Label speech and image generation models in the registry

src/pages/models.astro

@@ -9,7 +9,7 @@ const base = import.meta.env.BASE_URL;
     <div class="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8 relative">
       <p class="text-xs font-semibold tracking-[0.2em] uppercase mb-3 text-blue-200">Model API</p>
       <h1 class="font-display text-4xl sm:text-5xl font-bold tracking-tight">Model Registry</h1>
-      <p class="mt-4 max-w-2xl text-lg text-blue-100">Open-weight models served with zero data retention. Compare context limits and token prices, then copy the exact API ID.</p>
+      <p class="mt-4 max-w-2xl text-lg text-blue-100">Open-weight models served with zero data retention. Compare capabilities, context limits, and token prices, then copy the exact API ID.</p>
       <div class="flex flex-wrap gap-x-6 gap-y-2 mt-6 text-sm font-semibold">
         <a class="underline underline-offset-4 decoration-blue-300 hover:decoration-white" href={`${base}models/guide/`}>How to read the registry</a>
         <a class="underline underline-offset-4 decoration-blue-300 hover:decoration-white" href={`${base}docs/api-keys/`}>Model API documentation</a>

src/scripts/model-availability.ts

@@ -42,6 +42,8 @@ type Offering = z.infer<typeof offeringSchema>;
 const capabilityNames = new Map([
   ['text-generation', 'Text generation'],
   ['automatic-speech-recognition', 'Speech recognition'],
+  ['text-to-speech', 'Speech generation'],
+  ['text-to-image', 'Image generation'],
   ['vision', 'Image input'],
   ['function-calling', 'Function calling'],
   ['structured-output', 'Structured output'],
@@ -91,6 +93,8 @@ const iconPaths = new Map([
   ['structured-output', 'M8 3H5v6l-2 3 2 3v6h3 M16 3h3v6l2 3-2 3v6h-3 M10 9h4 M10 15h4'],
   ['text-generation', 'M4 4h16v12H8l-4 4V4Z M8 8h8 M8 12h5'],
   ['automatic-speech-recognition', 'M9 5a3 3 0 0 1 6 0v7a3 3 0 0 1-6 0V5Z M5 10v2a7 7 0 0 0 14 0v-2 M12 19v3 M8 22h8'],
+  ['text-to-speech', 'M11 5 6 9H2v6h4l5 4V5Z M15.5 8.5a5 5 0 0 1 0 7 M19 5a10 10 0 0 1 0 14'],
+  ['text-to-image', 'M5 3h14a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2Z M11 9a2 2 0 1 1-4 0 2 2 0 0 1 4 0 M21 15l-5-5L5 21'],
   ['long-context', 'M6 3h9l4 4v14H6V3Z M14 3v5h5 M9 12h7 M9 16h7'],
   ['open-weights', 'M7 11V7a5 5 0 0 1 9-3 M5 11h14v10H5V11Z M12 15v3'],
 ]);
@@ -206,17 +210,24 @@ function apiIdentity(offering: Offering): HTMLElement {
   return api;
 }
 
+// Speech and image models are priced per audio minute or image tile, not per
+// token, and have no text context window to report.
+const nonTokenCapabilities = new Set(['automatic-speech-recognition', 'text-to-speech', 'text-to-image']);
+
 function offeringRow(offering: Offering): HTMLElement {
   const row = element('div', '', 'model-facts');
-  const specs = figure('Context limit', offering.context_length === null ? 'Not published' : `${integers.format(offering.context_length)} tokens`);
+  const nonToken = offering.capabilities.some(capability => nonTokenCapabilities.has(capability));
+  const specs = offering.context_length === null && nonToken
+    ? element('div', '', 'spec')
+    : figure('Context limit', offering.context_length === null ? 'Not published' : `${integers.format(offering.context_length)} tokens`);
   const capabilities = element('div', '', 'capabilities');
   for (const capability of offering.capabilities) capabilities.append(badge(capabilityNames.get(capability) ?? capability, capability));
   if (offering.capabilities.length === 0) capabilities.append(element('span', 'Capabilities not published', 'spec-value'));
   if (offering.context_length !== null && offering.context_length >= 100_000) capabilities.append(badge('Long context', 'long-context'));
 
   const prices = element('div', '', 'offering-prices');
   if (offering.pricing === undefined) {
-    prices.append(figure('USD / million tokens', 'Token prices not published'));
+    prices.append(nonToken ? figure('Pricing', 'Not priced per token') : figure('USD / million tokens', 'Token prices not published'));
   } else {
     const { input, output, basis } = offering.pricing;
     const prefix = basis === 'starting_at' ? 'from ' : '';

tests/models_page_browser.py

@@ -47,6 +47,7 @@ def pricing(input_price: float, output_price: float, basis: str = "standard") ->
     offering("same-name-a", name="Identical name"),
     offering("same-name-b", name="Identical name"),
     offering("image-route", name="Image generator", capabilities=["text-to-image"], pricing=pricing(0.00000001, 0.1)),
+    offering("flux-1-schnell", "workers-ai", name="FLUX.1 schnell", capabilities=["text-to-image"], context_length=None),
 ]}
 MODEL_COUNT = len(CATALOG["data"])
 
@@ -89,6 +90,10 @@ def test_canonical_models_and_copy(page: Page) -> None:
     expect(model_card(page, "deepseek-v3.2-speciale")).to_contain_text("Input from $0.04")
     expect(model_card(page, "image-route")).to_contain_text("$0.00000001")
     expect(model_card(page, "new-release-2099")).to_contain_text("Token prices not published")
+    flux = model_card(page, "flux-1-schnell")
+    expect(flux).to_contain_text("Image generation")
+    expect(flux).to_contain_text("Not priced per token")
+    expect(flux).not_to_contain_text("Context limit")
     summary = model_card(page, "gpt-oss-120b").locator(".model-summary")
     expect(summary).to_contain_text("116.8B")
     summary.get_by_text("Details and sources", exact=True).press("Enter")