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<!DOCTYPE html>
<html>
<head>
<title>LLMPerf</title>
<script src="https://d3js.org/d3.v7.min.js"></script>
<link href="https://fonts.googleapis.com/css2?family=Open+Sans:wght@300;400;600&display=swap" rel="stylesheet">
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background-color: #1d1d1f;
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font-weight: 300;
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color: #0070c9;
border-bottom: 2px solid #0070c9;
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</head>
<body>
<div class="header">
<h1>LLMPerf</h1>
</div>
<div class="notification-bar">
Updated March 31: Added Command R and Qwen, added mobile UI and FAQ
</div>
<div class="tab">
<button class="tablinks" onclick="openTab(event, 'graph')">Graph</button>
<button class="tablinks" onclick="openTab(event, 'table')">Table</button>
<button class="tablinks" onclick="openTab(event, 'faq')">FAQ</button>
</div>
<div id="graph" class="tabcontent">
<div class="dropdownholder">
<select id="xMetric" class="dropdown">
<option value="output_tokens_price_per_million">Output Tokens Price
per Million (USD)</option>
<option value="input_tokens_price_per_million">Input Tokens Price per
Million (USD)</option>
</select>
<div class="vs">vs.</div>
<select id="yMetric" class="dropdown">
<option value="score">Chatbot Arena ELO</option>
<option value="mmlu_score">MMLU Score</option>
<option value="mt_bench">MT-Bench</option>
<option value="context_length">Context</option>
<option value="gpqa">GPQA</option>
<option value="human_eval">HumanEval (Coding)</option>
</select>
</div>
<div id="detailPanel" class="detail-panel">
<span class="close-btn" onclick="closeDetailPanel()">×</span>
<h2 id="selectedLLM"></h2>
<p id="llmDescription"></p>
<p>Company: <a id="companyLink" target="_blank"></a></p>
<table id="llmStats">
<tbody>
</tbody>
</table>
</div>
<div id="graphCore"></div>
<div class="legend"></div>
</div>
<div id="table" class="tabcontent">
<table id="dataTable">
<thead>
<tr>
<th>Model/Service</th>
<th>Company</th>
<th>Input Tokens Price per Million (USD)</th>
<th>Output Tokens Price per Million (USD)</th>
<th>Context Length</th>
<th>Chatbot Arena ELO</th>
<th>MMLU Score</th>
<th>MT-Bench</th>
<th>GPQA</th>
<th>HumanEval (Coding)</th>
<th>Release Date</th>
<th>Knowledge Cutoff Date</th>
</tr>
</thead>
<tbody>
</tbody>
</table>
<div class="legend"></div>
</div>
<div id="faq" class="tabcontent">
<h2>FAQ</h2>
<dl>
<dt>What is an LLM?</dt>
<dd>LLM stands for Large Language Model, which is a type of artificial
intelligence that understands and generates human-like text based on
the input it receives. LLMs are trained on vast datasets of human
language and can perform a wide range of language-related tasks.</dd>
<dt>Why do you display obsolete models?</dt>
<dd>We display obsolete models to provide a comprehensive historical
context and enable performance comparisons over time. This helps users
understand the evolution of LLM technology and its capabilities.</dd>
<dt>Why don't you display very old models?</dt>
<dd>Very old models are omitted to maintain the relevance and clarity of
our benchmarks. Including them might clutter the data and make it
harder for users to find useful information about current and recently
obsolete models.</dd>
<dt>How do you gather pricing information for open models?</dt>
<dd>For the open models, you can download and run the weights yourself
for free -- but you probably will not due to the very expensive GPUs
required. Instead, this site shows prices from together.ai, a low-cost
service that hosts open models for you.</dd>
<dt>What is context length?</dt>
<dd>Context length refers to the maximum number of tokens an LLM can
process in a single prompt. A token can be a word, part of a word, or
even punctuation, depending on the model's training data and
tokenization method. This parameter is crucial as it affects the
model's ability to understand and generate coherent and contextually
relevant responses.</dd>
<dt>What are the metrics shown?</dt>
<dd>The metrics displayed on our site include Chatbot Arena ELO, MMLU
(Massive Multitask Language Understanding), MT-Bench (Multi-turn Benchmark),
GPQA (Google-proof Question and Answer), and
HumanEval. These metrics are used to evaluate the performance of LLMs
across different tasks and capabilities.</dd>
<dt>How often is the data updated?</dt>
<dd>The data is updated approximately once a week. This frequency
ensures that our benchmarks remain up-to-date and reflect the latest
developments and improvements in LLM technology.</dd>
<dt>Why is Grok not included?</dt>
<dd>We don't yet have any benchmark data for Grok.</dd>
<dt>Which metric is best for my application?</dt>
<dd>The best metric for your application depends on the specific tasks
you intend to use the LLM for.
<ul>
<li><strong>Chatbot Arena ELO:</strong> This is a human-rated score
where the human asks two randomly-chosen LLMs the same question and chooses the superior
answer. The ChatBot
Arena is <a
href="https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard">here.</a></li>
<li><strong>MMLU:</strong> MMLU is a very large database of
school-test-like multiple choice questions, similar to what an
undergraduate student might learn. Try it <a
href="https://d.erenrich.net/are-you-smarter-than-an-llm/index.html">here</a>.</li>
<li><strong>MT-Bench:</strong> MT-Bench is a benchmark for
"multi-turn" chats (conversations involving back-and-forth). A
description is <a
href="https://klu.ai/glossary/mt-bench-eval">here</a>.</li>
<li><strong>GPQA:</strong> GPQA is an extraordinarily challenging
multiple-choice question database similar to what an advanced
graduate student or scientific expert might learn. Learn more
about it <a href="https://github.com/idavidrein/gpqa">
here</a>.</li>
<li><strong>HumanEval:</strong> HumanEval is a code generation
benchmark that pairs questions in 23 natural languages with 12
different programming languages. The benchmark asks a question in
a natural language; gets a small piece of code; and runs it to
verify functionality.</li>
</ul>
<p>For many applications, Chatbot Arena ELO is the best metric to use, since it cannot
be trained for in advance and is evaluated by humans and an independent organization.
One downside of Chatbot Arena is that humans typically favor longer responses, even if a shorter
response is equally correct. The highest-scoring models on Chatbot Arena do tend to produce long responses. </p>
<p>MMLU, MT-Bench, HumanEval, and GPQA are automatically evaluated, so they are not biased by long answers.
The risk with these four benchmarks is that correct answers might leak into training data.
This is particularly a problem for MMLU, since the questions and answers are widely available.
MT-Bench and HumanEval mitigate this by using automatically-generated questions, and GPQA mitigates this
by not publicly releasing the correct answers. However, at least in theory, a model could be excessively
fine-tuned for these benchmarks. </p>
</dd>
</dl>
</div>
<div id="footer">
<h3>
<a href="https://github.com/dfeldman/llmperf" target="_blank">
<img
src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png"
alt="GitHub Link" style="width: 32px; height: 32px;"> Edit on GitHub
</a>
</h3>
</div>
<script>
const data = [
{
name: "Claude 3 Haiku",
input_tokens_price_per_million: 0.25,
output_tokens_price_per_million: 1.25,
context_length: 200,
score: 1179,
mmlu_score: 75.2,
mt_bench: null,
gpqa: 33.3,
human_eval: 75,
company: "Anthropic",
description: "Claude 3 Haiku is a powerful language model developed by Anthropic, offering high-quality text generation with a context length of 200K tokens.",
companyWebsite: "https://www.anthropic.com",
release: "2024-03-14",
cutoff: "2023-08-01"
},
{
name: "Claude 3 Sonnet",
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output_tokens_price_per_million: 15,
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mmlu_score: 79.0,
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human_eval: 73,
company: "Anthropic",
description: "Claude 3 Sonnet is another impressive language model by Anthropic, providing excellent performance for various natural language tasks with a context length of 200K tokens.",
companyWebsite: "https://www.anthropic.com",
release: "2024-03-14",
cutoff: "2023-08-01"
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{
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mt_bench: null,
gpqa: 50.4,
human_eval: 84.9,
company: "Anthropic",
description: "Claude 3 Opus is the most advanced language model in the Claude 3 series by Anthropic, delivering top-notch performance with a context length of 200K tokens.",
companyWebsite: "https://www.anthropic.com",
release: "2024-03-14",
cutoff: "2023-08-01"
},
{
name: "GPT-4 Turbo",
input_tokens_price_per_million: 10,
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context_length: 128,
score: 1251,
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mt_bench: 9.32,
gpqa: 35.7,
human_eval: 74.5,
company: "OpenAI",
description: "GPT-4 Turbo is a high-performance language model by OpenAI, offering a context length of 128K tokens for efficient and accurate text generation.",
companyWebsite: "https://www.openai.com",
cutoff: "2023-04-01",
release: "202-06-13"
},
{
name: "GPT-4",
input_tokens_price_per_million: 30,
output_tokens_price_per_million: 60,
context_length: 32,
score: 1159,
mmlu_score: 86.4,
mt_bench: 8.96,
gpqa: 35.7,
human_eval: 74.5,
company: "OpenAI",
description: "GPT-4 is the original, now-deprecated version of GPT-4. New users should switch to GPT-4 Turbo.",
companyWebsite: "https://www.openai.com",
release: "2023-03-14",
cutoff: "2021-09-01",
},
{
name: "GPT-3.5 Turbo 0613",
input_tokens_price_per_million: 0.50,
output_tokens_price_per_million: 1.50,
context_length: 16,
score: 1114,
mmlu_score: 70,
mt_bench: 8.39,
gpqa: 28.1,
human_eval: null,
company: "OpenAI",
description: "GPT-3.5 Turbo 0613 is a powerful language model by OpenAI, offering a context length of 16K tokens for efficient and high-quality text generation.",
companyWebsite: "https://www.openai.com",
release: "2023-06-13",
cutoff: "2021-09-01"
},
{
name: "Mistral Medium",
input_tokens_price_per_million: 2.7,
output_tokens_price_per_million: 8.1,
context_length: 8,
score: 1145,
mmlu_score: null,
mt_bench: null,
gpqa: null,
human_eval: null,
company: "Mistral",
description: "Mistral Medium is a language model developed by Mistral, offering a context length of 8K tokens for various natural language tasks.",
companyWebsite: "http://mistral.ai",
release: "2024-02-25",
},
{
name: "Mistral Large",
input_tokens_price_per_million: 8,
output_tokens_price_per_million: 24,
context_length: 32,
score: 1157,
mmlu_score: null,
mt_bench: null,
gpqa: null,
human_eval: null,
company: "Mistral",
description: "Mistral Large is a more advanced language model by Mistral, delivering improved performance with a context length of 32K tokens.",
companyWebsite: "http://mistral.ai",
release: "2024-02-25",
cutoff: "2022-12-01"
},
{
name: "Gemini Dev API",
input_tokens_price_per_million: 0.50,
output_tokens_price_per_million: 1.50,
context_length: 128,
score: 1125,
mmlu_score: 83.7,
mt_bench: null,
gpqa: null,
human_eval: 74.4,
company: "Google",
description: "Gemini Dev API is a language model offered by Google, providing a context length of 128K tokens for various natural language processing tasks.",
companyWebsite: "https://www.google.com",
release: "2024-02-15",
cutoff: "2023-04-01",
},
{
name: "Gemini Pro (Bard)",
input_tokens_price_per_million: null,
output_tokens_price_per_million: null,
context_length: 128,
score: 1203,
mmlu_score: null,
mt_bench: null,
gpqa: null,
human_eval: null,
company: "Google",
description: "Gemini Advanced is a high-performance language model by Google, offering a context length of 128K tokens for advanced natural language applications.",
companyWebsite: "https://www.google.com",
release: "Unknown",
cutoff: "Online"
},
{
name: "Qwen1.5-72B-Chat",
input_tokens_price_per_million: 0.90,
output_tokens_price_per_million: 0.90,
context_length: 32,
score: 1149,
mmlu_score: 77.5,
human_eval: 42.5,
mt_bench: 8.61,
company: "Alibaba (Open)",
description: "Qwen1.5-72B-Chat is a high-performance open language model by Alibaba.",
companyWebsite: "https://qwenlm.github.io/blog/qwen1.5/",
cutoff: "2024-02-01",
release: "2024-02-04"
},
{
name: "Command R",
input_tokens_price_per_million: 0.90,
output_tokens_price_per_million: 0.90,
score: 1146,
context_length: 32,
company: "Cohere (Open)",
description: "Command R is an open model intended specifically for retrieval-augemented generation (RAG) tasks.",
companyWebsite: "https://cohere.ai",
cutoff: "2024-03-11"
}
];
function formatName(internalName) {
switch (internalName) {
case "output_tokens_price_per_million":
return "Output Tokens Price per Million (USD)";
case "input_tokens_price_per_million":
return "Input Tokens Price per Million (USD)";
case "score":
return "Chatbot Arena ELO";
case "mmlu_score":
return "MMLU Score";
case "mt_bench":
return "MT-Bench";
case "context_length":
return "Context Length";
case "gpqa":
return "GPQA";
case "human_eval":
return "HumanEval (Coding)";
case "release":
return "Release Date"
case "cutoff":
return "Knowledge Cutoff Date"
default:
return internalName;
}
}
const graphContainer = d3.select("#graphCore");
const margin = { top: 40, right: 20, bottom: 60, left: 60 };
const width = 800 - margin.left - margin.right;
const height = 600 - margin.top - margin.bottom;
const svg = graphContainer.append("svg")
.attr("width", "100%")
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform", `translate(${margin.left},${margin.top})`);
const xScale = d3.scaleLinear().range([0, width]);
const yScale = d3.scaleLinear().range([height, 0]);
const colorScale = d3.scaleOrdinal(d3.schemePastel1);
const xAxis = d3.axisBottom(xScale);
const yAxis = d3.axisLeft(yScale);
let labelData = [];
let lastYMetric = "";
let lastXMetric = "";
let prevPositions = {};
const tooltip = d3.select("body").append("div")
.attr("class", "tooltip")
.style("opacity", 0);
function addToolTip(xMetric, yMetric) {
return function (event, d) {
if (d.data) {
d = d.data;
}
tooltip.transition()
.duration(200)
.style("opacity", .9);
tooltip.html(`
<strong>${d.name}</strong><br/>
${formatName(xMetric)}: ${d[xMetric]}<br/>
${formatName(yMetric)}: ${d[yMetric]}<br/>
Company: ${d.company}
`)
.style("left", (event.pageX + 10) + "px")
.style("top", (event.pageY - 28) + "px");
}
}
function closeAllToolTips() {
d3.select(".tooltip").style("opacity", 0);
}
function getTextWidth(text) {
const canvas = document.createElement("canvas");
const context = canvas.getContext("2d");
context.font = "12px sans-serif"; // Specify the font style and size
const width = context.measureText(text).width;
return width;
}
function isTopModel(d, xMetric, yMetric) {
const selectedModel = d;
const otherModels = data.filter(model => model !== selectedModel);
return otherModels.every(model => {
return (
(model[xMetric] === null || model[xMetric] > selectedModel[xMetric]) ||
(model[xMetric] <= selectedModel[xMetric] && model[yMetric] < selectedModel[yMetric])
);
});
}
function update(xMetric, yMetric) {
newWidth = graphContainer.node().getBoundingClientRect().width - margin.left - margin.right;
newHeight = graphContainer.node().getBoundingClientRect().height - margin.top - margin.bottom;
const filteredData = data.filter(d => d[xMetric] !== null && d[yMetric] !== null && d[xMetric] !== undefined && d[yMetric] !== undefined);
svg.selectAll(".axis-layer").remove();
svg.selectAll(".dot-layer").remove();
svg.selectAll(".label-layer").remove();
svg.selectAll(".grid-layer").remove();
const axisLayer = svg.append("g").attr("class", "axis-layer");
const dotLayer = svg.append("g").attr("class", "dot-layer");
const labelLayer = svg.append("g").attr("class", "label-layer");
const gridLayer = svg.append("g").attr("class", "grid-layer");
xScaleOrig = d3.extent(filteredData, d => d[xMetric]);
xScaleOrig[1] = xScaleOrig[1] * 1.2;
xScale.domain(xScaleOrig).nice();
yScale.domain(d3.extent(filteredData, d => d[yMetric])).nice();
// Add grid lines
gridLayer.append("g")
.attr("class", "grid")
.attr("transform", `translate(0,${height})`)
.call(d3.axisBottom(xScale)
.tickSize(-height)
.tickFormat("")
);
gridLayer.append("g")
.attr("class", "grid")
.call(d3.axisLeft(yScale)
.tickSize(-width)
.tickFormat("")
);
axisLayer.selectAll(".x-axis").remove();
axisLayer.append("g")
.attr("class", "x-axis")
.attr("transform", `translate(0,${height})`)
.call(xAxis)
.append("text")
.attr("class", "axis-label")
.attr("x", (width - margin.left - margin.right) / 2)
.attr("y", margin.bottom - 10)
.attr("fill", "black")
.style("text-anchor", "middle")
.text(formatName(xMetric));
axisLayer.selectAll(".y-axis").remove();
axisLayer.append("g")
.attr("class", "y-axis")
.call(yAxis)
.append("text")
.attr("class", "axis-label")
.attr("transform", "rotate(-90)")
.attr("x", -(height - margin.top - margin.bottom) / 2)
.attr("y", -margin.left + 20)
.attr("fill", "black")
.style("text-anchor", "middle")
.style("z-index", "-1000")
.text(formatName(yMetric));
dotLayer.selectAll(".dot").remove();
dotLayer.selectAll().remove();
dotLayer.selectAll(".dot")
.data(filteredData)
.join("circle")
.attr("class", "dot")
.attr("cx", d => xScale(d[xMetric]))
.attr("cy", height)
.attr("r", 6)
.style("fill", d => isTopModel(d, xMetric, yMetric) ? colorScale(d.company) : "#ddd")
.on("mouseover", addToolTip(xMetric, yMetric))
.on("mouseout", function (d) {
tooltip.transition()
.duration(1200)
.style("opacity", 0);
})
.on("click", function (event, d) {
showDetailPanel(d);
})
.transition()
.duration(1000)
.attr("cy", d => yScale(d[yMetric]));
const padding = 5;
const rectHeight = 14;
if (newWidth < 500) {
return;
}
prevPositions = labelData.map(label => ({ x: label.x, y: label.y }));
labelData = filteredData.map(d => {
const x = xScale(d[xMetric]);
const y = yScale(d[yMetric]) - 22;
const textWidth = getTextWidth(d.name);
return {
x: x,
y: y,
textWidth: textWidth,
data: d,
width: textWidth + padding,
height: rectHeight,
};
});
adjustLabels(labelData);
const labelRects = labelLayer.selectAll(".dot-label-rect.data(labelData)").join("rect")
.attr("class", "dot-label-rect")
.attr("x", d => d.x - (d.width / 2))
.attr("y", d => d.y - (d.height / 2) - (padding))
.attr("width", d => d.width)
.attr("height", d => d.height + padding)
.attr("rx", 5)
.style("fill", "#ccc");
const labelTexts = labelLayer.selectAll(".dot-label-text")
.data(labelData)
.join("text")
.attr("class", "dot-label-text")
.attr("x", d => d.x)
.attr("y", height)
.text(d => d.data.name)
.style("text-anchor", "middle")
.style("font-size", "12px")
.style("fill", "#333")
.on("mouseover", addToolTip(xMetric, yMetric))
.on("mouseout", function (d) {
tooltip.transition()
.duration(500)
.style("opacity", 0);
})
.on("click", function (event, d) {
showDetailPanel(d.data);
})
.transition()
.duration(1000)
.attr("y", d => d.y);
const labelsVisible = d3.select("#labelCheckbox").property("checked");
labelRects.style("display", labelsVisible ? "block" : "none");
labelTexts.style("display", labelsVisible ? "block" : "none");
}
function adjustLabels(labelData) {
const maxIterations = 500;
let iteration = 0;
let overlaps = true;
while (overlaps && iteration < maxIterations) {
overlaps = false;
for (let i = 0; i < labelData.length - 1; i++) {
const label1 = labelData[i];
for (let j = i + 1; j < labelData.length; j++) {
const label2 = labelData[j];
if (isOverlapping(label1, label2)) {
overlaps = true;
const dx = label2.x - label1.x;
const dy = label2.y - label1.y;
const distance = Math.sqrt(dx * dx + dy * dy);
const overlap = nanToSmall((label1.width + label2.width) / 2 - distance);
const adjustmentX = nanToSmall(dx / distance) * overlap * (0.2);
const adjustmentY = nanToSmall(dy / distance) * overlap * (0.2);
label1.x -= adjustmentX;
label1.y -= adjustmentY;
label2.x += adjustmentX;
label2.y += adjustmentY;
}
}
//console.log("first pass end",labelData[i].data.name, labelData[i].x, labelData[i].y)
}
iteration++;
}
// Update label positions based on previous positions
labelData.forEach((label, index) => {
if (prevPositions[index]) {
const distanceThreshold = 10; // Adjust this value as needed
const prevX = prevPositions[index].x;
const prevY = prevPositions[index].y;
// Calculate the distance between the current and previous positions
const distance = Math.sqrt((label.x - prevX) ** 2 + (label.y - prevY) ** 2);
// If the distance is within the threshold, use the previous position
if (distance < distanceThreshold) {
//console.log("Switch back to previous position", label.x, label.y, prevX, prevY)
label.x = prevX;
label.y = prevY;
}
} else {
//console.log("No previous position found")
}
});
}