Measured, not asserted.

The same 13 videos, implemented idiomatic-minimal in Clipkit, Remotion, and HyperFrames — every implementation validated, token-counted, and render-verified. Plus a 60-cell benchmark where real agents author from neutral briefs across three model tiers. Our ground rule: bias lives in choosing the battlegrounds, never in the measurement — numbers we don't like get published anyway.

The same video, three formats, counted.

Tokens are the unit AI systems pay in. Each brief below was implemented in all three tools; counts are tiktoken o200k_base. Clipkit is counted minified (what the MCP server and structured outputs emit); Remotion includes its registration file.

Every ratio above 1.00x is a cell Clipkit wins. There are no cells below 1.00x — and if there were, they would be in this table.

What happens when agents actually author.

60 cells: 5 briefs (kinetic type, data scene, brand sizzle, UI screencast, 30s launch spot) x 3 model tiers (Haiku 4.5, Sonnet 5, Opus 4.8) x isolated authoring agents per tool. Artifacts validated with each tool's own gate, rendered on the real pipelines, tokens pulled from the agents' transcripts.

The honest read: Remotion is slightly cheaper per attempt — and ships an invalid video 13% of the time, discovered only after you render. Validity is the product. Remotion's two failures compiled cleanly (tsc-green) and crashed at render time; a schema gate catches the same class before a frame is drawn.

The corpus is the proof. Open it.

Below is the actual benchmark corpus — every brief, all three implementations, the exact files behind the table above. Read them side by side, copy them out, or open the Clipkit source in the editor and watch it render live in your browser.

{
  "output_format": "mp4",
  "width": 1920,
  "height": 1080,
  "duration": 5,
  "frame_rate": 30,
  "background_color": "#0b0f14",
  "elements": [
    {
      "id": "headline",
      "type": "text",
      "layer": 3,
      "time": 0,
      "duration": 5,
      "text": "Ship videos faster",
      "font_family": "Inter",
      "font_weight": "800",
      "font_size": 96,
      "fill_color": "#f4f7fb",
      "x": 960,
      "y": 470,
      "x_anchor": "50%",
      "y_anchor": "50%",
      "animations": [
        {
          "type": "text-slide",
          "time": 0.3,
          "duration": 0.6
        }
      ]
    },
    {
      "id": "bar",
      "type": "shape",
      "shape": "rectangle",
      "layer": 2,
      "time": 0.8,
      "duration": 4.2,
      "x": 960,
      "y": 560,
      "x_anchor": "50%",
      "y_anchor": "50%",
      "width": 240,
      "height": 6,
      "fill_color": "#1ba6ff",
      "keyframe_animations": [
        {
          "property": "width",
          "keyframes": [
            {
              "time": 0,
              "value": 0
            },
            {
              "time": 0.5,
              "value": 240,
              "easing": "ease-out"
            }
          ]
        }
      ]
    },
    {
      "id": "subline",
      "type": "text",
      "layer": 1,
      "time": 1,
      "duration": 4,
      "text": "The video engine for AI agents",
      "font_family": "Inter",
      "font_weight": "500",
      "font_size": 40,
      "fill_color": "#9aa3b2",
      "x": 960,
      "y": 640,
      "x_anchor": "50%",
      "y_anchor": "50%",
      "animations": [
        {
          "type": "fade-in",
          "duration": 0.5
        }
      ]
    }
  ]
}

The full suite — corpus, runners, timing scripts, and methodology — is being published as an open-source repo so the numbers can be reproduced (and challenged) on your hardware. Machine-record template included; PRs adding briefs or engines are welcome once it lands.

The numbers that didn't favor us.

• "75% fewer tokens" — our real medians are 39% and 53%; we publish the distribution.
• "200+ fps browser export" as a blanket claim — that is Apple-Silicon-scoped (M4, n=3); the same export runs 4–5 fps on an Intel i9, and both rows are published.
• "No headless browsers" — all three tools drive one for local CLI renders; ours composites on the GPU instead of screenshotting the DOM, but the browser is there.
• Agent-lane token superiority — Remotion agents emit terser code per attempt; our agent-lane win is validity at cost parity, not tokens.

Conditions: Intel i9 MacBook Pro / M4 mini (both recorded), Node 22, remotion 4.0.498–499, hyperframes 0.6.97–0.7.68, @clipkit/* 1.0.1–1.3.0, tiktoken o200k_base. Full per-experiment conditions ship with the repo.