feat(data): improved model usage section

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Adam 2026-07-06 13:23:53 -05:00
parent 203885d241
commit c3b9c9d888
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3 changed files with 554 additions and 3 deletions

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@ -210,7 +210,7 @@ const en = {
"model.totalModels": "{{count}} models",
"model.momentum": "Momentum",
"model.vsPreviousWindow": "vs previous window",
"model.usageDescription": "Daily OpenCode Go token volume over the recent two-month window.",
"model.usageDescription": "Daily token volume.",
"model.noUsageTitle": "No usage",
"model.noUsageDescription": "No usage landed in the current window.",
"model.dailyTokenChart": "Daily token usage chart",

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@ -15,7 +15,7 @@ import {
type UsageRange,
} from "@opencode-ai/stats-core/domain/home"
import { createAsync, query, useParams } from "@solidjs/router"
import { createMemo, createSignal, For, onMount, Show, type JSX } from "solid-js"
import { createMemo, createSignal, createUniqueId, For, onMount, Show, type JSX } from "solid-js"
import { getRequestEvent } from "solid-js/web"
import type { FeatureCollection, GeometryObject, GeoJsonProperties } from "geojson"
import type { GeometryCollection, Topology } from "topojson-specification"
@ -121,6 +121,7 @@ export default function StatsModel() {
const modelHeaderLinks = createMemo<readonly HeaderLink[]>(() => [
{ href: "#overview", label: i18n.t("nav.overview") },
{ href: "#momentum", label: i18n.t("model.momentum") },
{ href: "#usage", label: i18n.t("nav.usage") },
{ href: "#efficiency", label: i18n.t("nav.efficiency") },
{ href: "#geo-breakdown", label: i18n.t("nav.geoBreakdown") },
{ href: "#peers", label: i18n.t("nav.peers") },
@ -183,6 +184,7 @@ export default function StatsModel() {
/>
<ModelOverview catalog={catalogEntry() ?? null} />
<ModelMomentumSection data={stats() ?? null} />
<ModelUsageSection data={stats() ?? null} />
<ModelEfficiencySection data={stats() ?? null} catalog={catalogEntry() ?? null} />
<ModelGeoBreakdownSection data={stats()?.country ?? emptyCountryRecord()} />
<ModelPeersSection data={stats() ?? null} />
@ -561,6 +563,211 @@ function MomentumMetric(props: { label: string; value: string; watermark?: strin
)
}
function ModelUsageSection(props: { data: StatsModelData | null }) {
const i18n = useI18n()
const activeLineClipId = createUniqueId()
const activeLineMaskId = createUniqueId()
const [activeIndex, setActiveIndex] = createSignal<number>()
const usage = createMemo(() => props.data?.usage ?? [])
const tokenMax = createMemo(() => Math.max(0, ...usage().map((item) => item.tokens)) || 1)
const linePoints = createMemo(() =>
usage().map((point, index) => ({
point,
x: modelUsagePointX(index, usage().length),
y: modelUsageLineY(point.tokens, tokenMax()),
})),
)
const tokenLinePath = createMemo(() => modelUsageLinePath(linePoints()))
const activeLineBreak = createMemo(() => {
const index = activeIndex()
if (index === undefined || linePoints().length < 2) return undefined
return modelUsageColumnBounds(index, linePoints().length)
})
const activeLineClip = createMemo(() => {
const index = activeIndex()
if (index === undefined || linePoints().length < 2) return undefined
return modelUsageColumnInnerBounds(index, linePoints().length)
})
const activeTooltip = createMemo(() => {
const index = activeIndex()
const point = index === undefined ? undefined : usage()[index]
if (index === undefined || !point) return undefined
const bounds = modelUsageColumnBounds(index, usage().length)
return {
bounds,
index,
point,
tokenY: linePoints()[index]?.y ?? 100,
tooltipY: ((linePoints()[index]?.y ?? 100) * 370) / 450,
}
})
const monthTicks = createMemo(() => modelUsageMonthTicks(usage(), props.data?.updatedAt ?? null))
return (
<section id="usage" data-section="model-panel">
<SectionTitle href="#usage" title={i18n.t("nav.usage")} description={i18n.t("model.usageDescription")} />
<Show
when={usage().some((item) => item.tokens > 0)}
fallback={
<ModelEmptyState title={i18n.t("model.noUsageTitle")} description={i18n.t("model.noUsageDescription")} />
}
>
<div
data-component="model-usage-chart"
data-variant="model-trend"
role="img"
aria-label={i18n.t("model.dailyTokenChart")}
style={{ "--model-usage-count": usage().length } as JSX.CSSProperties}
onPointerLeave={(event) => {
if (event.pointerType === "touch") return
setActiveIndex(undefined)
}}
>
<div data-slot="model-trend-plot">
<Show when={tokenLinePath()}>
{(path) => (
<>
<svg
data-slot="model-trend-line"
data-layer="base"
viewBox="0 0 100 100"
preserveAspectRatio="none"
aria-hidden="true"
>
<Show when={activeLineBreak()} fallback={<path data-slot="model-trend-line-base" d={path()} />}>
{(lineBreak) => (
<>
<defs>
<mask id={activeLineMaskId} maskUnits="userSpaceOnUse">
<rect x="0" y="-2" width="100" height="104" fill="white" />
<rect x={lineBreak().x} y="-2" width={lineBreak().width} height="104" fill="black" />
</mask>
</defs>
<path data-slot="model-trend-line-base" d={path()} mask={`url(#${activeLineMaskId})`} />
</>
)}
</Show>
</svg>
<Show when={activeLineClip()}>
{(clip) => (
<svg
data-slot="model-trend-line"
data-layer="active"
viewBox="0 0 100 100"
preserveAspectRatio="none"
aria-hidden="true"
>
<defs>
<clipPath id={activeLineClipId} clipPathUnits="userSpaceOnUse">
<rect x={clip().x} y="-2" width={clip().width} height="104" />
</clipPath>
</defs>
<path data-slot="model-trend-line-active" d={path()} clip-path={`url(#${activeLineClipId})`} />
</svg>
)}
</Show>
</>
)}
</Show>
<Show when={linePoints().at(-1)}>
{(point) => (
<span
data-slot="model-trend-end-marker"
aria-hidden="true"
style={
{
"--model-trend-end-x": `${point().x}%`,
"--model-trend-end-top": `${(point().y * 370) / 450}%`,
} as JSX.CSSProperties
}
/>
)}
</Show>
<div data-slot="model-trend-bars">
<For each={usage()}>
{(point, index) => (
<div
data-slot="model-trend-column"
role="button"
tabIndex={0}
aria-label={`${point.date} ${formatTokens(point.tokens)} ${i18n.t("lab.tokens")}`}
data-active={activeIndex() === index() ? "true" : undefined}
data-muted={activeIndex() !== undefined && activeIndex() !== index() ? "true" : undefined}
style={
{
"--model-trend-token-height": `${modelUsageStripHeight(point.tokens, tokenMax())}px`,
} as JSX.CSSProperties
}
onPointerDown={(event) => {
if (event.pointerType !== "touch") return
setActiveIndex(index())
}}
onPointerEnter={() => setActiveIndex(index())}
onPointerMove={(event) => {
if (event.pointerType === "touch") return
setActiveIndex(index())
}}
onClick={() => setActiveIndex(index())}
onFocus={() => setActiveIndex(index())}
onBlur={() => setActiveIndex(undefined)}
onKeyDown={(event) => {
if (event.key !== "Enter" && event.key !== " ") return
event.preventDefault()
setActiveIndex(index())
}}
>
<div data-slot="model-trend-token-bar" />
</div>
)}
</For>
</div>
<Show when={activeTooltip()} keyed>
{(active) => (
<div
data-component="chart-tooltip"
data-placement={active.index > usage().length * 0.62 ? "left" : "right"}
style={
{
"--model-trend-tooltip-left": `${active.bounds.x}%`,
"--model-trend-tooltip-right": `${active.bounds.x + active.bounds.width}%`,
"--model-trend-token-y": `${active.tokenY}`,
"--model-trend-tooltip-y": `${active.tooltipY}`,
} as JSX.CSSProperties
}
>
<strong>{formatModelUsageTooltipDate(active.point.date, props.data?.updatedAt ?? null)}</strong>
<span>
{formatTokens(active.point.tokens)} {i18n.t("lab.tokens")}
</span>
<div data-slot="tooltip-divider" />
<p>
<span data-slot="tooltip-label">
<i data-kind="tokens" /> {i18n.t("lab.dailyTokens")}
</span>
<b>{formatTokens(active.point.tokens)}</b>
</p>
</div>
)}
</Show>
</div>
<div data-slot="model-trend-months" aria-hidden="true">
<For each={monthTicks()}>
{(tick) => (
<span
data-align={tick.align}
style={{ "--model-trend-month-left": `${tick.left}%` } as JSX.CSSProperties}
>
{tick.label}
</span>
)}
</For>
</div>
</div>
</Show>
</section>
)
}
function ModelEfficiencySection(props: { data: StatsModelData | null; catalog: ModelCatalogEntry | null }) {
const i18n = useI18n()
return (
@ -1014,6 +1221,93 @@ function formatMomentumDateLabel(date: string) {
return `${shortMonths[parsed.getUTCMonth()]} ${parsed.getUTCDate()}`
}
function modelUsageStripHeight(value: number, max: number) {
if (value <= 0 || max <= 0) return 0
return Math.max(2, (value / max) * 40)
}
function modelUsageLineY(value: number, max: number) {
if (value <= 0 || max <= 0) return 100
return Math.max(0, 100 - (value / max) * 100)
}
function modelUsagePointX(index: number, count: number) {
if (count <= 1) return 50
return ((index + 0.5) / count) * 100
}
function modelUsageColumnBounds(index: number, count: number) {
if (count <= 0) return { x: 0, width: 100 }
return { x: (index / count) * 100, width: 100 / count }
}
function modelUsageColumnInnerBounds(index: number, count: number) {
const bounds = modelUsageColumnBounds(index, count)
const inset = Math.min(bounds.width * 0.1, 0.24)
return { x: bounds.x + inset, width: Math.max(0.001, bounds.width - inset * 2) }
}
type ModelUsageLinePoint = { x: number; y: number }
function modelUsageLinePath(points: ModelUsageLinePoint[]) {
if (points.length === 0) return ""
if (points.length === 1) return `M ${formatUsagePathNumber(points[0].x)} ${formatUsagePathNumber(points[0].y)}`
return points.slice(0, -1).reduce(
(path, point, index) => {
const next = points[index + 1]
const previous = points[index - 1] ?? point
const afterNext = points[index + 2] ?? next
const controlA = {
x: point.x + (next.x - previous.x) / 6,
y: clampUsagePercent(point.y + (next.y - previous.y) / 6),
}
const controlB = {
x: next.x - (afterNext.x - point.x) / 6,
y: clampUsagePercent(next.y - (afterNext.y - point.y) / 6),
}
return `${path} C ${formatUsagePathNumber(controlA.x)} ${formatUsagePathNumber(controlA.y)}, ${formatUsagePathNumber(controlB.x)} ${formatUsagePathNumber(controlB.y)}, ${formatUsagePathNumber(next.x)} ${formatUsagePathNumber(next.y)}`
},
`M ${formatUsagePathNumber(points[0].x)} ${formatUsagePathNumber(points[0].y)}`,
)
}
function formatUsagePathNumber(value: number) {
return Number(value.toFixed(3))
}
function clampUsagePercent(value: number) {
return Math.max(0, Math.min(100, value))
}
function modelUsageMonthTicks(points: ModelUsagePoint[], updatedAt: string | null) {
const seen = new Set<string>()
return points.flatMap((point, index) => {
const parsed = parseMomentumDate(point.date, updatedAt)
const label = shortMonths[parsed.getUTCMonth()]
if (!label || seen.has(label)) return []
seen.add(label)
return [
{
label,
left: modelUsagePointX(index, points.length),
align: index === 0 ? "start" : index >= points.length - 2 ? "end" : "center",
},
]
})
}
function formatModelUsageTooltipDate(value: string, updatedAt: string | null) {
const date = parseMomentumDate(value, updatedAt)
if (date.getUTCFullYear() === 1970) return value
return new Intl.DateTimeFormat("en", {
month: "short",
day: "numeric",
year: "numeric",
timeZone: "UTC",
}).format(date)
}
function formatRankLabel(rank: number | null) {
if (rank === null) return "--"
return `#${String(rank).padStart(2, "0")}`

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@ -4871,6 +4871,200 @@
background: var(--lab-usage-line);
}
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] {
--model-trend-gap: 4px;
--model-trend-column-gutter: calc(var(--model-trend-gap) / 2);
--model-trend-line-bottom: 80px;
--model-trend-bar: #dbdbdb;
--model-trend-line: #808080;
--model-trend-active: #3b5cf6;
--model-trend-band: #f2f2f2;
grid-template-rows: minmax(0, 450px) 14px;
gap: 40px;
height: 504px;
}
[data-page="stats"] [data-slot="model-trend-plot"] {
position: relative;
min-width: 0;
min-height: 0;
overflow: visible;
}
[data-page="stats"] [data-slot="model-trend-plot"]::after {
position: absolute;
inset: 0;
z-index: 0;
background: linear-gradient(to bottom, transparent 42%, color-mix(in srgb, var(--stats-text) 3%, transparent));
content: "";
pointer-events: none;
}
[data-page="stats"] [data-slot="model-trend-line"] {
position: absolute;
top: 0;
right: 0;
bottom: auto;
left: 0;
display: block;
width: 100%;
height: calc(100% - var(--model-trend-line-bottom));
overflow: visible;
pointer-events: none;
}
[data-page="stats"] [data-slot="model-trend-line"][data-layer="base"] {
z-index: 2;
}
[data-page="stats"] [data-slot="model-trend-line"][data-layer="active"] {
z-index: 4;
}
[data-page="stats"] [data-slot="model-trend-line"] path {
fill: none;
stroke-linecap: round;
stroke-linejoin: round;
stroke-width: 1.5;
vector-effect: non-scaling-stroke;
}
[data-page="stats"] [data-slot="model-trend-line-base"] {
stroke: var(--model-trend-line);
}
[data-page="stats"] [data-slot="model-trend-line-active"] {
stroke: var(--model-trend-active);
}
[data-page="stats"] [data-slot="model-trend-end-marker"] {
position: absolute;
top: var(--model-trend-end-top);
left: var(--model-trend-end-x);
z-index: 3;
width: 6px;
height: 6px;
background: var(--model-trend-bar);
transform: translate(-50%, -50%);
pointer-events: none;
}
[data-page="stats"] [data-slot="model-trend-bars"] {
position: absolute;
inset: 0;
z-index: 3;
display: flex;
gap: 0;
min-width: 0;
}
[data-page="stats"] [data-slot="model-trend-column"] {
position: relative;
box-sizing: border-box;
flex: 1 1 0;
min-width: 0;
height: 100%;
outline: none;
cursor: pointer;
}
[data-page="stats"] [data-slot="model-trend-column"]::before {
position: absolute;
inset: 0;
z-index: 1;
background: transparent;
content: "";
pointer-events: none;
transition: background 120ms ease;
}
[data-page="stats"] [data-slot="model-trend-column"][data-active="true"]::before,
[data-page="stats"] [data-slot="model-trend-column"]:focus-visible::before {
background: var(--model-trend-band);
box-shadow:
inset var(--model-trend-column-gutter) 0 0 var(--stats-bg),
inset calc(var(--model-trend-column-gutter) * -1) 0 0 var(--stats-bg);
}
[data-page="stats"] [data-slot="model-trend-token-bar"] {
position: absolute;
right: var(--model-trend-column-gutter);
bottom: 0;
left: var(--model-trend-column-gutter);
z-index: 2;
height: var(--model-trend-token-height);
background: var(--model-trend-bar);
transition:
background 120ms ease,
opacity 120ms ease;
}
[data-page="stats"] [data-slot="model-trend-column"][data-active="true"] [data-slot="model-trend-token-bar"] {
background: var(--model-trend-active);
}
[data-page="stats"] [data-slot="model-trend-column"][data-muted="true"] [data-slot="model-trend-token-bar"] {
opacity: 0.64;
}
[data-page="stats"] [data-slot="model-trend-months"] {
position: relative;
height: 14px;
color: var(--stats-muted);
font-size: 11px;
font-weight: 500;
line-height: 14px;
}
[data-page="stats"] [data-slot="model-trend-months"] span {
position: absolute;
top: 0;
left: var(--model-trend-month-left);
transform: translateX(-50%);
}
[data-page="stats"] [data-slot="model-trend-months"] span[data-align="start"] {
transform: none;
}
[data-page="stats"] [data-slot="model-trend-months"] span[data-align="end"] {
transform: translateX(-100%);
}
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] [data-component="chart-tooltip"] {
z-index: 6;
top: clamp(12px, calc(var(--model-trend-tooltip-y) * 1% - 46px), calc(100% - 108px));
width: 192px;
min-width: 192px;
background: var(--stats-bg);
}
[data-page="stats"]
[data-component="model-usage-chart"][data-variant="model-trend"]
[data-component="chart-tooltip"][data-placement="right"] {
right: auto;
left: calc(var(--model-trend-tooltip-right) + 8px);
}
[data-page="stats"]
[data-component="model-usage-chart"][data-variant="model-trend"]
[data-component="chart-tooltip"][data-placement="left"] {
right: calc(100% - var(--model-trend-tooltip-left) + 8px);
left: auto;
}
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] [data-component="chart-tooltip"] p {
height: 16px;
margin: 8px 0;
}
[data-page="stats"]
[data-component="model-usage-chart"][data-variant="model-trend"]
[data-component="chart-tooltip"]
i[data-kind="tokens"] {
background: var(--model-trend-active);
}
[data-page="stats"] [data-component="model-peer-list"] {
display: grid;
gap: 8px;
@ -4966,6 +5160,15 @@
--lab-usage-band: #242424;
}
[data-page="stats"][data-theme="dark"] [data-component="model-usage-chart"][data-variant="model-trend"],
:root[data-stats-theme="dark"]
[data-page="stats"]:not([data-theme="light"])
[data-component="model-usage-chart"][data-variant="model-trend"] {
--model-trend-bar: #3a3a3a;
--model-trend-line: #a3a3a3;
--model-trend-band: #242424;
}
[data-page="stats"][data-theme="dark"] [data-component="chart-tooltip"],
:root[data-stats-theme="dark"] [data-page="stats"]:not([data-theme="light"]) [data-component="chart-tooltip"] {
background: #242424f2;
@ -5131,6 +5334,14 @@
--lab-usage-band: #242424;
}
:root:not([data-stats-theme="light"])
[data-page="stats"]:not([data-theme="light"])
[data-component="model-usage-chart"][data-variant="model-trend"] {
--model-trend-bar: #3a3a3a;
--model-trend-line: #a3a3a3;
--model-trend-band: #242424;
}
:root:not([data-stats-theme="light"]) [data-page="stats"]:not([data-theme="light"]) [data-component="chart-tooltip"] {
background: #242424f2;
}
@ -5229,15 +5440,27 @@
[data-page="stats"][data-theme="dark"]
[data-component="model-usage-chart"][data-variant="lab-usage"]
[data-component="chart-tooltip"],
[data-page="stats"][data-theme="dark"]
[data-component="model-usage-chart"][data-variant="model-trend"]
[data-component="chart-tooltip"],
:root[data-stats-theme="dark"]
[data-page="stats"]:not([data-theme="light"])
[data-component="model-usage-chart"][data-variant="lab-usage"]
[data-component="chart-tooltip"],
:root[data-stats-theme="dark"]
[data-page="stats"]:not([data-theme="light"])
[data-component="model-usage-chart"][data-variant="model-trend"]
[data-component="chart-tooltip"],
:root:not([data-stats-theme="light"])
[data-page="stats"]:not([data-theme="light"])
[data-component="model-usage-chart"][data-variant="lab-usage"]
[data-component="chart-tooltip"],
[data-page="stats"] [data-component="model-usage-chart"][data-variant="lab-usage"] [data-component="chart-tooltip"] {
:root:not([data-stats-theme="light"])
[data-page="stats"]:not([data-theme="light"])
[data-component="model-usage-chart"][data-variant="model-trend"]
[data-component="chart-tooltip"],
[data-page="stats"] [data-component="model-usage-chart"][data-variant="lab-usage"] [data-component="chart-tooltip"],
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] [data-component="chart-tooltip"] {
background: var(--stats-bg);
}
@ -5514,6 +5737,11 @@
margin-top: 16px;
}
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] [data-component="chart-tooltip"] {
position: absolute;
margin-top: 0;
}
[data-page="stats"] [data-component="footer"] {
padding: 88px 24px 24px;
}
@ -5935,6 +6163,35 @@
min-width: 0;
}
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] {
--model-usage-mobile-bar-width: 20px;
--model-usage-mobile-edge-space: 48px;
--model-usage-mobile-track-width: calc(
var(--model-usage-count) * var(--model-usage-mobile-bar-width) + var(--model-usage-mobile-edge-space)
);
--model-trend-line-bottom: 104px;
grid-template-rows: minmax(0, 360px) 14px;
gap: 24px;
height: 398px;
}
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] [data-slot="model-trend-plot"],
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] [data-slot="model-trend-months"] {
direction: ltr;
width: var(--model-usage-mobile-track-width);
min-width: var(--model-usage-mobile-track-width);
}
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] [data-slot="model-trend-column"] {
flex: 0 0 var(--model-usage-mobile-bar-width);
}
[data-page="stats"] [data-component="model-usage-chart"][data-variant="model-trend"] [data-component="chart-tooltip"] {
top: auto;
width: auto;
min-width: 0;
}
[data-page="stats"] [data-component="model-peer-list"] a {
grid-template-columns: 30px minmax(0, 1fr) minmax(64px, auto);
}