map((row) => ( {row.label} {models.map((model) => ( {row.key === "developer" ? model.developer || "Unknown developer" : null} {row.key === "params" ? formatParamsBillions(model.params_billions, model.model_name) : null} {row.key === "benchmarks" ? (
{model.benchmarks_count} covered benchmarks
{model.evaluations_count} reported result{model.evaluations_count !== 1 ? "s" : ""}
) : null} {row.key === "reporting" ? (
{model.evaluator_count} reporting org{model.evaluator_count !== 1 ? "s" : ""}
{model.evaluator_names.slice(0, 3).join(", ") || "Not named"}
) : null} {row.key === "independence" ? (
0 ? "secondary" : "outline"} className="font-medium" > {getIndependentLabel(model)}
{getIndependentValue(model)}
) : null} {row.key === "reproducibility" ? (
{model.reproducibility_status === "missing" ? ( ) : ( )} {getReproducibilityLabel(model.reproducibility_status)}
{model.missing_generation_config_count > 0 ? `${model.missing_generation_config_count} result${model.missing_generation_config_count !== 1 ? "s" : ""} without generation config` : "No missing generation config in current corpus"}
) : null} {row.key === "latest" ? (
{model.latest_source_name || "No named source"} {model.source_urls[0] ? ( ) : null}
) : null} {row.key === "updated" ? formatDate(model.latest_timestamp) : null}
))}
))}
) }