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R119 · Floyd-Steinberg dithering a gradient

blind round 9/11 local models answered

Every answer below carries the name of the model that wrote it. Blind refers to the scoring: the review was done without knowing which model produced which answer — the published result names them.

2 of the 11 local models delivered nothing through our test harness in this round — for reasons that lie with our setup, not with the models. Those cards are marked DNF like any other, but they are not evidence about those models — the reason is noted on the card itself.

Show task
Create ONE self-contained HTML file (STRICT LIMIT: max 7000 characters in total — this is a budget on characters, not on lines, so removing line breaks does not help) that applies Floyd-Steinberg dithering on a <canvas>: generate a smooth greyscale image from a mathematical function that changes over time, then quantise it to a small palette while distributing each pixel's quantisation error to its neighbouring pixels with the classic 7/16, 3/16, 5/16, 1/16 weights. Draw the dithered result through ImageData. Show the palette size and the frame number. Smooth requestAnimationFrame animation, NO user input, starts immediately and keeps running (restart with a fresh random instance when finished). No external libraries, no comments needed. Output ONLY the HTML file content, nothing else.

The answers

Local models (Brain cluster) — identical prompt, shuffled order (the sequence is not a ranking)
qwen3:14bERRBLANKMINIFIEDFORMAT
Show code
```html
<!DOCTYPE html>
<html><head><title>Floyd-Steinberg Dithering</title><style>body{background:#000;margin:0;overflow:hidden}canvas{display:block}</style></head><body><canvas id="c"></canvas><script>const c=document.getElementById('c');const ctx=c.getContext('2d');c.width=512;c.height=512;let t=0;let p=[0, 128, 255];function loop(){t+=1;let imageData=ctx.getImageData(0,0,512,512);let data=imageData.data;let w=512;let h=512;for(let y=0;y<h;y++){for(let x=0;x<w;x++){let i=(y*w+x)*4;let v=Math.sin((x/w+t/50)%1) * 127 + Math.cos((y/h+t/30)%1) * 127;v=Math.abs(v);if(!data[i]){// First pass calculation (initial value) data[i]=v;data[i+1]=v;data[i+2]=v;data[i+3]=255;}else{let original=data[i]/255;let q=p[Math.round(original/127*2)]*255;let err=original-q/255;for(let dy=0;dy<=1;dy++){for(let dx=0;dx<2;dx++){let nx=x+dx*(dx>0?1:0);let ny=y+dy*(dy>0?1:0);if(nx<w&&ny<h){let ni=(ny*w+nx)*4;let weight=0;if(dx===1&&dy===0)weight=7/16;else if(dx===0&&dy===1)weight=3/16;else if(dx===1&&dy===1)weight=5/16;else if(dx===0&&dy===0)weight=1/16;data[ni+0]-=(err*Math.pow(2,weight));data[ni+1]-=(err*Math.pow(2,weight));data[ni+2]-=(err*Math.pow(2,weight));}}}let data[i]=q;data[i+1]=q;data[i+2]=q;data[i+3]=255;}}ctx.putImageData(imageData,0,0);document.querySelector('body').innerHTML=`<div style="position:fixed;top:10px;left:10px;color:#aaa;">Palette Size: ${p.length}, Frame: ${t}</div>`;requestAnimationFrame(loop);}loop();</script></body></html>
```
mistral-small:24bOKFORMAT
Show code
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Floyd-Steinberg Dithering</title>
<style>
body {margin: 0; overflow: hidden;}
canvas {display: block;}
div {position: absolute; top: 10px; left: 10px; color: white; font-size: 20px;}
</style>
</head>
<body>
<canvas id="canvas"></canvas><div id="info"></div>
<script>
function createImageData(canvas) {
const width = canvas.width;
const height = canvas.height;
return new ImageData(width, height);
}
function setPixel(data, x, y, value) {
data.data[(y * data.width + x) * 4] = value;
data.data[(y * data.width + x) * 4 + 1] = value;
data.data[(y * data.width + x) * 4 + 2] = value;
data.data[(y * data.width + x) * 4 + 3] = 255;
}
function getPixel(data, x, y) {
return data.data[(y * data.width + x) * 4];
}
function dither(imgData, paletteSize) {
const width = imgData.width;
const height = imgData.height;
for (let y = 0; y < height; y++) {
	for (let x = 0; x < width; x++) {
		const oldPixel = getPixel(imgData, x, y);
		const newPixel = Math.round(oldPixel / paletteSize) * paletteSize;
		setPixel(imgData, x, y, newPixel);
		const error = oldPixel - newPixel;
		if (x + 1 < width) setPixel(imgData, x + 1, y, getPixel(imgData, x + 1, y) + (error * 7 / 16));
		if (x - 1 >= 0 && y + 1 < height) setPixel(imgData, x - 1, y + 1, getPixel(imgData, x - 1, y + 1) + (error * 3 / 16));
		if (y + 1 < height) setPixel(imgData, x, y + 1, getPixel(imgData, x, y + 1) + (error * 5 / 16));
		if (x + 1 < width && y + 1 < height) setPixel(imgData, x + 1, y + 1, getPixel(imgData, x + 1, y + 1) + (error * 1 / 16));
	}
}
return imgData;
}
function drawCanvas(ctx, imgData, paletteSize, frame) {
ctx.putImageData(dither(imgData, paletteSize), 0, 0);
document.getElementById("info").textContent = `Palette Size: ${paletteSize} Frame: ${frame}`;
}
function animate(canvas, ctx, paletteSize, frame) {
const imgData = createImageData(canvas);
for (let y = 0; y < canvas.height; y++) {
	for (let x = 0; x < canvas.width; x++) {
		const value = Math.sin(x / 20 + frame / 10) * 127 + 128;
		setPixel(imgData, x, y, value);
	}
}
drawCanvas(ctx, imgData, paletteSize, frame);
frame++;
if (frame < 360) requestAnimationFrame(() => animate(canvas, ctx, Math.floor(Math.random() * 5) + 1, frame));
else canvas.width = 800; // Restart
}
const canvas = document.getElementById("canvas");
canvas.width = 800;
canvas.height = 600;
animate(canvas, canvas.getContext('2d'), Math.floor(Math.random() * 5) + 1, 0);
</script>
</body>
</html>
```
qwen3:8bharness
No answer reached us (ERR) — and the reason lies with our setup, not with the model: reasoning consumed the entire token budget and the answer stayed empty; the gateway rejects budgets above 3000 tokens Counted as a did-not-finish, but it is not evidence about this model.
deepseek-r1:14bERRBLANKFORMAT
Show code
```html
<!DOCTYPE html>
<html><body style="margin:0;overflow:hidden;">
<canvas id=c></canvas>
<script>
const c=document.getElementById('c'), ctx=c.getContext('2d');
const w=c.width=512,h=c.height=512;
let t=0;
const palette=[0,32,64,96,128,160,192,224];
function floydSteinberg() {
    const data=new Uint8ClampedArray(w*h);
    for(let y=0;y<h;y++) {
        for(let x=0;x<w;x++) {
            let i=y*w+x;
            // Generate grayscale value from sine wave
            let v=(Math.sin(x/w*4+2*Math.PI*t/10)+1)/2*255;
            let q=palette[Math.floor((v/(256/palette.length)))];
            data[i]=q;
            let e=v-q;
            // Distribute error to neighbors
            if(x<w-1) data[i+1]+=e*7/16;
            if x<w-1&&y<h-1 data[i+w+1]+=e*5/16;
            if y<h-1 data[i+w]+=e*3/16;
            if x>0&&y<h-1 data[i-w+1]+=e*1/16;
        }
    }
    const img=new ImageData(data,w,h);
    ctx.putImageData(img,0,0);
}
function animate() {
    t=(t+0.1)%25;
    floydSteinberg();
    ctx.fillStyle='rgba(0,0,0,0.05)';
    ctx.fillRect(0,0,w,h);
    ctx.fillStyle='white';
    ctx.font='8px monospace';
    ctx.fillText(`Palette: ${palette.length} colors | Frame: ${Math.floor(t*10)}`,10,20);
    requestAnimationFrame(animate);
}
animate();
</script></body></html>
```
gemma4:26bharness
No answer reached us (THIN) — and the reason lies with our setup, not with the model: reasoning consumed the entire token budget (3 min 28 s, zero characters of answer); larger budgets are refused by the gateway Counted as a did-not-finish, but it is not evidence about this model.
qwen3.5:9bOKFORMAT
Show code
```html
<!DOCTYPE html>
<html>
<head><title>Floyd-Steinberg Dithering</title></head>
<body><canvas id="c"></canvas><div id="s"></div><script>
const c = document.getElementById('c');
const s = document.getElementById('s');
let ctx = null;

// Constants for the simulation
const W = 256;
const H = 256;
const PALETTE_SIZE = 16; // Max brightness levels (0-15)

c.width = W;
c.height = H;
ctx = c.getContext('2d');

// Precompute the palette (simple linear ramp for greyscale)
let P = [];
for(let i=0; i<PALETTE_SIZE; i++) {
    P[i] = Math.floor(i * 16); // 0, 16, 32, ..., 240 (approximation of brightness levels)
}

// Global state variables
let frameCount = 0;
let imageData = null;
let data = new Uint8ClampedArray(W * H * 4);

function generateImageData() {
    imageData = ctx.getImageData(0, 0, W, H);
    data = imageData.data;
}

function drawFrame() {
    if (!ctx) return;

    let t = frameCount / 100; // Time factor for the math function

    // 1. Generate Source Data (Math Function: Sine wave gradient over time)
    const srcData = new Uint8ClampedArray(W * H);
    for (let y = 0; y < H; y++) {
        for (let x = 0; x < W; x++) {
            // Simple function that changes over time: sin((x+y)/5 + t)
            const val = Math.floor(127 * (Math.sin((x + y) / 10 + t) + 1) / 2);
            srcData[y * W + x] = val; // Greyscale intensity (0-255 range for calculation)
        }
    }

    // Reset the data buffer with source luminance values
    for(let i=0; i < W*H; i++) {
        data[i*4 + 0] = srcData[i]; // R
        data[i*4 + 1] = srcData[i]; // G
        data[i*4 + 2] = srcData[i]; // B
        data[i*4 + 3] = 255;       // A (Opaque)
    }

    let error = new Float32Array(W * H).fill(0);

    // 2. Floyd-Steinberg Dithering and Quantization
    for (let y = 0; y < H; y++) {
        for (let x = 0; x < W; x++) {
            const i = (y * W + x) * 4;

            // Current Luminance Value (Source value plus accumulated error)
            let val = data[i]; // R, G, B are currently identical sources
            val += error[y * W + x];

            // Clamp and Quantize: Find the closest palette entry index
            const clampedVal = Math.max(0, Math.min(255, val));
            let bestIndex = 0;
            let minDiff = Infinity;

            for(let p_idx=0; p_idx < PALETTE_SIZE; p_idx++) {
                // Using the precomputed palette values (scaled to 0-255)
                const paletteValue = P[p_idx] * Math.ceil(256 / PALETTE_SIZE); 
                const diff = Math.abs(clampedVal - paletteValue);
                if (diff < minDiff) {
                    minDiff = diff;
                    bestIndex = p_idx;
                }
            }

            // The quantized pixel value
            let qVal = P[bestIndex] * Math.ceil(256 / PALETTE_SIZE); 

            // Store the result in the data array (R=G=B)
            data[i + 0] = qVal;
            data[i + 1] = qVal;
            data[i + 2] = qVal;
            data[i + 3] = 255;

            // Calculate Quantization Error
            let errorValue = val - qVal;

            // Distribute Error (7/16, 3/16, 5/16, 1/16)
            const weights = [7 / 16, 3 / 16, 5 / 16, 1 / 16];
            let errorIndex = y * W + x;

            // Right (x+1)
            if (x + 1 < W) {
                error[errorIndex + 1] += errorValue * weights[0];
            }
            // Down-Right (y+1, x+1)
            if (y + 1 < H && x + 1 < W) {
                error[(y + 1) * W + (x + 1)] += errorValue * weights[1];
            }
            // Down (y+1)
            if (y + 1 < H) {
                error[(y + 1) * W + x] += errorValue * weights[2];
            }
            // Down-Left (y+1, x-1)
            if (y + 1 < H && x - 1 >= 0) {
                error[(y + 1) * W + (x - 1)] += errorValue * weights[3];
            }
        }
    }

    // 3. Draw the result to canvas
    ctx.putImageData(imageData, 0, 0);

    // Update status display
    s.innerHTML = `Frame: ${frameCount}<br>Palette Size: ${PALETTE_SIZE}`;

    frameCount++;
    requestAnimationFrame(loop);
}

function loop() {
    drawFrame();
}

// Initialization and start
window.onload = () => {
    generateImageData(); // Initial setup
    requestAnimationFrame(loop);
};

</script></body></html>
```
qwen3-coder:30bERRBLANK
Show code
<!DOCTYPE html>
<html><head><style>body{margin:0;background:#111;display:flex;align-items:center;justify-content:center;height:100vh}canvas{border:1px solid #333;}</style></head><body id="b"><canvas id="c"></canvas><script>
const c=document.getElementById('c');
const g=c.getContext('2d');
let w=w_s=h=400;
c.width=w.height=h;

let time=0;
let frame=0;
const P=[0, 128, 255]; // Palette: Black, Mid-grey, White (3 levels)
const N=P.length;

function init() {
    time=0;
    frame=0;
    g.clearRect(0, 0, w, h);
}

function generateData(x, y, t, data) {
    let val = Math.sin((x/w+y/h)*Math.PI*2 + t/10) * 0.5 + 0.5;
    return val;
}

function quantizeAndDither(data, x, y) {
    let v=data[y*w+x];
    if (v < 0 || v > 1) return [v, 0];
    
    let q_idx = Math.floor(Math.min(N-1, Math.max(0, v * N)));
    let quantized_val = P[q_idx] / 255;
    let error = v - quantized_val;

    // Distribute error (Floyd-Steinberg weights)
    const E7=error*7/16;
    const E3=error*3/16;
    const E5=error*5/16;
    const E1=error*1/16;

    // Apply error to neighbors (must check bounds)
    if (x+1 < w) data[y*w+(x+1)] += E7;
    if (y+1 < h && x-1 >= 0) data[(y+1)*w+(x-1)] += E3;
    if (y+1 < h) data[(y+1)*w+x] += E5;
    if (y+1 < h && x+1 < w) data[(y+1)*w+(x+1)] += E1;

    return [quantized_val, 0];
}

function drawFrame() {
    g.clearRect(0, 0, w, h);
    let imageData = g.getImageData(0, 0, w, h);
    let data=imageData.data;
    
    // Use a floating point buffer to store continuous error values for the current frame iteration
    let floatBuffer=new Float32Array(w*h).fill(0);

    for (let y=0; y<h; y++) {
        for (let x=0; x<w; x++) {
            // Get initial value + accumulated error from previous pixels in this frame (simulating continuous source)
            let v_initial = generateData(x, y, time, floatBuffer); 
            let data_val = v_initial + floatBuffer[y*w+x];

            // Quantize and distribute error. The function modifies the buffer for subsequent pixels.
            let [q_val, _] = quantizeAndDither(floatBuffer, x, y);
            
            // Set pixel color using the quantized value
            let i=y*w+x;
            data[i*4+0]=Math.floor(q_val*255); // R
            data[i*4+1]=Math.floor(q_val*255); // G
            data[i*4+2]=Math.floor(q_val*255); // B
            data[i*4+3]=255;                    // A
        }
    }

    g.putImageData(imageData, 0, 0);
    
    document.getElementById('b').innerHTML=`<canvas id="c"></canvas><script>/*Code*/</script>`+`<div style='position:absolute;top:10px;left:10px;color:#ddd;'>Palette Size: ${N}, Frame: ${frame}</div>`;

    time+=1/60;
    frame++;
    requestAnimationFrame(drawFrame);
}

init();
</script></body></html>
llama3.1:8bERRBLANKFORMAT
Show code
```html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Floyd-Steinberg Dithering</title>
</head>
<body>
<script>
function f(x,y) {
    return Math.sin(Math.PI/2+Math.cos(y*Math.PI*3)*8-Math.abs(x));
}
var canvas = document.createElement('canvas');
document.body.appendChild(canvas);
var ctx = canvas.getContext('2d');
var paletteSize = 128;
var frame = 0;
var imageData = new ImageData(256,256);
for (var p=0;p<imageData.data.length;p+=4) {
    var r = f(p/4%512, Math.floor(p/768));
    imageData.data[p] = imageData.data[p+1] = imageData.data[p+2] = Math.floor(r*255);
}
function dither(imageData) {
    var data = imageData.data;
    for (var p=0;p<imageData.data.length;) {
        if (data[p] !== Math.floor(data[p])) {
            var r = data[p];
            data[p] = data[p+3] = Math.floor(r);
            data[p+1] = data[p+4] = Math.floor(Math.floor(5*r/16));
            data[p+2] = data[p+5] = Math.floor(Math.floor(3*r/16));
            if (p > 0) data[p-4] += data[p] - r;
            if (p > 767) data[p+768] += data[p] - r;
            if (p > 1535) data[p+1536] += data[p] - r;
            p += 16;
        } else {
            p+=4;
        }
    }
}
function draw() {
    ctx.putImageData(imageData,0,0);
    document.title = 'Floyd-Steinberg Dithering (' + frame + ')';
    document.body.querySelector('script').remove();
    var palette = [];
    for (var i=0;i<paletteSize;i++) {
        palette.push(Math.floor(255*Math.random()));
        if (palette.length === 256) break;
    }
    dither(imageData);
    var c = ctx.getImageData(128,128,1,1).data[0];
    canvas.width = canvas.height = Math.min(c*2+16,512);
    imageData = new ImageData(canvas.width,canvas.height);
    for (var p=0;p<imageData.data.length;p+=4) {
        var r = f(p/4%canvas.width*c, Math.floor(p/768));
        imageData.data[p] = imageData.data[p+1] = imageData.data[p+2] = Math.min(255,r*255);
    }
    requestAnimationFrame(draw);
}
requestAnimationFrame(draw);
</script>
</body>
</html>
```
deepseek-r1:32bOKFORMAT
Show code
```html
<!DOCTYPE html><html><body style="background:#000;margin:0;display:flex;justify-content:center;align-items:center;height:100vh"><canvas id=c></canvas><div id=f style="position:absolute;color:white"></div><script>
const c=document.getElementById('c'),ctx=c.getContext('2d');
let w=512,h=512,time=0,paletteSize=4;
c.width=w;c.height=h;

function floydSteinberg() {
  let img=new ImageData(w,h),data=img.data,i=0,t=time;
  for(let y=0;y<h;++y)for(let x=0;x<w;++x){
    let v=(Math.sin(time*0.02+x)+1)/2*(paletteSize-1)|0;
    v+=Math.sin(time*0.03+y)*0.5;v=Math.max(0,Math.min(paletteSize-1,v));
    data[i]=data[i+1]=data[i+2]=v*255/(paletteSize-1);data[i+3]=255;
    i+=4;
  }
  
  for(let y=0;y<h;++y)for(let x=0;x<w;++x){
    let idx=y*w+x,i=idx*4,d=data[i];
    let err=d-(d|0);
    
    data[i]=(d|0)*255/(paletteSize-1); // Set to nearest color
    
    if(x+1<w){data[(idx+1)*4]+=err*.3; if(y+1<h)data[(idx+1)*4+1]+=.7*err;}
    if(y+1<h){data[idx*w + (x+1)*w]*= err*.5;if(x-1>=0&&y+1<h)data[idx*w -1 + (x+1)*w]+=err*.1;}
  }
  
  ctx.putImageData(img,0,0);
}

document.body.appendChild(c);let frame=0;
function animate(){
floydSteinberg();
f.textContent=`Palette: ${paletteSize}, Frames: ${frame++}`;
time+=0.1;
requestAnimationFrame(animate);
}
animate();
</script></body></html>
```
command-r:35bOKFORMAT
Show code
```html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Floyd-Steinberg Dithering</title>
    <style>
        body {
            margin: 0;
            display: flex;
            justify-content: center;
            align-items: center;
            height: 100vh;
        }
        #animation-container {
            position: relative;
            width: 640px;
            height: 360px;
        }
    </style>
</head>
<body>
    <div id="animation-container">
        <canvas id="ditheringCanvas" width="640" height="360"></canvas>
        <p class="info">Palette Size: <span id="paletteSize">N/A</span></p>
        <p class="info">Frame: <span id="frameCount">N/A</span></p>
    </div>

    <script>
        const canvas = document.getElementById('ditheringCanvas');
        const ctx = canvas.getContext('2d');

        let frameCount = 0;
        let paletteSize = 8; // Initial palette size
        let imageData = null;

        function generateImageData(palette) {
            imageData = new ImageData(canvas.width, canvas.height);

            for (let i = 0; i < imageData.data.length; i += 4) {
                const index = Math.floor(Math.random() * paletteSize);
                imageData.data[i] = palette[index]; // Red
                imageData.data[i+1] = palette[index + 1]; // Green
                imageData.data[i+2] = palette[index + 2]; // Blue
                imageData.data[i+3] = 255;
            }

            return imageData;
        }

        function getPalette() {
            const palette = [];
            for (let i = 0; i < paletteSize * 4; i += 4) {
                const red = Math.floor(Math.random() * 256);
                const green = Math.floor(Math.random() * 256);
                const blue = Math.floor(Math.random() * 256);
                palette.push(red, green, blue);
            }
            return palette;
        }

        function quantizePixel(palette, pixel) {
            let minDistance = Infinity;
            let closestColorIndex = -1;

            for (let i = 0; i < paletteSize * 3; i += 3) {
                const distance = Math.sqrt(
                    Math.pow(pixel[0] - palette[i], 2) + 
                    Math.pow(pixel[1] - palette[i+1], 2) + 
                    Math.pow(pixel[2] - palette[i+2], 2)
                );

                if (distance < minDistance) {
                    minDistance = distance;
                    closestColorIndex = i / 3;
                }
            }

            return [
                palette[closestColorIndex * 3], 
                palette[closestColorIndex * 3 + 1], 
                palette[closestColorIndex * 3 + 2]
            ];
        }

        function ditherPixel(palette, pixel, x, y) {
            const quantized = quantizePixel(palette, pixel);

            for (let i = -1; i <= 1; i++) {
                for (let j = -1; j <= 1; j++) {
                    if (i === 0 && j === 0) continue;

                    // Floyd-Steinberg weights 
                    const errorMultiplier = [
                        [7 / 16, x + i, y + j], 
                        [3 / 16, x + i - 1, y + j], 
                        [5 / 16, x + i + 1, y + j], 
                        [1 / 16, x + i, y + j - 1]
                    ];

                    const targetIndex = (x + i) * 4 + (y + j) * canvas.width * 4;

                    if (imageData.data[targetIndex + 3] === 255) {
                        imageData.data[targetIndex] += errorMultiplier[0][0] * (pixel[0] - quantized[0]);
                        imageData.data[targetIndex+1] += errorMultiplier[0][0] * (pixel[1] - quantized[1]);
                        imageData.data[targetIndex+2] += errorMultiplier[0][0] * (pixel[2] - quantized[2]);
                    }
                }
            }

            imageData.data[(x + y * canvas.width) * 4] = quantized[0]; 
            imageData.data[(x + y * canvas.width) * 4 + 1] = quantized[1]; 
            imageData.data[(x + y * canvas.width) * 4 + 2] = quantized[2]; 
        }

        function renderFrame() {
            frameCount++;

            const palette = getPalette();
            document.getElementById('paletteSize').textContent = `Palette Size: ${paletteSize}`;
            document.getElementById('frameCount').textContent = `Frame: ${frameCount}`;

            generateImageData(palette);

            for (let y = 0; y < canvas.height; y++) {
                const rowOffset = y * canvas.width * 4;

                for (let x = 0; x < canvas.width; x++) {
                    ditherPixel(palette, [imageData.data[rowOffset + x * 3], imageData.data[rowOffset + x * 3 + 1], imageData.data[rowOffset + x * 3 + 2]], x, y);
                }
            }

            ctx.putImageData(imageData, 0, 0);
        }

        function animate() {
            renderFrame();
            requestAnimationFrame(animate);
        }

        animate();
    </script>
</body>
</html>
```
qwq:32bSTAT
Show code
Here's a JavaScript implementation of Floyd-Steinberg dithering applied to an image generated with a sinusoidal pattern. The code creates a canvas and displays the dithered image.

```javascript
function ditherImage(width, height, frame) {
    const step = 16;
    const pixelCount = width * height;
    const floatData = new Float64Array(4 * pixelCount);
    const data = new Uint8ClampedArray(4 * pixelCount);

    // Initialize with a sinusoidal function
    for (let y = 0; y < height; y++) {
        for (let x = 0; x < width; x++) {
            const index = (y * width + x) * 4;
            const val = Math.sin(x * 0.1 + y * 0.1 + frame * 0.5) * 127 + 128;
            floatData[index] = val; // Red
            floatData[index + 1] = val; // Green
            floatData[index + 2] = val; // Blue
            floatData[index + 3] = 255; // Alpha
        }
    }

    // Apply Floyd-Steinberg dithering
    for (let y = 0; y < height; y++) {
        for (let x = 0; x < width; x++) {
            const index = (y * width + x) * 4;
            let currentVal = floatData[index];
            const quantizedVal = Math.round(currentVal / step) * step;
            const error = currentVal - quantizedVal;

            floatData[index] = quantizedVal;

            // Distribute error to neighboring pixels
            // Right pixel
            if (x + 1 < width) {
                floatData[(y * width + (x + 1)) * 4] += error * 7 / 16;
            }
            // Southwest
            if (y + 1 < height && x > 0) {
                floatData[((y + 1) * width + (x - 1)) * 4] += error * 3 / 16;
            }
            // South pixel
            if (y + 1 < height) {
                floatData[((y + 1) * width + x) * 4] += error * 5 / 16;
            }
            // Southeast pixel
            if (y + 1 < height && x + 1 < width) {
                floatData[((y + 1) * width + (x + 1)) * 4] += error * 1 / 16;
            }
        }
    }

    // Convert to Uint8ClampedArray for display
    for (let i = 0; i < floatData.length; i++) {
        data[i] = Math.round(floatData[i]);
    }

    // Create canvas to display the image
    const canvas = document.createElement('canvas');
    canvas.width = width;
    canvas.height = height;
    const ctx = canvas.getContext('2d');
    ctx.putImageData(new ImageData(data, width, height), 0, 0);
    return canvas;
}

// Example usage: Create a 100x100 image at frame 0
const canvas = ditherImage(100, 100, 0);
document.body.appendChild(canvas);
```

### Explanation:
1. **Initialization**: Uses a sinusoidal function to generate pixel values between 0 and 255.
2. **Quantization**: Reduces color depth by dividing values into 16 levels (step size of 16).
3. **Error Distribution**: Applies the Floyd-Steinberg algorithm to distribute quantization errors to neighboring pixels, improving visual quality.
4. **Precision Handling**: Uses `Float64Array` to maintain floating-point precision during calculations before converting to integers for display.

To see the effect of animation, you could modify the `frame` parameter over time using `requestAnimationFrame` or `setTimeout`.
Outside the Brain cluster — same task, same character budget, collected through their own tooling (response times not comparable)
GPT-5.5 (Codex)OKMINIFIED
Show code
<!doctype html><html><head><meta charset=utf-8><title>Dither</title><style>html,body{margin:0;height:100%;background:#111;color:#eee;font:16px monospace;overflow:hidden}canvas{width:100vw;height:100vh;image-rendering:pixelated;display:block}#i{position:fixed;left:12px;top:10px;background:#0008;padding:6px 8px}</style></head><body><canvas id=c></canvas><div id=i></div><script>
let C=c.getContext("2d"),W=220,H=150,P,F=0,S=0,D,T=420;
function R(){P=2+(Math.random()*5|0);F=0;S=Math.random()*9}
function Z(){let r=devicePixelRatio||1;c.width=innerWidth*r;c.height=innerHeight*r;C.setTransform(r,0,0,r,0,0)}
onresize=Z;Z();R();
function A(t){let im=C.createImageData(W,H),a=new Float32Array(W*H),e=new Float32Array((W+2)*(H+1)),k=0,x,y,v,q,er,idx;for(y=0;y<H;y++)for(x=0;x<W;x++){v=128+70*Math.sin(x*.045+S+t*.0017)+55*Math.sin((x+y)*.035+t*.0011)+42*Math.cos((Math.hypot(x-W/2,y-H/2))* .075-t*.0013);a[k++]=v}for(y=0;y<H;y++)for(x=0;x<W;x++){idx=y*(W+2)+x+1;k=y*W+x;v=Math.max(0,Math.min(255,a[k]+e[idx]));q=Math.round(v*(P-1)/255)*255/(P-1);er=v-q;e[idx+1]+=er*7/16;e[idx+W+1]+=er*3/16;e[idx+W+2]+=er*5/16;e[idx+W+3]+=er/16;k*=4;im.data[k]=im.data[k+1]=im.data[k+2]=q;im.data[k+3]=255}let o=new OffscreenCanvas(W,H),g=o.getContext("2d");g.putImageData(im,0,0);C.imageSmoothingEnabled=false;C.drawImage(o,0,0,innerWidth,innerHeight);i.textContent="palette "+P+"  frame "+F++;if(F>T)R();requestAnimationFrame(A)}
requestAnimationFrame(A)
</script></body></html>
Show code
<!DOCTYPE html>
<html><head><meta charset="utf-8"><title>Floyd-Steinberg</title>
<style>
html,body{margin:0;height:100%;background:#0b0b0e;overflow:hidden}
#c{width:100vw;height:calc(100vh - 28px);image-rendering:pixelated;image-rendering:crisp-edges;display:block;background:#000}
#i{position:fixed;left:0;right:0;bottom:0;height:28px;line-height:28px;padding:0 10px;
box-sizing:border-box;font:13px monospace;color:#9ef;background:#000;border-top:1px solid #234}
</style></head>
<body>
<canvas id="c"></canvas><div id="i"></div>
<script>
var W=160,H=100;
var cv=document.getElementById('c');cv.width=W;cv.height=H;
var ctx=cv.getContext('2d');
var info=document.getElementById('i');
var img=ctx.createImageData(W,H);
var err=new Float32Array(W*H);
var frame=0,inst;

function newInstance(){
  inst={
    a1:0.02+Math.random()*0.08, b1:0.02+Math.random()*0.08,
    a2:0.01+Math.random()*0.05, b2:0.01+Math.random()*0.05,
    ft:0.015+Math.random()*0.045, ft2:0.01+Math.random()*0.04,
    p1:Math.random()*6.283, p2:Math.random()*6.283, p3:Math.random()*6.283,
    pal:2+Math.floor(Math.random()*7),
    life:280+Math.floor(Math.random()*260),
    t0:frame
  };
  inst.pv=[];
  for(var i=0;i<inst.pal;i++){
    inst.pv.push(inst.pal===1?128:Math.round(i*255/(inst.pal-1)));
  }
}
newInstance();

function grey(x,y,t,k){
  var s=Math.sin(x*k.a1+t*k.ft+k.p1)*0.5+
        Math.cos(y*k.b1-t*k.ft*0.7+k.p2)*0.5+
        Math.sin(x*k.a2+y*k.b2+t*k.ft2+k.p3)*0.5;
  var v=(s/1.5+1)/2;
  if(v<0)v=0; if(v>1)v=1;
  return v*255;
}

function step(){
  requestAnimationFrame(step);
  frame++;
  if(frame-inst.t0>inst.life){newInstance();}
  var t=frame,k=inst,pv=inst.pv,data=img.data;

  for(var y=0;y<H;y++){
    for(var x=0;x<W;x++){
      err[y*W+x]=grey(x,y,t,k);
    }
  }

  for(var y=0;y<H;y++){
    for(var x=0;x<W;x++){
      var idx=y*W+x;
      var old=err[idx];
      if(old<0)old=0; if(old>255)old=255;
      var best=pv[0],bd=Math.abs(old-pv[0]);
      for(var pIdx=1;pIdx<pv.length;pIdx++){
        var d=Math.abs(old-pv[pIdx]);
        if(d<bd){bd=d;best=pv[pIdx];}
      }
      var e=old-best;
      var di=idx*4;
      data[di]=data[di+1]=data[di+2]=best;
      data[di+3]=255;
      if(x+1<W) err[idx+1]+=e*7/16;
      if(y+1<H){
        if(x>0) err[idx+W-1]+=e*3/16;
        err[idx+W]+=e*5/16;
        if(x+1<W) err[idx+W+1]+=e*1/16;
      }
    }
  }

  ctx.putImageData(img,0,0);
  info.textContent='Palette: '+k.pal+'   Frame: '+frame+'   Instance age: '+(frame-k.t0)+'/'+k.life;
}
requestAnimationFrame(step);
</script>
</body></html>
Results at a glance— every model in this round with score, response time and status.

Who wrote what

ModelBlind scoreLatencyStatus
qwen3:14b 13.3 sOK
mistral-small:24b 108.5 sOK
qwen3:8b harness
deepseek-r1:14b 52.2 sOK
gemma4:26b harness
qwen3.5:9b 24.6 sOK
qwen3-coder:30b 22.3 sOK
llama3.1:8b 81.5 sOK
deepseek-r1:32b 111.5 sOK
command-r:35b 37.2 sOK
qwq:32b 273.7 sOK